How to Change WordPress Login Page URL + How It Keeps You Safe

You’re no doubt aware how important security is to your WordPress site. In fact, you’ve probably heard plenty of advice on the subject – including that you should change your WordPress login page URL. However, you may not be sure why or how to do that.Changing your login page URL is a simple but effective security technique that can help keep hackers out. After all, a unique, difficult-to-guess URL is harder to locate. This means people are less likely to gain access to your site unless you want them to.In this post, we’ll briefly discuss why changing your WordPress login page URL is a smart idea. Then we’ll show you how to do so using a free plugin. Let’s take a look! you customize your login page, it looks something like thisBy default, WordPress sites all use identical URL structures for this page. If your website’s domain is www.mysite.com, for example, you can log in by visiting www.mysite.com/wp-login.php or www.mysite.com/wp-adm in.This makes it easy to remember how to access your site. However, the downside is that anyone who knows the first thing about WordPress can find your login page quickly. Once theyve located it, hackers can get busy trying to break in. If you change the URL to something hard to guess, on the other hand, you’ll slow those same hackers down by making your login page harder to find.Additionally, changing your login page URL has a secondary benefit in that it can eliminate a lot of resource-wasting bot traffic to your site.Why you shouldnt change your login page URL manuallyBelow, well walk you through the process of changing your login page URL using a plugin. However, in some cases you may be tempted to complete this task manually (for instance, if you want to limit the number of plugins you install on your site).While you can use File Transfer Protocol (FTP) or another method of accessing your sites files directly to  sort of make this change, this is  not a good idea   for a couple main reasons:Every time you update WordPress, it will recreate the login page file. This means youll need to change the URL all over again.Manually changing your login page URL can create errors with your logout screen, and cause other issues with important site functionality.In general, we recommend not altering your sites core files if you dont have to. Doing this can have unintended consequences. Fortunately, theres a better way of hiding your login page.How to change your WordPress login page URL using a pluginWe should emphasize that this technique won’t prevent hacking completely. However, it does provide an extra layer of security for your site. Changing your login page URL is best used in combination with other methods of protecting your admin area, such as implementing Two-Factor Authentication (2FA) and limiting the number of login attempts allowed.To change your WordPress login page URL, we recommend WPS Hide Login: WPS Hide Login Author(s): WPServe ur, NicolasKulka, tabrisrpCurrent Version: 1.5.4.2Last Updated: August 22, 2019wps-hide-login.1.5.4.2.zip 98%Ratings 3,615,198Downloads WP 4.1+Requires This is a lightweight solution that gets the job done simply and quickly. What’s more, its popular, has excellent reviews, and receives regular updates from the developer.You’ll want to start out by backing up your site, just to be safe.  Then, you’ll need to install and activate the plugin.After those tasks are done, navigate to Settings General in your WordPress dashboard.If you scroll to the very bottom of the page, you’ll find a new section labeled WPS Hide Login:This option will enable you to create a new URL for your login page by typing it into the field after your website’s domain name. Your best bet is to choose something random, as you would for a password (for example, a string of numbers and letters). Just make sure you record the new URL somewhere secure, so you don’t los e access to your site.When you’re happy with the new URL, click on the Save Changes button.From now on, you’ll be able to use this address to log into your site, and the default URL will be disabled. If for some reason you ever want to reverse this process, just deactivate WPS Hide Login, and the URL will return to normal.ConclusionWordPress is a very secure platform, but there are always steps you can take to further protect your website. Changing your login page URL is a small tweak that – when implemented as part of a comprehensive security plan – makes it more difficult for hackers and spammers to gain access.Furthermore, using this technique is surprisingly simple. In fact, it will only take you a few minutes if you use the right tool. By installing a  plugin like  WPS Hide Login, you can alter your login pages URL through your dashboard settings, and see the change take effect immediately.Do you have any questions about how to change your WordPr ess login page URL? Tell us in the comments section below! How to change your #WordPress login page URL. Plus how it helps keep your site secure! #tutorial

Social Sciense

Social Sciense 3‚ º AÂÆ'‘OECONOMÂÆ' A Y CONTABILIDAD2014GUÂÆ' ATEÂÆ'“RICO-PRÂÆ' CTICAAUTOR:Prof. Claudio H. OlivetoEconomÂÆ' ­a y ContabilidadÂÆ' MBITO ECONÂÆ'“MICOEL HOMBREen su relaciÂÆ' ³n con elECONOMÂÆ' A POLÂÆ' TICA CULTURA TECNOLOGÂÆ' AVive en utiliza necesitaContextoSOCIEDADENCUESTAS ENTREVISTAS ESTADÂÆ' STICAS POLÂÆ' TICAS ECONÂÆ'“MICASLA ORGANIZACIÂÆ'“NPor lo cual se creanrequiereNutreSOCIEDADES COMERCIALESSISTEMAS DE INFORMACIÂÆ'“NAMBIENTE EXTERNOprovieneAMBIENTE INTERNOQue utilizanREGISTRACIÂÆ'“N CONTABLEDOCUMENTOS COMERCIALESque respaldan laUNIDAD 1 : El Universo EconÂÆ' ³micoLa EconomÂÆ' ­a cuenta con recursos naturales (bosques, minerales), bienes (libros, mÂÆ' ¡quinas) y capacidad del hombre (inteligencia, creatividad) para satisfacer las necesidades de los hombres.En el circuito econÂÆ' ³mico encontramos dos unidades econÂÆ' ³micas fundamentales: Unidad de Consumo (familias) y Unidad d e ProducciÂÆ' ³n (empresas). La primera ofrece a la segunda los Factores Productivos (naturaleza, trabajo, capital y direcciÂÆ' ³n) a cambio la unidad de producciÂÆ' ³n le retribuye con Ingresos (renta, salario, interÂÆ' ©s y beneficio). Con los factores productivos la unidad de ProducciÂÆ' ³n produce Bienes y Servicios que se los entregan a la unidad de Consumo a cambio de un Precio.Precios de Referencia PROCREAR. La Rioja y Catamar...Factores Productivos : Todo proceso productivo implica la concurrencia de los factores que hacen posible esa producciÂÆ' ³n de bienes y servicios.Trabajo: mano de obra.Capital: equipo fÂÆ' ­sico, que constituye los recursos productivos (materia prima, maquinaria, etc.).Naturaleza: recursos naturales.DirecciÂÆ' ³n: conducciÂÆ' ³n, administraciÂÆ' ³n.Moneda - MercadoLas primeras transacciones comerciales se desarrollaron a travÂÆ' ©s del trueque. Luego surgiÂÆ' ³ la necesidad de utilizar una unidad de cambio de acepta ciÂÆ' ³n generalizada que se denominÂÆ' ³ moneda.La moneda cumple estas funciones elementales: Medida de todos los valores, medio de divisibilidad y medio de atesoramiento.La moneda puede ser metÂÆ' ¡lica y billetes (el circulante de un paÂÆ' ­s).El concepto de dinero es mÂÆ' ¡s amplio que el de moneda. El dinero es el intermediario de todos los cambios. Tiene tres funciones: Medio de compra...

A Raisin in the Sun Study Guide for Act Three

A Raisin in the Sun Study Guide for Act Three This plot summary and study guide for Lorraine Hansberrys play, A Raisin in the Sun, provides an overview of Act Three. To learn more about the previous scenes, check out the following articles: A Raisin in the Sun: Act One, Scene OneA Raisin in the Sun: Act One, Scene TwoA Raisin in the Sun: Act Two, Scene OneA Raisin in the Sun: Act Two, Scene TwoA Raisin in the Sun: Act Two, Scene Three The third act of A Raisin in the Sun is a single scene. It takes place an hour after the events of Act Two (when $6500 was swindled from Walter Lee). In the stage directions, playwright Lorraine Hansberry describes the light of the living room as gray and gloomy, just as it was at the beginning of Act One. This dismal lighting represents the feeling of hopelessness, as though the future promises nothing. Joseph Asagais Proposal Joseph Asagai pays a spontaneous visit to the household, offering to help the family pack. Beneatha explains that Walter Lee lost her money for medical school. Then, she recounts a childhood memory about a neighbor boy who injured himself severely. When the doctors fixed his face and broken bones, young Beneatha realized she wanted to become a doctor. Now, she thinks that she has stopped caring enough to join the medical profession. Joseph and Beneatha then launch into an intellectual discussion about idealists and realists. Joseph sides with idealism. He is dedicated to improving life in Nigeria, his homeland. He even invites Beneatha to return home with him, as his wife. She is both bewildered and flattered by the offer. Joseph leaves her to think about the idea. Walters New Plan During his sisters conversation with Joseph Asagai, Walter has been listening intently from the other room. After Joseph leaves, Walter enters the living room and finds the business card of Mr. Karl Lindner, the chairman of the so-called welcoming committee of Clybourne Park, a neighborhood with white residents who are willing to pay a large amount of money to prevent black families from moving into the community. Walter leaves to contact Mr. Lindner. Mama enters and starts to unpack. (Because Walter lost the money, she no longer plans to move to the new house.) She remembers when as a child people would say that she always aimed too high. It seems she finally agrees with them. Ruth still wants to move. She is willing to go to work extreme hours in order to keep their new house in Clybourne Park. Walter returns and announces that he has made a call to the Man more specifically, he has asked Mr. Lindner back to their home to discuss a business arrangement. Walter plans to accept Lindners segregationist terms in order to make a profit. Walter has determined that humanity is divided into two groups: those who take and those who are tooken. From now on, Walter vows to be a taker. Walter Hits Rock Bottom Walter breaks down as he imagines putting on a pathetic show for Mr. Lindner. He pretends that he is speaking to Mr. Lindner, using a slave dialect to express how subservient he is in comparison to the white, property owner. Then, he goes into the bedroom, alone. Beneatha verbally disowns her brother. But Mama devoutly says that they must still love Walter, that a family member needs love the most when they have reached his lowest point. Little Travis runs in to announce the arrival of the moving men. At the same time, Mr. Lindner appears, carrying contracts to be signed. A Moment of Redemption Walter enters the living room, somber and ready to do business. His wife Ruth tells Travis to go downstairs because she does not want her son to see his father debase himself. However, Mama declares: MAMA: (Opening her eyes and looking into Walters.) No. Travis, you stay right here. And you make him understand what you doing, Walter Lee. You teach him good. Like Willy Harris taught you. You show where our five generations done come to. When Travis smiles up at his father, Walter Lee has a sudden change of heart. He explains to Mr. Lindner that his family members are plain but proud people. He tells of how his father worked for decades as a laborer, and that ultimately his father earned the right for his family to move into their new home in Clybourne Park. In short, Walter Lee transforms into the man his mother had prayed he would become. Realizing that the family is bent on moving into the neighborhood, Mr. Lindner shakes his head in dismay and leaves. Perhaps the most excited of all the family members, Ruth joyously shouts, Lets get the hell out of here! The moving men enter and begin to pack up the furniture. Beneatha and Walter exit as they argue about who would be a more suitable husband: the idealistic Joseph Asagai or the wealthy George Murchison. All of the family except Mama have left the apartment. She looks around one last time, picks up her plant, and leaves for a new home and a new life.

Argumentative Essay on Dog Fighting

Argumentative Essay on Dog Fighting Argumentative Essay on Dog Fighting Dog fighting is a sadistic practice that should be banned because of the many inhumane events that this practice entails. This practice entails breeding dogs specifically to make them fight. Such dogs are usually enclosed in small pits very early in their lives and, as they grow, are made to fight with other dogs to satisfy their owner’s gambling appetites. One of the main reasons why dog fighting should be illegal is because of the high level of suffering that these fights put the dog through. Given that the average dog fight lasts anywhere between one and two hours, the dogs that are participating in dog fights often suffer severe injuries that sometimes result in fatalities. Unlike other animals that naturally flee when they sense they cannot win a fight, the dogs that participate in dog fights are trained not to run away but to continue fighting regardless of the amount of injuries they sustain. It is only when the gambling appetites of their owners have been satisfied that the dogs are allowed to stop fighting. One of the most commonly used dogs in dog fights are pit bulls, which have powerful jaws and given that they rarely let go once they bite, the victim dog could end up suffering severe injuries, broken bones, and may even die just so their owner can have a chance of winning a bet. Some of the common injuries experienced by dogs that engage in dog fights include extreme blood loss, dehydration, extreme exhaustion, and even infections in the wounds they sustain. To make things even worse for these dogs, those that lose fights one too many times are often sacrificed for being weak and the same fate follows those dogs that are deemed old or those that do not fight as viciously as their owners expect them to. The injuries suffered by dogs that engage in fights have raised concerns in many authorities and in many places; this form of sadist sport has been classified as an illegal sport. Some individuals have even faced felony charges in courts of law. However, this seems not to have stopped some individuals from continuing to breed dogs so they can reap benefits from dog fighting. Interestingly enough, illegalizing this sport seems to have turned into a fortune for those who own dogs that engage in these fights because of the massive profits they get from those who are willing to pay large amounts of money so they can watch the illegal fights. It appears the authorities still need to do much more to deter people from willingly participating in dog fights. The minor penalties and convictions given to those found to have participated in dog fighting is not doing much to discourage this sport. The profits the dog fighters receive from the fights make the punishment, according to them; seem li ke a drop in the ocean. Perhaps it is time more severe penalties were enforced. Some tips on writing an argumentative essay: Make sure your thesis statement is clearly defined. Use transition words between paragraphs and make sure that your paragraphs are logically connected. Use facts and statistical data to support your arguments. Visit to buy argumentative essay on  Dog Fighting which will be written from scratch by highly qualified writers. You can get a free quote now!

Russian taxation and tax optimization schemes Essay

Russian taxation and tax optimization schemes - Essay Example Tax optimization schemes can therefore be said to be the structuring and organizing of a company’s or individual’s activities in order to reduce of minimize that their tax liabilities. This exercise which is becoming legal increases the amount of money maybe a company wishes to reinvest in its productive assets or even distribute among its shareholders (Saez, Slemrod, and Giertz 13 –50). There is no way to escape interacting with the tax authorities. Of the surveys conducted in the recent past, it emerges that there has been charged additional tax liabilities which are related to VAT and profit tax. These tax charges were due to insufficient economic documentation and justification. There has been consistent strengthening of the tax policy in Russia. This has lead to a marked reduction of tax payments. The Russian government introduced changes. These tax changes protect the integrity of the country’s tax system. These changes included the introduction of a mendments to the general anti-tax avoidance provisions. These are part of the tax optimization schemes that Russia is implementing. They include income tax exemptions and the introduction of the option of a tax liability in cases where a tenant qualifies for tax deductions. There are also schemes that let companies avoid declaring de facto members of staff as employees. This frees such staff from the obligation of paying high payroll taxes which my go up to thirty percent of one’s salary. This paper will look at income taxes or VAT and the tax Optimization schemes (Selen 17-67). Income taxes or VAT in Russia in 2012 and the tax Optimization schemes According to Ernst & Young (14 – 32), one of the structures for optimization in Russian that is most common is the creation and the usage of the of the corporate profit centers in the internal offshore zones as well as in foreign offshore jurisdictions. This has created an environment whereby some certain taxpayers are relea sed from taxation and the administrative territorial formations enjoying reductions in the tax rates in federal taxation. By utilizing these optimization schemes, they result to an effective profit tax rate. Most companies are employing these optimization schemes because of that they need to disclose their statements, so they can be issued with foreign securities, or obtain loans from foreign banks or even in cases of multinational mergers. Compared to other countries round the world the Russian Federation offers more protection to taxpayers. This is done by putting the burden of proving the usage of illegal tax shelters on the tax authorities. This has resulted to having the tax inspectorate taking the taxpayers to court to prove that tax shelters are illegal and in most cases the inspectorate loses such (Ernst & Young 14 – 32). The draconian rules that exist in other parts of the world, for instance the disclosure rules that were introduced in order to evaluate in United Ki ngdom the Inland Revenue on tax planning in advance, do not exist in Russia (Long 855-869). Russian businesses and foreign investors will at times be embroiled in tax disputes with the tax authorities. Due to changes introduced in tax laws which require a taxpayer to appeal to the highest tax authority before proceeding to court, it has really reduced these tax disputes. The tax optimization schemes have created an opportunity whereby conclusive amicable agreements with the tax authorities are reached during the litigation process (Long 855-869).

Organisational change and development Essay Example | Topics and Well Written Essays - 2000 words

Organisational change and development - Essay Example In addition, this paper will explain two approaches that may become critical in addressing such difficulty: project management and systems approach to management. Change in Organisations There are at least three important changes in organisational life today. First, there is the case of changing the organisational goals and objectives. An organisation passes through a life cycle and that, along the way, it is inevitable to make modifications in order to address and adapt to unforeseen challenges as well as new opportunities. Managers, in these cases, revise organisational objectives in order to enforce better management and operational initiatives (Stam and Andriessen, 2009, p. 136). Changing this fundamental aspect in an organisation entails far ranging restructuring and shifts. It involves the changing the rationale behind the organisation’s existence. Secondly, there is cultural change. It is the next logical step once a strategic change is adopted or when management decide s to change the organisational goals and objectives. The rationale is that in order to achieve effective change in that direction, a gradual change in mentalities must be achieved as well (Hamalainen and Saarinen, 2004, p.143). This is crucial in changing the organisational behaviour. ... In tandem with several external variables such as the spurt of innovations in the market, the increase in competition, and the level of complexity of the supply chain, among others, it forces organisations to change. Resistance Resistance to change is inherent in every organisation. This is the general consensus in academic literature and is largely based on the principle that organisations are made up of human beings and that resistance is part of human characteristics (Passmore, Woodman and Shani, 2010, p.234). Even researchers and academics who question the assumption of such pervasiveness, tacitly recognize the inevitability of resistance when they argue in focusing on the differences and contexts in the way people respond to change (Fisher and Howell, 2004; Piderit, 2000). The human variable in this theme ensures the persistence of such behaviour and underpins the methods behind change initiatives. Several thoughts attempted to explain resistance as a concept. For example, there is the position that it is â€Å"a reactive process where agents embedded in power relations oppose initiatives by other agents† (Jermier et al., 1994, p.9). The breadth of scholarly work and empirical evidences on this subject show conceptualizations of resistance as a behaviour, emotion and belief that determine the way people respond to change (Piderit, 2000, p.786). All in all, the theoretical and empirical evidences highlight the dominant view that resistance is both negative and counterproductive in implementing change; hence, it must be addressed. An excellent way to demonstrate the difficulty in handling resistance to change at the group level is to explain the dynamics of an approach in forming a team. When one is building a team from the ground

Keylogger Scam Essay Example | Topics and Well Written Essays - 750 words

Keylogger Scam - Essay Example ger pertains to hardware, a computer program, or a physical device, which aims at logging all the keystrokes that are input by the users and generated from the keyboard. These keystrokes are then secretly stored and logged without letting the computer users know that all that is being typed in can be seen by anyone else. Generally, the logs can be retrieved by the individual who has installed the key-logger into the computer only through the pressing of an arrangement of different keys at once and/or by inputting a confidential password. In numerous situations, the log cannot be transferred by the key-logger remotely through Bluetooth, email or any such methods. There are various ways in which the individuals can avoid being subject to the keylogger scams. For instance, it is essential to read all the terms and conditions when any offer is received over the Internet because claims made by extremely cheap offers have costs and threats hidden in them. Unsolicited or suspicious emails should not be opened, and no links in spam mails should be clicked. Above all, the software should be installed to protect the computer from unwanted programs or viruses. Any harmful gaming or music websites should not be opened as they may become a source of hidden viruses or scams (Scam watch, 2012). Public computers should never be used for making any transactions which expose private information. It is essential to keep the spyware updated, and an encrypted file should also be maintained to keep passwords. There are several types of computer crimes that are committed in the modern era. They include identity theft, bank frauds, theft of classified information, extortion, cyber stalking, phishing scams and many others. All these are the modern crimes that aim at spoiling an individuals identity or posing harms and dangers to the personal or financial assets. In both political and industrial espionage, keyloggers can be utilized as tools to access data which might include classified

Knowledge Management, Social Networks and Innovation Essay - 4

Knowledge Management, Social Networks and Innovation - Essay Example Through this, organizations aim to acquire and create potentially useful knowledge that can be used to achieve maximum effective usage to influence the organizational performance positively. What has been learned is then embedded into the organization’s fabric through organizational learning that is complementary to knowledge management (Easterby et al, 1999). A company like China Telecom happens to be the largest fixed-line service provider in China. It is also the third largest mobile telecommunication provider in the country. The company offers an attractive full range of integrated information, application services, and internet connection. It has over 200,000 staff members with branches in other regions of the Americas, Hong Kong, Europe and Macao. In order to stay competitive, the company accelerates creation of new products through optimal use of its worker base in a unified innovation process. To facilitate collaboration among employees, customers, and partners the company developed innovation platforms with Web portal interfaces. The portal in turn accepts ideas and innovative experiences from the enlarged community. The company’s marketing team analyzes new acquired information that is gathered from the consumers’ Web 2.0 entries and uses the information to introduce and launch new products and services with the kn owledge that subscriber demand exists. The company embraces an open dialog with its customers, employees and partners through social tools that involve them in internal and external processes. By using social networking tools like social media tools, a culture of information sharing is encouraged within an organization. They provide a gateway for the exchange of current and relevant information across organizational silos and geographies. To drive a social change in the work force it is essential for organizations to build trust and encourage social interactions. Social networking tools also empower employees and

Self-Disclosure Peer Review Essay Example for Free

Self-Disclosure Peer Review Essay In the field of psychiatry, self-disclosure is only limited on the side of the therapist because the purpose of the session is to elicit as much information as the therapist can from the client. This is necessary so as to effectively provide solutions for the client’s psychological problems. If the therapist would inject self-disclosing moments during the session, this can either make the client feel insignificant and incompetent. However, if self-disclosure would be used appropriately, it can further enhance the session thus speeding up the process. In line with this, I think if a therapist decides to disclose personal experiences or information to the client, caution must always be in mind. Clients should be treated gently as if they are always in a vulnerable state. Through this, additional problems or conflicts can be prevented from manifesting. Post No. 2 by Meagan Bowser I agree with what the learner have posted about self-disclosure. This method should be utilized correctly and it should be implemented at the right time. Therapists must always put the clients first before themselves during sessions so as to emphasize that the focus are the clients and not the therapists. However, since people are distinct from one another using self-disclosure as a tool to resolve problems can have varying results. A certain approach for a specific client may not be applicable to another client. More so, is it appropriate for therapists to make up information in order to show empathy? For example, if a therapist has no experience or any idea about the situation of the client, can the therapists create fictional experiences so as to make the clients feel that they are not alone? Will this gesture be ethical or not?

Artificial Neural Networks to forecast London Stock Exchange

Artificial Neural Networks to forecast London Stock Exchange Abstract This dissertation examines and analyzes the use of the Artificial Neural Networks (ANN) to forecast the London Stock Exchange. Specifically the importance of ANN to predict the future trends and value of the financial market is demonstrated. There are several contributions of this study to this area. The first contribution of this study is to find the best subset of the interrelated factors at both local and international levels that affect the London stock exchange from the various input variables to be used in the future studies. We use novel aspects, in the sense that we base the forecast on both the fundamental and technical analysis.The second contribution of this study was to provide well defined methodology that can be used to create the financial models in future studies. In addition, this study also gives various theoretical arguments in support of the approaches used in the construction of the forecasting model by comparing the results of the previous studies and modifying some of the existing approaches and tested them. The study also compares the performance of the statistical methods and ANN in the forecasting problem. The main contribution of this thesis lies in comparing the performance of the five different types of ANN by constructing the individual forecasting model of them. Accuracy of models is compared by using different evaluation criteria and we develop different forecasting models based on both the direction and value accuracy of the forecasted value. The fourth contribution of this study is to investigate whether the hybrid approach combining different individual forecasting models can outperform the individual forecasting models and compare the performance of the different hybrid approaches. Three hybrid approaches are used in this study, two are existing approaches and the third original approach, the mixed combined neural network -is being proposed in this study to the academic studies to forecast the stock exchange. The last contribution of this study lies in modifying the existing trading strategy to increase the profitability of the investor and support the argument that the investor earns more profit if the forecasting model is being developed by using the direction accuracy as compared to the value accuracy. The best forecasting classification accuracy obtained is 93% direction accuracy and 0.0000831 (MSE) value accuracy which are better than the accuracies obtained by the previous academic studies. Moreover, this research validates the work of the existing studies that hybrid approach outperforms the individual forecasting model. In addition, the rate of the return that was attained in this thesis by using modified trading strategy is 120.14% which has shown significant improvement as compared to the 10.8493% rate of return of the existing trading strategy in other academics studies. The difference in the rate of return could be due to the fact that this study has developed good forecasting model or a better trading strategy. The experimental results show our method not only improves the accuracy rate, but also meet the short-term investors’ expectations. The results of this thesis also support the claim that some financial time series are not entirely random, and that contrary to the predictions of the efficient markets hypothesis (EMH), a trading strategy could be based solely on historical data. It was concluded that ANN do have good capabilities to forecast financial markets and, if properly trained, the investor could benefit from the use of this forecasting tool and trading strategy. Chapter 1 1 Introduction 1.1 Background to the Research Financial Time Series forecasting has attracted the interest of academic researchers and it has been addressed since the 1980.It is a challenging problem as the financial time series have complex behavior, resulting from a various factors such as economic, psychological or political reasons and they are non-stationary , noisy and deterministically chaotic. In today’s world, almost every individual is influenced by the fluctuations in the stock market. Now day’s people prefer to invest money in the diversified financial funds or shares due to its high returns than depositing in the banks. But there is lot of risk in the stock market due to its high rate of uncertainty and volatility. To overcome such risks, one of the main challenges for many years for the researchers is to develop the financial models that can describe the movements of the stock market and so far there had not been an optimum model. The complexity and difficulty of forecasting the stock exchange, and the emergence of data mining and computational intelligence techniques, as alternative techniques to the conventional statistical regression and Bayesian models with better performance, have paved the road for the increased usage of these techniques in fields of finance and economics. So, traders and investors have to rely on the various types of intelligent systems to make trading decisions. (Hameed,2008). A Computational Intelligence system such as neural networks, fuzzy logic, genetic algorithms etc has been widely established research area in the field of information systems. They have been used extensively in forecasting of the financial market and they have been quite successful to some extent .Although the number of purposed methods in financial time series is very large , but no one technique has been successful to consistently to â€Å"beat the market†. For last three decades, opposing views have existed between the academic communities and traders about the topic of â€Å"Random walk theory â€Å"and â€Å"Efficient Market Hypothesis(EMH)† due to the complexity of the financial time series and lot of publications by different researchers have gather various amount of evidences in support as well as against it. Lehman (1990), Haugen (1999) and Lo (2000) gave evidence of the deficiencies in EMH. But the investors such as Warren Buffet for long period of time have beaten the stock market consistently. Market Efficiency or â€Å"Random walk theory† in terms of stock trading in the financial market means that it is impossible to earn excess returns using any historic information. In essence, then, the new information is the only variable that causes to alter the price of the index as well as used to predict the arrival and timing. Bruce James Vanstone (2005) stated that in an efficient market, security prices should appear to be randomly generated. Both sides in this argument are supported by empirical results from the different markets across over the globe. This thesis does not wish to enter into the argument theoretically whether to accept or reject the EMH. Instead, this thesis concentrates on the methodologies to be used for development of the financial models using the artificial neural networks (ANN), compares the forecasting capabilities of the various ANN and hybrid based approach models, develop the trading strategy that can help the investor and leaves the research of this thesis to stack up with the published work of other researchers which document ways to predict the stock market. In recent years and since its inception, ANN has gained momentum and has been widely used as a viable computational intelligent technique to forecast the stock market. The main challenge of the traders is to know the signals when the stock market deviates and to take advantage of such situations. The data used by the traders to remove the uncertainty in the stock market and to take trading decisions whether to buy or sell the stock using the information process is â€Å"noisy†. Information not contained in the known information subset used to forecast is considered to be noise and such environment is characterized by a low signal-to noise ratio. Refenes et.al (1993) and Thawornwong and Enke (2004) described that the relationship between the security price or returns and the variables that constitute that price (return), changes over time and this fact is widely accepted within the academic institutes. In other words, the stock market‘s structural mechanics may change over time which causes the effect on the index also change. Ferreira et al. (2004) described that the relationship between the variables and the predicted index is non linear and the Artificial neural networks (ANN) have the characteristic to represent such complex non-linear relationship. This thesis presents the mechanical London Stock Market trading system that uses the ANN forecasting model to extract the rules from daily index movements and generate signal to the investors and traders whether to buy, sell or hold a stock. The figure 1 and 2 represents the stock exchange and ANN forecasting model. By viewing the stock exchange as a financial market that takes historical and current data or information as an input, the investors react to this information based on their understanding, speculations, analysis etc. It would now seem very difficult to predict the stock market, characterized by high noise, nonlinearities, using only high frequency (weekly, daily) historical prices. Surprisingly though, there are anomalies in the behavior of the stock market that cannot be explained under the existing paradigm of market efficiency. Studies discussed in the literature review have been able to predict the stock market accurately to some extent and it seems that forecasting model developed by them have been able to pick some of the hidden patterns in the inherently non-linear price series. While it is true that forecasting model need to be designed and optimized with care in order to get accurate results . Further, it aims to contribute knowledge that will one day lead to a standard or optimum model for the prediction of the stock exchange. As such, it aims to present a well defined methodology that can be used to create the forecasting models and it is hoped that this thesis can address many of the deficiencies of the published research in this area. In the last decade, there has been plethora of the ANN models that were developed due to the absence of the well defined methodology, which were difficult to compare due to less published work and some of them have shown superior results in their domains. Moreover, this study also compares the predictive power of the ANN with the statistical models. Normally the approach used by the academic researchers in the forecasting use technical analysis and some of them include the fundamental analysis. The technical analysis uses only historical data (past price) to determine the movement of the stock exchange and fundamental analysis is based on external information (like interest rates, prices and returns of other asset) that comes from the economic system surrounding the financial market. Building a trading system using forecasting model and testing it on the evaluation criteria is the only practical way to evaluate the forecasting model. There has been so much prior research on identifying the appropriate trading strategy for forecasting problem. This thesis does not wish to enter into the argument which strategy is best or not. Although, the importance of the trading strategy can hardly be underestimated, but this thesis concentrates on using one of the existing strategy, modify it and compares the return by the forecasting models. But there has always been debate in the academic studies over how to effectively benchmark the model of ANN for trading. Some of the academic researchers stated that predicting the direction of the stock exchange may lead to higher profits while some of them supported the view that predicting the value of the stock exchange may lead to higher rate of return. Azoff (1994) and Thawornwong and Enke (2004) discussed about this debate in their study. In essence, there is a need for a formalized development methodology for developing the ANN financial models which can be used as a benchmark for trading systems. All of this is accommodated by this thesis. 1.2 Problem Statement and Research Question The studies mentioned above have generally indicated that ANN, as used in the stock market, can be a valuable tool to the investor .Due to some of the problems discussed above, we are not still able to answer the question: Can ANNs be used to develop the accurate forecasting model that can be used in the trading systems to earn profit for the investor? From the variety of academic research summarized in the literature review, it is clear that a great deal of research in this area has taken place by different academic researchers and they have gathered various amounts of evidences in support as well as against it. This directly threatens the use of ANN applicability to the financial industry. Apart from the previous question, this research addresses various other problems: 1. Which ANN have better performance in the forecasting of the London Stock Exchange from the five different types of the ANN which are widely used in the academics? 2. Which subset of the potential input variables from 2002-08 affect the LSE? 3. Do international stock exchanges, currency exchange rate and other macroeconomic factors affect the LSE? 4. How much the performance of the forecasting model is improved by using the regression analysis in the factor selection? 5. Can use of the technical indicators improve the performance of the forecasting model? 6. Which learning algorithm in the training of the ANN give the better performance? 7. Does Hybrid-based Forecasting Models give better performance than the individual ANN forecasting models? 8. Which Hybrid-based models have the better performance and what are the limitations of using them? 9. Does the forecasting model developed on the basis of the percentage accuracy gives more rate of the return as compared to the value accuracy? 10. Does the forecasting model having better performance in terms of the accuracy increase the profit of the investor when applied to the trading strategy? Apart from all questions outlined above, it addresses various another questions regarding the design of the ANN. †¢ Are there any approaches to solve the various issues in designing of the ANN like number of hidden layers and activation functions? This thesis will attempt to answer the above question within the constraints and scope of the 6-year sample period (from 2002-2008) using historical data of various variables that affect the LSE. Further, this thesis will also attempt to answer these questions within the practical constraints of transaction costs and money management imposed by real-world trading systems. Although a formal statement of the methodology or steps that is being used is left until section 3, it makes sense to discuss the way in which this thesis will address the above question. In this thesis, various types of ANN will be trained using fundamental data, and technical data according to the direction and value accuracy. A better trading system development methodology will be defined, and the performance of the forecasting model will be checked by using evaluation criteria rate of the return .In this way, the benefits of incorporating ANN into trading strategies in the stock market can be exposed and quantified. Once this process has been undertaken, it will be possible to answer the thesis all questions. 1.3 Motivation of the Research Stock market has always had been an attractive appeal for the researchers and financial investors and they have studied it over again to extract the useful patterns to predict the movement of the stock market. The reason is that if the researchers can make the accurate forecasting model, they can beat the market and can gain excess profit by applying the best trading strategy. Numerous financial investors have suffered lot of financial losses in the stock market as they were not aware of the stock market behavior. They had the problem that they were not able to decide when they should sell or buy the stock to gain profit. Nevertheless, finding out the best time for the investor to buy or to sell has remained a very difficult task because there are too many factors that may influence stock prices. If the investors have the accurate forecasting model, then they can predict the future behavior of the stock exchange and can gain profit. This solves the problem of the financial investors to some extent as they will not bear any financial loss. But it does not guarantee that the investor can have better profit or rate of return as compared to other investors unless he utilized the forecasting model using better trading strategy to invest money in the share market. This thesis tries to solve the above problem by providing the investor better forecasting model and trading strategies that can be applied to real-world trading systems. 1.4 Justification of Research There are several features of this academic research that distinguish it from previous academic researches. First of all, the time frame chosen for the investigation of the ANN (2002-08) in the London Stock Exchange has never been tested in the previous academic work. The importance of the period chosen is that there are two counter forces, which are opposing each other. On the one hand, the improvement of the UK and other countries economy after the 2001 financial crises happened in this period as a whole. On the other hand, this period also shows the decline in the stock markets from Jan, 2008 to Dec, 2008. So, it is important to test the forecasting model for bull, stable and bear market. Second, some of the research questions addressed in the above section, have not been investigated much in the academic studies, especially there is hardly any study which have done research on all the problems. Moreover, original hybrid based mixed neural network, better trading strategy and other modified approaches have been successfully being described and used in this study Finally, there is a significant lack of work carried out in this area in the LSE. As such, this thesis draws heavily on results published mainly within the United States and other countries; from the academics .One interesting aspect of this thesis is that it will be interesting to see how much of the published research on application of ANN in stock market anomalies is applicable to the UK market. This is important as some of the academic studies (Pan et al (2005)) states that each stock market in the globe is different. 1.5 Delimitations of scope The thesis concerns itself with historical data for the variables that affect London Stock Exchange during the period 2002 – 2008. 1.6 Outline of the Report The remaining part of the thesis is organized in the following six chapters. The second chapter, the background and literature review, provides a brief introduction to the domain and also pertinent literature is reviewed to discuss the related published work of the previous researchers in terms of their contribution and content in the prediction of the stock exchange which serves as the building block for much of the research. Moreover, this literature review also gave solid justification why a particular set of ANN inputs are selected, which is important step according to the Thawornwong and Enke (2004) and and some concepts from finance. The third chapter, the methodology, describes the steps in detail, data and the mechanics or techniques that take place in the thesis along with the empirical evidence. In addition, it also discuss the literature review for each step. Formulas and diagrams are shown to explain the techniques when necessary and it also covers issues as software and hardware used in the study. The fourth chapter, the implementation, discusses the approaches used in the implementation in detail based on the third chapter. It also covers such issues as software and hardware used in the study. The fifth chapter, the results and analysis, present the results according to the performance and benchmark measures that we have used in this study to compare with other models. It describes the choices that were needed in making model and justifies these choices in terms of the literature. The sixth chapter, conclusions and further work, restates the thesis hypothesis, discuss the conclusions drawn from the project and also thesis findings are put into perspective. Finally, the next steps to improve the model performance are considered. Chapter 2 Background and Literature Review 2 Background and Literature Review This section of thesis explores the theory of three relevant fields of the Financial Time Series, Stock Market, and Artificial Neural Networks, which together form the conceptual frameworks of the thesis as shown in the figure 1. Framework is provided to the trader to make quantitative and qualitative judgments concerning the future stock exchange movements. These three fields are reviewed in historical context, sketching out the development of those disciplines, and reviewing their academic credibility, and their application to this thesis. In the case of Neural Networks, the field is reviewed with regard to that portion of the literature which deals with applying neural network to the prediction of the stock exchange, the various type of techniques and neural networks used and an existing prediction model is extended to allow a more detailed analysis of the area than would otherwise have been possible. 2.1 Financial Time Series 2.1.1 Introduction The field of the financial time series prediction is a highly complex task due to the following reasons: 1. The financial time series frequently behaves like a random-walk process and predictability of such series is controversial issue which has been questioned in scope of EMH. 2. The statistical property of the financial time series shift with the different time. Hellstr ¨om and Holmstr ¨om [1998]). 3. Financial time series is usually noisy and the models which have been able to reduce such noise has been the better model in forecasting the value and direction of the stock exchange. 4. In the long run, a new forecasting technique becomes a part of the process to be forecasted, i.e. it influences the process to be forecasted (Hellstr ¨om and Holmstr ¨om [1998]). The first point is explained later in this section while discussing the EMH theory (Page).The graph of the volatility time series of FTSE 100 index from 14 June, 1993 to 29 December, 1998 and Dow Jones from 1928 to 2000 by Nelson Areal (2008) and Negrea Bogdan Cristian (2007) illustrates the second point of the FTSE 100 [2.1.r]in figure 2.1.1 and 2.2.2.These figures also shows that the volatility changes with period , in some periods FTSE 100 index value fluctuates so much and in some it remains calm. The third point is explained by the fact the events on a particular data affect the financial time series of the index, for example, the volatility of stocks or index increases before announcement of major stock specific news (Donders and Vorst [1996]). These events are random and contribute noise in the time series which may make difficult to compare the two forecasting models difficult to compare as a random model can also produce results. The fourth result can be explained by the example. Suppose a company develop a model or technique that can outcast all other models or techniques. The company will make lot of profits if this model is available to less people. But if this technique is available to all people with time due to its popularity, than the profits of the company will decrease as the company will not no longer take advantage of this technique. This argument is described in Hellstr ¨om and Holmstr ¨om [1998] and Swingler [1994] . 2.1.2 Efficient Market Hypothesis (EMH) EMH Theory has been a controversial issue for many years and there has been no mutual agreed deal among the academic researchers, whether it is possible to predict the stock price. The people who believe that the prices follow â€Å"random walk† trend and cannot be predicted, are usually people who support the EMH theory. Academic researchers( Tino et al. [2000]), have shown that the profit can be made by using historical information , whereas they also found difficult to verify the strong form due to lack of all private and public data. The EMH was developed in 1965 by Fama (Fama [1965], Fama [1970]) and has found widely accepted (Anthony and Biggs [1995], Malkiel [1987], White [1988], Lowe and Webb [1991]) in the academic community (Lawrence et al. [1996]).It states that the future index or stock value is completely unpredictable given the historical information of the index or stocks. There are three forms of EMH: weak, semi-strong, and strong form. The weak EMH rules out any form of forecasting based on the stock’s history, since the stock prices follows a random walk in which in which successive changes have zero correlation (Hellstr ¨om and Holmstr ¨om [1998]). In Semi Strong hypothesis, we consider all the publicly available information such as volume data and fundamental data. In strong form, we consider all the publicly and privately available information. Another reason for argument against the EMH is that different investors or traders react differently when a stock suddenly drops in a value. These different time perspectives will cause the unexpected change in the stock exchange, even if the new information has not entered in the scene. It may be possible to identify these situations and actually predict future changes (Hellstr  ¨om and Holmstr ¨om [1998]) The developer have proved it wrong by making forecasting models, this issue remains an interesting area. This controversy is just only matter of the word immediately in the definition. The studies in support of the argument of EMH rely on using the statistical tests and show that the technical indicators and tested models can’t forecast. However, the studies against the argument uses the time delay between the point when new information enters the model or system and the point when the information has spread across over the globe and a equilibrium has been reached in the stock market with a new market price. 2.1.3 Financial Time Series Forecasting Financial Time series Forecasting aims to find underlying patterns, trends and forecast future index value using using historical and current data or information. The historic values are continuous and equally spaced value over time and it represent various types of data . The main aim of the forecasting is to find an approximate mapping function between the input variables and the forecasted or output value . According to Kalekar (2004), Time series forecasting assumes that a time series is a combination of a pattern and some error. The goal of the model using time series is to separate the pattern from the error by understanding the trend of the pattern and its seasonality Several methods are used in time series forecasting like moving average (section ) moving averages, linear regression with time etc. Time series differs from the technical analysis (section) that it is based on the samples and treated the values as non-chaotic time series. Many academic researchers have applied t ime series analysis in their forecasting model, but there has been no major success. [1a] 2.2 Stock Market 2.2.1 Introduction Let us consider the basics of the stock market. MM What are stocks? Stock refers to a share in the ownership of a corporation or company. They represent a claim of the stock owner on the company’s earnings and assets and by buying more stocks; the stake in the ownership is increased. In United States, stocks are often referred as shares, whereas in the UK they are also used as synonym for bonds, shares and equities. MM Why a Company issues a stock? The main reason for issuing stock is that the company wants to raise money by selling some part of the company. A company can raise money by two ways: â€Å"debt financing† (borrowing money by issuing bonds or loan from bank) and â€Å"equity financing â€Å"(borrowing money by issuing stocks).It is advantageous to raise the money by issuing stocks as the company has not to pay money back to the stock owners but they have to share the profit in the form of the dividends. MM What is Stock Pricing or price? A stock price is the price of a single stock of a number of saleable stocks traded by the company. A company issue stock at static price, and the stock price may increase or decrease according to the trade. Normally the price of the stocks in the stock market is determined by the supply/demand equilibrium. MM What is a Stock Market? Stock Market or equity market is a public market where the trading and issuing of a company stock or derivates takes place either through the stock exchange or they may be traded privately and over-the counter markets. It is vital part of the economy as it provides opportunities to the company to raise money and also to the investors of having potential gain by selling or buying share. The stock market in the US includes the NYSE, NASDAQ, the AMEX as well as many regional exchanges. London Stock Exchange is the major stock exchange in the UK and Europe.As mentioned in the Chapter 1, in this study we forecast the London Stock Exchange (Section 2.2.2.). Investing in the stock market is very risky as the stock market is uncertain and unsteady. The main aim of the investor is to get maximum returns from the money invested in the stock market, for which he has to study about the performance, price history about the stock company .So it is a broad category and according to Hellstrom (1997), there are four main ways to predict the stock market: 1. Fundamental analysis (section 2.2.3) 2. Technical analysis, (section 2.2.4) 3. Time series forecasting (section 2.1) 4. Machine learning (ANN). (Section 2.3) 2.2.2 London Stock Exchange London Stock Exchange is one of the world’s oldest and largest stock exchanges in the world, which started its operation in 1698, when John Casting commenced â€Å"at this Office in Jonathan’s Coffee-house† a list of stock and commodity prices called â€Å"The Course of the Exchange and other things† [2] .On March 3, 1801, London Stock Exchange was officially established with current lists of over 3,200 companies and has existed, in one or more form or another for more than 300 years. In 2000, it decided to become public and listed its shares on its own stock exchange in 2001. The London Stock market consists of the Main Market and Alternative Investments Market (AIM), plus EDX London (exchange for equity derivatives). The Main Market is mainly for established companies with high performance, and AIM hand trades small-caps, or new enterprises with high growth potential.[1] Since the launch of the AIM in 1995, AIM has become the most successful growth market in the world with over 3000 companies from across the globe have joined AIM. To evaluate the London Stock Exchange, the autonomous FTSE Group (owned by the Financial Times and the London Stock Exchange) , sustains a series of indices comprising the FTSE 100 Index, FTSE 250 Index, FTSE 350 Index, FTSE All-Share, FTSE AIM-UK 50, FTSE AIM 100, FTSE AIM All-Share, FTSE SmallCap, FTSE Tech Mark 100 ,FTSE Tech Mark All-Share.[4] FTSE 100 is the most famous and composite index calculated respectively from the top 100 largest companies whose shares are listed on the London Stock Exchange. The base date for calculation of FTSE 100 index is 1984. [2] In the UK, the FTSE 100 is frequently used by large investor, financial experts and the stock brokers as a guide to stock market performance. The FTSE index is calculated from the following formula: 2.2.3 Fundamental Analysis Fundamental Analysis focuses on evaluation of the future stock exchange movements Artificial Neural Networks to forecast London Stock Exchange Artificial Neural Networks to forecast London Stock Exchange Abstract This dissertation examines and analyzes the use of the Artificial Neural Networks (ANN) to forecast the London Stock Exchange. Specifically the importance of ANN to predict the future trends and value of the financial market is demonstrated. There are several contributions of this study to this area. The first contribution of this study is to find the best subset of the interrelated factors at both local and international levels that affect the London stock exchange from the various input variables to be used in the future studies. We use novel aspects, in the sense that we base the forecast on both the fundamental and technical analysis.The second contribution of this study was to provide well defined methodology that can be used to create the financial models in future studies. In addition, this study also gives various theoretical arguments in support of the approaches used in the construction of the forecasting model by comparing the results of the previous studies and modifying some of the existing approaches and tested them. The study also compares the performance of the statistical methods and ANN in the forecasting problem. The main contribution of this thesis lies in comparing the performance of the five different types of ANN by constructing the individual forecasting model of them. Accuracy of models is compared by using different evaluation criteria and we develop different forecasting models based on both the direction and value accuracy of the forecasted value. The fourth contribution of this study is to investigate whether the hybrid approach combining different individual forecasting models can outperform the individual forecasting models and compare the performance of the different hybrid approaches. Three hybrid approaches are used in this study, two are existing approaches and the third original approach, the mixed combined neural network -is being proposed in this study to the academic studies to forecast the stock exchange. The last contribution of this study lies in modifying the existing trading strategy to increase the profitability of the investor and support the argument that the investor earns more profit if the forecasting model is being developed by using the direction accuracy as compared to the value accuracy. The best forecasting classification accuracy obtained is 93% direction accuracy and 0.0000831 (MSE) value accuracy which are better than the accuracies obtained by the previous academic studies. Moreover, this research validates the work of the existing studies that hybrid approach outperforms the individual forecasting model. In addition, the rate of the return that was attained in this thesis by using modified trading strategy is 120.14% which has shown significant improvement as compared to the 10.8493% rate of return of the existing trading strategy in other academics studies. The difference in the rate of return could be due to the fact that this study has developed good forecasting model or a better trading strategy. The experimental results show our method not only improves the accuracy rate, but also meet the short-term investors’ expectations. The results of this thesis also support the claim that some financial time series are not entirely random, and that contrary to the predictions of the efficient markets hypothesis (EMH), a trading strategy could be based solely on historical data. It was concluded that ANN do have good capabilities to forecast financial markets and, if properly trained, the investor could benefit from the use of this forecasting tool and trading strategy. Chapter 1 1 Introduction 1.1 Background to the Research Financial Time Series forecasting has attracted the interest of academic researchers and it has been addressed since the 1980.It is a challenging problem as the financial time series have complex behavior, resulting from a various factors such as economic, psychological or political reasons and they are non-stationary , noisy and deterministically chaotic. In today’s world, almost every individual is influenced by the fluctuations in the stock market. Now day’s people prefer to invest money in the diversified financial funds or shares due to its high returns than depositing in the banks. But there is lot of risk in the stock market due to its high rate of uncertainty and volatility. To overcome such risks, one of the main challenges for many years for the researchers is to develop the financial models that can describe the movements of the stock market and so far there had not been an optimum model. The complexity and difficulty of forecasting the stock exchange, and the emergence of data mining and computational intelligence techniques, as alternative techniques to the conventional statistical regression and Bayesian models with better performance, have paved the road for the increased usage of these techniques in fields of finance and economics. So, traders and investors have to rely on the various types of intelligent systems to make trading decisions. (Hameed,2008). A Computational Intelligence system such as neural networks, fuzzy logic, genetic algorithms etc has been widely established research area in the field of information systems. They have been used extensively in forecasting of the financial market and they have been quite successful to some extent .Although the number of purposed methods in financial time series is very large , but no one technique has been successful to consistently to â€Å"beat the market†. For last three decades, opposing views have existed between the academic communities and traders about the topic of â€Å"Random walk theory â€Å"and â€Å"Efficient Market Hypothesis(EMH)† due to the complexity of the financial time series and lot of publications by different researchers have gather various amount of evidences in support as well as against it. Lehman (1990), Haugen (1999) and Lo (2000) gave evidence of the deficiencies in EMH. But the investors such as Warren Buffet for long period of time have beaten the stock market consistently. Market Efficiency or â€Å"Random walk theory† in terms of stock trading in the financial market means that it is impossible to earn excess returns using any historic information. In essence, then, the new information is the only variable that causes to alter the price of the index as well as used to predict the arrival and timing. Bruce James Vanstone (2005) stated that in an efficient market, security prices should appear to be randomly generated. Both sides in this argument are supported by empirical results from the different markets across over the globe. This thesis does not wish to enter into the argument theoretically whether to accept or reject the EMH. Instead, this thesis concentrates on the methodologies to be used for development of the financial models using the artificial neural networks (ANN), compares the forecasting capabilities of the various ANN and hybrid based approach models, develop the trading strategy that can help the investor and leaves the research of this thesis to stack up with the published work of other researchers which document ways to predict the stock market. In recent years and since its inception, ANN has gained momentum and has been widely used as a viable computational intelligent technique to forecast the stock market. The main challenge of the traders is to know the signals when the stock market deviates and to take advantage of such situations. The data used by the traders to remove the uncertainty in the stock market and to take trading decisions whether to buy or sell the stock using the information process is â€Å"noisy†. Information not contained in the known information subset used to forecast is considered to be noise and such environment is characterized by a low signal-to noise ratio. Refenes et.al (1993) and Thawornwong and Enke (2004) described that the relationship between the security price or returns and the variables that constitute that price (return), changes over time and this fact is widely accepted within the academic institutes. In other words, the stock market‘s structural mechanics may change over time which causes the effect on the index also change. Ferreira et al. (2004) described that the relationship between the variables and the predicted index is non linear and the Artificial neural networks (ANN) have the characteristic to represent such complex non-linear relationship. This thesis presents the mechanical London Stock Market trading system that uses the ANN forecasting model to extract the rules from daily index movements and generate signal to the investors and traders whether to buy, sell or hold a stock. The figure 1 and 2 represents the stock exchange and ANN forecasting model. By viewing the stock exchange as a financial market that takes historical and current data or information as an input, the investors react to this information based on their understanding, speculations, analysis etc. It would now seem very difficult to predict the stock market, characterized by high noise, nonlinearities, using only high frequency (weekly, daily) historical prices. Surprisingly though, there are anomalies in the behavior of the stock market that cannot be explained under the existing paradigm of market efficiency. Studies discussed in the literature review have been able to predict the stock market accurately to some extent and it seems that forecasting model developed by them have been able to pick some of the hidden patterns in the inherently non-linear price series. While it is true that forecasting model need to be designed and optimized with care in order to get accurate results . Further, it aims to contribute knowledge that will one day lead to a standard or optimum model for the prediction of the stock exchange. As such, it aims to present a well defined methodology that can be used to create the forecasting models and it is hoped that this thesis can address many of the deficiencies of the published research in this area. In the last decade, there has been plethora of the ANN models that were developed due to the absence of the well defined methodology, which were difficult to compare due to less published work and some of them have shown superior results in their domains. Moreover, this study also compares the predictive power of the ANN with the statistical models. Normally the approach used by the academic researchers in the forecasting use technical analysis and some of them include the fundamental analysis. The technical analysis uses only historical data (past price) to determine the movement of the stock exchange and fundamental analysis is based on external information (like interest rates, prices and returns of other asset) that comes from the economic system surrounding the financial market. Building a trading system using forecasting model and testing it on the evaluation criteria is the only practical way to evaluate the forecasting model. There has been so much prior research on identifying the appropriate trading strategy for forecasting problem. This thesis does not wish to enter into the argument which strategy is best or not. Although, the importance of the trading strategy can hardly be underestimated, but this thesis concentrates on using one of the existing strategy, modify it and compares the return by the forecasting models. But there has always been debate in the academic studies over how to effectively benchmark the model of ANN for trading. Some of the academic researchers stated that predicting the direction of the stock exchange may lead to higher profits while some of them supported the view that predicting the value of the stock exchange may lead to higher rate of return. Azoff (1994) and Thawornwong and Enke (2004) discussed about this debate in their study. In essence, there is a need for a formalized development methodology for developing the ANN financial models which can be used as a benchmark for trading systems. All of this is accommodated by this thesis. 1.2 Problem Statement and Research Question The studies mentioned above have generally indicated that ANN, as used in the stock market, can be a valuable tool to the investor .Due to some of the problems discussed above, we are not still able to answer the question: Can ANNs be used to develop the accurate forecasting model that can be used in the trading systems to earn profit for the investor? From the variety of academic research summarized in the literature review, it is clear that a great deal of research in this area has taken place by different academic researchers and they have gathered various amounts of evidences in support as well as against it. This directly threatens the use of ANN applicability to the financial industry. Apart from the previous question, this research addresses various other problems: 1. Which ANN have better performance in the forecasting of the London Stock Exchange from the five different types of the ANN which are widely used in the academics? 2. Which subset of the potential input variables from 2002-08 affect the LSE? 3. Do international stock exchanges, currency exchange rate and other macroeconomic factors affect the LSE? 4. How much the performance of the forecasting model is improved by using the regression analysis in the factor selection? 5. Can use of the technical indicators improve the performance of the forecasting model? 6. Which learning algorithm in the training of the ANN give the better performance? 7. Does Hybrid-based Forecasting Models give better performance than the individual ANN forecasting models? 8. Which Hybrid-based models have the better performance and what are the limitations of using them? 9. Does the forecasting model developed on the basis of the percentage accuracy gives more rate of the return as compared to the value accuracy? 10. Does the forecasting model having better performance in terms of the accuracy increase the profit of the investor when applied to the trading strategy? Apart from all questions outlined above, it addresses various another questions regarding the design of the ANN. †¢ Are there any approaches to solve the various issues in designing of the ANN like number of hidden layers and activation functions? This thesis will attempt to answer the above question within the constraints and scope of the 6-year sample period (from 2002-2008) using historical data of various variables that affect the LSE. Further, this thesis will also attempt to answer these questions within the practical constraints of transaction costs and money management imposed by real-world trading systems. Although a formal statement of the methodology or steps that is being used is left until section 3, it makes sense to discuss the way in which this thesis will address the above question. In this thesis, various types of ANN will be trained using fundamental data, and technical data according to the direction and value accuracy. A better trading system development methodology will be defined, and the performance of the forecasting model will be checked by using evaluation criteria rate of the return .In this way, the benefits of incorporating ANN into trading strategies in the stock market can be exposed and quantified. Once this process has been undertaken, it will be possible to answer the thesis all questions. 1.3 Motivation of the Research Stock market has always had been an attractive appeal for the researchers and financial investors and they have studied it over again to extract the useful patterns to predict the movement of the stock market. The reason is that if the researchers can make the accurate forecasting model, they can beat the market and can gain excess profit by applying the best trading strategy. Numerous financial investors have suffered lot of financial losses in the stock market as they were not aware of the stock market behavior. They had the problem that they were not able to decide when they should sell or buy the stock to gain profit. Nevertheless, finding out the best time for the investor to buy or to sell has remained a very difficult task because there are too many factors that may influence stock prices. If the investors have the accurate forecasting model, then they can predict the future behavior of the stock exchange and can gain profit. This solves the problem of the financial investors to some extent as they will not bear any financial loss. But it does not guarantee that the investor can have better profit or rate of return as compared to other investors unless he utilized the forecasting model using better trading strategy to invest money in the share market. This thesis tries to solve the above problem by providing the investor better forecasting model and trading strategies that can be applied to real-world trading systems. 1.4 Justification of Research There are several features of this academic research that distinguish it from previous academic researches. First of all, the time frame chosen for the investigation of the ANN (2002-08) in the London Stock Exchange has never been tested in the previous academic work. The importance of the period chosen is that there are two counter forces, which are opposing each other. On the one hand, the improvement of the UK and other countries economy after the 2001 financial crises happened in this period as a whole. On the other hand, this period also shows the decline in the stock markets from Jan, 2008 to Dec, 2008. So, it is important to test the forecasting model for bull, stable and bear market. Second, some of the research questions addressed in the above section, have not been investigated much in the academic studies, especially there is hardly any study which have done research on all the problems. Moreover, original hybrid based mixed neural network, better trading strategy and other modified approaches have been successfully being described and used in this study Finally, there is a significant lack of work carried out in this area in the LSE. As such, this thesis draws heavily on results published mainly within the United States and other countries; from the academics .One interesting aspect of this thesis is that it will be interesting to see how much of the published research on application of ANN in stock market anomalies is applicable to the UK market. This is important as some of the academic studies (Pan et al (2005)) states that each stock market in the globe is different. 1.5 Delimitations of scope The thesis concerns itself with historical data for the variables that affect London Stock Exchange during the period 2002 – 2008. 1.6 Outline of the Report The remaining part of the thesis is organized in the following six chapters. The second chapter, the background and literature review, provides a brief introduction to the domain and also pertinent literature is reviewed to discuss the related published work of the previous researchers in terms of their contribution and content in the prediction of the stock exchange which serves as the building block for much of the research. Moreover, this literature review also gave solid justification why a particular set of ANN inputs are selected, which is important step according to the Thawornwong and Enke (2004) and and some concepts from finance. The third chapter, the methodology, describes the steps in detail, data and the mechanics or techniques that take place in the thesis along with the empirical evidence. In addition, it also discuss the literature review for each step. Formulas and diagrams are shown to explain the techniques when necessary and it also covers issues as software and hardware used in the study. The fourth chapter, the implementation, discusses the approaches used in the implementation in detail based on the third chapter. It also covers such issues as software and hardware used in the study. The fifth chapter, the results and analysis, present the results according to the performance and benchmark measures that we have used in this study to compare with other models. It describes the choices that were needed in making model and justifies these choices in terms of the literature. The sixth chapter, conclusions and further work, restates the thesis hypothesis, discuss the conclusions drawn from the project and also thesis findings are put into perspective. Finally, the next steps to improve the model performance are considered. Chapter 2 Background and Literature Review 2 Background and Literature Review This section of thesis explores the theory of three relevant fields of the Financial Time Series, Stock Market, and Artificial Neural Networks, which together form the conceptual frameworks of the thesis as shown in the figure 1. Framework is provided to the trader to make quantitative and qualitative judgments concerning the future stock exchange movements. These three fields are reviewed in historical context, sketching out the development of those disciplines, and reviewing their academic credibility, and their application to this thesis. In the case of Neural Networks, the field is reviewed with regard to that portion of the literature which deals with applying neural network to the prediction of the stock exchange, the various type of techniques and neural networks used and an existing prediction model is extended to allow a more detailed analysis of the area than would otherwise have been possible. 2.1 Financial Time Series 2.1.1 Introduction The field of the financial time series prediction is a highly complex task due to the following reasons: 1. The financial time series frequently behaves like a random-walk process and predictability of such series is controversial issue which has been questioned in scope of EMH. 2. The statistical property of the financial time series shift with the different time. Hellstr ¨om and Holmstr ¨om [1998]). 3. Financial time series is usually noisy and the models which have been able to reduce such noise has been the better model in forecasting the value and direction of the stock exchange. 4. In the long run, a new forecasting technique becomes a part of the process to be forecasted, i.e. it influences the process to be forecasted (Hellstr ¨om and Holmstr ¨om [1998]). The first point is explained later in this section while discussing the EMH theory (Page).The graph of the volatility time series of FTSE 100 index from 14 June, 1993 to 29 December, 1998 and Dow Jones from 1928 to 2000 by Nelson Areal (2008) and Negrea Bogdan Cristian (2007) illustrates the second point of the FTSE 100 [2.1.r]in figure 2.1.1 and 2.2.2.These figures also shows that the volatility changes with period , in some periods FTSE 100 index value fluctuates so much and in some it remains calm. The third point is explained by the fact the events on a particular data affect the financial time series of the index, for example, the volatility of stocks or index increases before announcement of major stock specific news (Donders and Vorst [1996]). These events are random and contribute noise in the time series which may make difficult to compare the two forecasting models difficult to compare as a random model can also produce results. The fourth result can be explained by the example. Suppose a company develop a model or technique that can outcast all other models or techniques. The company will make lot of profits if this model is available to less people. But if this technique is available to all people with time due to its popularity, than the profits of the company will decrease as the company will not no longer take advantage of this technique. This argument is described in Hellstr ¨om and Holmstr ¨om [1998] and Swingler [1994] . 2.1.2 Efficient Market Hypothesis (EMH) EMH Theory has been a controversial issue for many years and there has been no mutual agreed deal among the academic researchers, whether it is possible to predict the stock price. The people who believe that the prices follow â€Å"random walk† trend and cannot be predicted, are usually people who support the EMH theory. Academic researchers( Tino et al. [2000]), have shown that the profit can be made by using historical information , whereas they also found difficult to verify the strong form due to lack of all private and public data. The EMH was developed in 1965 by Fama (Fama [1965], Fama [1970]) and has found widely accepted (Anthony and Biggs [1995], Malkiel [1987], White [1988], Lowe and Webb [1991]) in the academic community (Lawrence et al. [1996]).It states that the future index or stock value is completely unpredictable given the historical information of the index or stocks. There are three forms of EMH: weak, semi-strong, and strong form. The weak EMH rules out any form of forecasting based on the stock’s history, since the stock prices follows a random walk in which in which successive changes have zero correlation (Hellstr ¨om and Holmstr ¨om [1998]). In Semi Strong hypothesis, we consider all the publicly available information such as volume data and fundamental data. In strong form, we consider all the publicly and privately available information. Another reason for argument against the EMH is that different investors or traders react differently when a stock suddenly drops in a value. These different time perspectives will cause the unexpected change in the stock exchange, even if the new information has not entered in the scene. It may be possible to identify these situations and actually predict future changes (Hellstr  ¨om and Holmstr ¨om [1998]) The developer have proved it wrong by making forecasting models, this issue remains an interesting area. This controversy is just only matter of the word immediately in the definition. The studies in support of the argument of EMH rely on using the statistical tests and show that the technical indicators and tested models can’t forecast. However, the studies against the argument uses the time delay between the point when new information enters the model or system and the point when the information has spread across over the globe and a equilibrium has been reached in the stock market with a new market price. 2.1.3 Financial Time Series Forecasting Financial Time series Forecasting aims to find underlying patterns, trends and forecast future index value using using historical and current data or information. The historic values are continuous and equally spaced value over time and it represent various types of data . The main aim of the forecasting is to find an approximate mapping function between the input variables and the forecasted or output value . According to Kalekar (2004), Time series forecasting assumes that a time series is a combination of a pattern and some error. The goal of the model using time series is to separate the pattern from the error by understanding the trend of the pattern and its seasonality Several methods are used in time series forecasting like moving average (section ) moving averages, linear regression with time etc. Time series differs from the technical analysis (section) that it is based on the samples and treated the values as non-chaotic time series. Many academic researchers have applied t ime series analysis in their forecasting model, but there has been no major success. [1a] 2.2 Stock Market 2.2.1 Introduction Let us consider the basics of the stock market. MM What are stocks? Stock refers to a share in the ownership of a corporation or company. They represent a claim of the stock owner on the company’s earnings and assets and by buying more stocks; the stake in the ownership is increased. In United States, stocks are often referred as shares, whereas in the UK they are also used as synonym for bonds, shares and equities. MM Why a Company issues a stock? The main reason for issuing stock is that the company wants to raise money by selling some part of the company. A company can raise money by two ways: â€Å"debt financing† (borrowing money by issuing bonds or loan from bank) and â€Å"equity financing â€Å"(borrowing money by issuing stocks).It is advantageous to raise the money by issuing stocks as the company has not to pay money back to the stock owners but they have to share the profit in the form of the dividends. MM What is Stock Pricing or price? A stock price is the price of a single stock of a number of saleable stocks traded by the company. A company issue stock at static price, and the stock price may increase or decrease according to the trade. Normally the price of the stocks in the stock market is determined by the supply/demand equilibrium. MM What is a Stock Market? Stock Market or equity market is a public market where the trading and issuing of a company stock or derivates takes place either through the stock exchange or they may be traded privately and over-the counter markets. It is vital part of the economy as it provides opportunities to the company to raise money and also to the investors of having potential gain by selling or buying share. The stock market in the US includes the NYSE, NASDAQ, the AMEX as well as many regional exchanges. London Stock Exchange is the major stock exchange in the UK and Europe.As mentioned in the Chapter 1, in this study we forecast the London Stock Exchange (Section 2.2.2.). Investing in the stock market is very risky as the stock market is uncertain and unsteady. The main aim of the investor is to get maximum returns from the money invested in the stock market, for which he has to study about the performance, price history about the stock company .So it is a broad category and according to Hellstrom (1997), there are four main ways to predict the stock market: 1. Fundamental analysis (section 2.2.3) 2. Technical analysis, (section 2.2.4) 3. Time series forecasting (section 2.1) 4. Machine learning (ANN). (Section 2.3) 2.2.2 London Stock Exchange London Stock Exchange is one of the world’s oldest and largest stock exchanges in the world, which started its operation in 1698, when John Casting commenced â€Å"at this Office in Jonathan’s Coffee-house† a list of stock and commodity prices called â€Å"The Course of the Exchange and other things† [2] .On March 3, 1801, London Stock Exchange was officially established with current lists of over 3,200 companies and has existed, in one or more form or another for more than 300 years. In 2000, it decided to become public and listed its shares on its own stock exchange in 2001. The London Stock market consists of the Main Market and Alternative Investments Market (AIM), plus EDX London (exchange for equity derivatives). The Main Market is mainly for established companies with high performance, and AIM hand trades small-caps, or new enterprises with high growth potential.[1] Since the launch of the AIM in 1995, AIM has become the most successful growth market in the world with over 3000 companies from across the globe have joined AIM. To evaluate the London Stock Exchange, the autonomous FTSE Group (owned by the Financial Times and the London Stock Exchange) , sustains a series of indices comprising the FTSE 100 Index, FTSE 250 Index, FTSE 350 Index, FTSE All-Share, FTSE AIM-UK 50, FTSE AIM 100, FTSE AIM All-Share, FTSE SmallCap, FTSE Tech Mark 100 ,FTSE Tech Mark All-Share.[4] FTSE 100 is the most famous and composite index calculated respectively from the top 100 largest companies whose shares are listed on the London Stock Exchange. The base date for calculation of FTSE 100 index is 1984. [2] In the UK, the FTSE 100 is frequently used by large investor, financial experts and the stock brokers as a guide to stock market performance. The FTSE index is calculated from the following formula: 2.2.3 Fundamental Analysis Fundamental Analysis focuses on evaluation of the future stock exchange movements

The Folly of René Descartes’ Discourse on Method and Meditations on Fir

The Folly of Renà © Descartes’ Discourse on Method and Meditations on First Philosophy In order to embark on his quest for truth, Descartes first devises his four rules which should serve as a solid foundation for all else that he comes to understand. Those rules are here evaluated in terms of what they fail to take into consideration. The rules are examined individually and consecutively, and are therefore also reiterated in order to be clear about them. Furthermore, the approach of using these rules is also analyzed to some degree. Ultimately, however, it is my conjecture that Descartes’ four rules are not as solid a foundation as he claims, but fail to consider key issues which are noted herein. Descartes’ first rule deals with the notion of truth, and states it as follows. The first [rule] was never to accept anything as true that I did not plainly know to be such; that is to say, carefully to avoid hasty judgment and prejudice; and to include nothing more in my judgments than what presented itself to my mind so clearly and so distinctly that I had no occasion to call it in doubt. (11) In essence, we are to accept only what is true. This brings up the question of how one can even know truth. For Descartes, the certain truth is â€Å"I think, therefore I am,† which is his first principle. However, even if this is a certain truth, how can we know anything else to be true? More importantly, however, the first rule states that nothing should be accepted that can be called into doubt, or to accept only that which is indubitable. Yet how can anything be indubitable, save perhaps Descartes’ first principle, and even there some may be able to find flaws? It seems doubtful whether anything can be proven beyond any reas... ..., then there is no thing that is easier to know than another. Descartes’ use of this approach is a false foundation as he does not see these complications. The underlying frailty of such rules is that it assumes absolute truths, without exceptions. I do not know of any truths that are absolute, and do not know of anyone who does. But more importantly, this approach would be much more effective if it was an inductive, and not a deductive, method. With an inductive method Descartes could not be refuted with a single instance, and he would not need to account for all contesting situations. It seems doubtful whether an absolutely deductive method could ever exist, based on the limits of human knowledge. Works Cited Renà © Descartes. Discourse on Method and Meditations on First Philosophy. 4th edition. Trans. Donald A. Cress. Indianapolis: Hackett, 1998.

Coming-of-Age Stories with Morals: T. Coraghessan Boyles Greasy Lake a

T. Coraghessan Boyle's "Greasy Lake" and John Updike's "A & P" have many similarities as well as differences. Both are coming-of-age stories that teach some sort of lesson to the protagonist at the end. â€Å"A&P† is about a nineteen-year-old boy who stands up against his manager to impress a couple of girls who are dressed â€Å"immodestly†. â€Å"Greasy Lake† is about many nineteen years olds playing a prank on a couple of bad characters who turn out to show the teens what they can really do in return. Luckily, the narrator and Sammy both realize their deficiency after the situations with the other characters. In â€Å"A&P† the narrator’s turning point in his life is when he finds the bikers body in the lake next to him. In â€Å"Greasy Lake† the realization occurs after Sammy quits his job and tries to be the â€Å"hero† to those girls. In both stories, the protagonists’ have no idea what the real world is like, or how it works. The narrator in â€Å"Greasy Lake† does not know what bad means until his own â€Å"badness† is put to the test in the real world. From his experience, Sammy learns that he will...

Proposed Debate Topics Business Essay

1. The government should impose gender quota on the Board of Directors of every company in order to increase the percentage of female representatives. 2. Being the Chairperson of a public listed company where most of your customers are Muslim, you decide to appoint a homosexual person to become your company CEO. 3. As a doctor of a financially distressed hospital and you discover unexplained increased in patient death, you would reveal the incident to the public. 4. It is better to have an ethical and benevolent CEO even though he / she cannot perform than to have a CEO who achieves performance through unethical means. 5. The government should make producing, selling and owning cigarettes / tobaccos illegal since smoking can cause serious diseases. 6. You are the CEO of a pharmaceutical company and you agree to release an untested drug in order to contain the outbreak of a deadly and highly contagious disease. 7. As a CEO of a car manufacturer and you found minor defects in your car, you would recall your car worldwide. 8. Mercy killing is the deliberate advancement of a person’s death for the benefit of that person. Therefore the rights of mercy killing should be granted and respected. 9. Death penalty is an important measure to deter serious crimes. Hence, death penalty should be adopted to effectively reduce a nation’s crime rates. 10. Privacy is becoming progressively more porous. Often, this is done in the name of security.

Film Comparative Analysis

Film Comparative Analysis â€Å"The general response following the screening was a distinct realization that nobody is above the law, and that the stereotypes associated with the â€Å"cono† nearly left Larranaga guilty as mistakenly charged. † (Syjuco, 2012) There is no justice, when innocent men are in jail; this is the main idea that the two films have in common. With this, let us ask ourselves, â€Å"Is there really something wrong with the Philippine and Texas justice system? Are we to admit that it is a corrupt system that we have? These two films will leave our eyes wide opened to the truth or if not, to the flaws and corruptions in the justice system, not only of our own country, but also that of the others. I. Background Give Up Tomorrow The documentary film is about a Filipino-Spanish student named Paco Larranaga, who was sentenced to death in 2004 for the double murder and rape of Chiong sisters (Marijoy and Jacqueline) in 1997. This is the story of what we now know as the Chiong Murder Case, a cebu scandal of the century.Two Chiong sisters go missing on July 16, 1997. Larranaga was one, along with six other suspects who was pinpointed by the state witness, David Rusia. David Rusia is a convicted felon and was sentenced to prison twice in the United States for other crimes. As claimed by Rusia, he was with Larranaga in Ayala Center, Cebu early in the evening of July 16, that evening Larranaga says that he was at R&R Restaurant in Quezon City with his friends; such fact was proven by photographs and the testimonies of his friends.The defense presented thirty-five witnesses, including Larranaga’s teachers and classmates at the Center for Culinary Arts (CCA) in Quezon City, who all testified under oath that Larranaga was in Quezon City, when the crime is said to have taken place in Cebu. The trial court considered these testimonies irrelevant, rejecting these as coming from â€Å"friends of the accused,† and were not admitte d. The following are also evidences presented by the defense during trial — a)Larranaga, at that time was at a party at the R&R Restaurant along Katipunan Avenue, Quezon City, and stayed there until early morning the following day. )After the party, the logbook of the security guard at Larranaga's condominium indicates that Larranaga returned to his Quezon City condominium at 2:45 a. m. c)Rowena Bautista, an instructor and chef at the culinary center, said Larranaga was in school from 8 a. m. to 11:30 a. m. and saw him again at about 6:30 p. m on July 16. d)The school’s registrar, Caroline Calleja, said she proctored a two-hour exam where Larranaga was present from 1:30 p. m. Larranaga attended his second round of midterm exams on July 17 commencing at 8 a. m. Only then did Larranaga leave for Cebu in the late afternoon of July 17, 1997. )Airline and airport personnel also came to court with their flight records, indicating that Larranaga did not take any flight on Jul y 16, 1997, nor was he on board any chartered aircraft that landed in or departed from Cebu during the relevant dates, except the 5 p. m. PAL flight on July 17, 1997 from Manila to Cebu The aforementioned evidences did not prevent the conviction of Larranaga along with his six co-accused. The trial court judge, after rendering judgment against them, was found dead in a hotel in Cebu, and allegedly committed suicide.This unexpected event during the Chiong murder case was proven in the film to be part of the whole scheme of putting the blame on Larranaga, and concealing the truth of the facts with regard to the murder and rape of the Chion sisters. Larranaga, along with the other co-accused were sentenced to death, and appealed later on, but all of them were denied. Considering the Filipino-Spanish nationality of Larranaga, his family asked for help from the Spanish government. In September 2009, the Department of Justice approved Larranaga's transfer to a Spanish prison.Thelma Chiong , the mother of the victims, expressed shock over the decision, saying that, despite Larranaga's Spanish citizenship, â€Å"If you committed a crime in the Philippines, you are jailed in the Philippines,† despite the fact that this would constitute a breach of the treaty and thus of international law. Larranaga, escorted by two Spanish Interpol agents, left for Spain on October 6, 2009. His good behavior at the New Bilibid Prison was taken into consideration, and he will serve the rest of his sentence at the Madrid Central Penitentiary at Soto del Real. The Thin Blue LineThe film is an investigation into the 1976 murder of Dallas police officer Robert Wood. Harris testified that Adams had shot and killed Wood after their car had been pulled over on their way home from a movie. Adams claimed to know nothing of the murder, insisting that Harris had dropped him at his home two hours before it occurred. Local authorities believed Harris, and witnesses corroborated his story, lead ing to Adams’ conviction and a death sentence, (which was later on changed). Randall Adams recalls the events in detail: after running out of gas, he had been picked up by Harris in a stolen car.The two had gone to a movie where they drank beer and smoked marijuana, and this was the extent of their relationship. David Harris, on the other hand, also recalls the events of the evening in detail, but creates a much different impression. Adam’s defense attorneys thought that Harris was the killer, pointing to his past criminal record and other crimes committed the night of the murder. The film presents a series of interviews about the investigation and reenactments of the shooting, based on the testimony and recollections of Adams, Harris, and various witnesses and detectives. Two attorneys who epresented Adams at the trial where he was convicted of capital murder also appear: they suggest that Adams was charged with the crime despite the better evidence against Harris bec ause, as Harris was a juvenile, Adams alone of the two could be sentenced to death under Texas law. II. Similarities and Differences The two films both dealt on the fact that there is a corrupt justice system. That even an innocent man can be put into jail all for the sake of concealing the truth. This idea is very obvious in the films presented, that even a man of little knowledge with the law will doubt the guilt of both, Larranaga and Adams.The idea brought up by the filmmakers of both was a frame up or cover up which lead to the conviction of innocent people. The very controlling authorities in both were the police officers, the judge, and other executive officers of the government and to add, the media, influencing the course of the trial and the impression of the masses on the suspects. In both, police authorities were overwhelmed with the idea of having someone to put the blame for the murder of the victims. They were like heroes of the public for having solved the case and f ound a suspect. In which, it is very obvious that it was politically motivated.As one of the differences between the case of Larranaga and Adams, is that of having exhausted all administrative remedies. Larranaga, after being convicted in the trial court, appealed to the Supreme Court, but was not able to attain a favourable judgment. After such, taking into consideration of the dual citizenship of Larranaga, they asked for the help of Spanish government so that the death penalty be withdrawn and let him be transferred in Spain where he will serve his sentence. This however paved the way for the abolishment of death penalty by former Pres.Gloria Arroyo and the approval of Larranaga’s transfer. The cases of Larranaga and Adams both involved rights which were violated. As declared under the Universal Declaration of Human Rights (UDHR), the following are those evidently violated in the course of the whole trial of the case, (a) Article 11, par. 1, (b) Article 9 and (c) Article 1 0. †¢Article 11, par. 1 Everyone charged with a penal offense has the right to be presumed innocent until proved guilty according to law in a public trial at which he has had all the guarantees necessary for his defense.In light of this article which pertains to the right of an accused to be presumed innocent, Larranaga was outrightly charged as a criminal in the minds of the people, especially those of the Cebuanos, even before a trial was held. Aggravating this situation was the participation of the media from the start up to the end of the case, tagged as the â€Å"trial of the decade†. The impressions that were made by the police authorities and the media, contributed to the image of Larranaga as guilty of the crime charged.His identity was corroborated as a rich bad boy/gangster from a prominent family, in which the people presumed that they will make use of their resources to pay for witnesses and manipulate the whole case and avoid prosecution. As to the case of A dams, he was made fit to the image of a cop-killer as compared to Harris. The prosecution relied on the testimony of Harris that it was Adams who killed Wood, even before the start of the case, they knew already who to convict. The fact that Harris was a juvenile that time, made it more likely for Adams to commit the crime thus moving away from the presumption of his innocence. Article 9 No one shall be subjected to arbitrary arrest, detention or exile. The course of Larranaga’s arrest came swift and unexpected and appeared to him as kidnapping. The people who arrested him were all in civilian clothes, though they looked like policemen. They did not identify themselves when they arrested Larranaga, until they were asked by Larranaga’s sister. They unlawfully arrested Larranaga due to absence of warrant of arrest, in defense, they said that he committed a continuing crime.As to Adams’ case, he was taken into the custody of the police few days after the commission of the crime. He was forced to sign a document containing an admission that he was the one who murdered Wood. The policeman even threatened him with a pistol if he will not sign it. There is no sufficient cause for his guilt. †¢Article 10 Everyone is entitled in full equality to a fair and public hearing by an independent and impartial tribunal, in the determination of his rights and obligations and of any criminal charge against him.The right to a fair and speedy trial was not accorded to Larranaga, first, the media had participated a lot in drawing the image of Larranaga as the criminal. The judge also showed his impartiality which was really unexplainable. After having refused to accept the testimonies of witnesses of Larranaga, preventing him to take the witness stand and rendered a judgment of double life imprisonment, the judge was found in a hotel dead. Through the series of events that had transpired, the fairness and impartiality of the trial cannot be said to be pres ent.As in the case of Adams, it cannot be said to have been a fair trial for him because the prosecution presented fake witnesses, in which the conviction was based. There is a biased judgment and inconsideration on the merits of the case. Adams was not able to defend himself, such conviction of him was predicated on the failure of his defense lawyer to clearly establish his innocence albeit all the frame-ups that had transpired. References: http://www. centerforsocialmedia. org/sites/default/files/documents/pages/interview_transciption_giveuptomorrow. pdf http://en. wikipedia. org/wiki/The_Thin_Blue_Line_(film)