Technical and Fundamental Features Analysis for Stock Market Prediction with Data Mining Methods

Predicting stock prices is an essential objective in the financial world. Forecasting stock returns and their risk represents one of the most critical concerns of market decision makers. This thesis investigates the stock price forecasting with three approaches from the data mining concept and shows how different elements in the stock price can help to enhance the accuracy of our prediction. For this reason, the first and second approaches capture many fundamental indicators from the stocks and implement them as explanatory variables to do stock price classification and forecasting. In the third approach, technical features from the candlestick representation of the share prices are extracted and used to enhance the accuracy of the forecasting. In each approach, different tools and techniques from data mining and machine learning are employed to justify why the forecasting is working. Furthermore, since the idea is to evaluate the potential of features in the stock trend forecasting, therefore we diversify our experiments using both technical and fundamental features. Therefore, in the first approach, a three-stage methodology is developed while in the first step, a comprehensive investigation of all possible features which can be effective on stocks risk and return are identified. Then, in the next stage, risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, based on some filters and function-based clustering; and re-predicted the risk and return of stocks. In the second approach, instead of using single classifiers, a fusion model is proposed based on the use of multiple diverse base classifiers that operate on a common input and a meta-classifier that learns from base classifiers’ outputs to obtain a more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting, and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes are determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. Finally, in the third approach, a novel forecasting model for stock markets based on the wrapper ANFIS (Adaptive Neural Fuzzy Inference System) – ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick is presented. Two approaches of Raw-based and Signal-based are devised to extract the model’s input variables and buy and sell signals are considered as output variables. To illustrate the methodologies, for the first and second approaches, Tehran Stock Exchange (TSE) data for the period from 2002 to 2012 are applied, while for the third approach, we used General Motors and Dow Jones indexes.Predicting stock prices is an essential objective in the financial world. Forecasting stock returns and their risk represents one of the most critical concerns of market decision makers. This thesis investigates the stock price forecasting with three approaches from the data mining concept and shows how different elements in the stock price can help to enhance the accuracy of our prediction. For this reason, the first and second approaches capture many fundamental indicators from the stocks and implement them as explanatory variables to do stock price classification and forecasting. In the third approach, technical features from the candlestick representation of the share prices are extracted and used to enhance the accuracy of the forecasting. In each approach, different tools and techniques from data mining and machine learning are employed to justify why the forecasting is working. Furthermore, since the idea is to evaluate the potential of features in the stock trend forecasting, therefore we diversify our experiments using both technical and fundamental features. Therefore, in the first approach, a three-stage methodology is developed while in the first step, a comprehensive investigation of all possible features which can be effective on stocks risk and return are identified. Then, in the next stage, risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, based on some filters and function-based clustering; and re-predicted the risk and return of stocks. In the second approach, instead of using single classifiers, a fusion model is proposed based on the use of multiple diverse base classifiers that operate on a common input and a meta-classifier that learns from base classifiers’ outputs to obtain a more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting, and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes are determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. Finally, in the third approach, a novel forecasting model for stock markets based on the wrapper ANFIS (Adaptive Neural Fuzzy Inference System) – ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick is presented. Two approaches of Raw-based and Signal-based are devised to extract the model’s input variables and buy and sell signals are considered as output variables. To illustrate the methodologies, for the first and second approaches, Tehran Stock Exchange (TSE) data for the period from 2002 to 2012 are applied, while for the third approach, we used General Motors and Dow Jones indexes.154 - Katedra financívyhově

Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

publishedVersio

An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods

An electronic financial system adviser for investors: the case of Saudi Arabia

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonFinancial markets, particularly capital and stock markets, play an important role in mobilizing and canalising the idle savings of individuals and institutions to the investment options where they are really required for productive purposes. The prediction of stock prices and returns is carried out in order to enhance the quality of investment decisions in stock markets, but it is considered to be tricky and complicates tasks as these prices behave in a random fashion and vary with time. Owing to the potential of returns and inherent risk factors in stock market returns. Various stock market prediction models and decision support systems such as Capital asset pricing model, the arbitrage pricing theory of Ross, the inter-temporal capital asset pricing model of Merton ,Fama and French five-factor model, and zero beta model to provide investors with an optimal forecast of stock prices and returns. In this research thesis, a stock market prediction model consisting of two parts is presented and discussed. The first is the three factors of the Fama and French model (FF) at the micro level to forecast the return of the portfolios on the Saudi Arabian Stock Exchange (SASE) and the second is a Value Based Management (VBM) model of decision-making. The latter is based on the expectations of shareholders and portfolio investors about taking investment decisions, and on the behaviour of stock prices using an accurate modern nonlinear technique in forecasting, known as Artificial Neural Networks (ANN). This study examined monthly data relating to common stocks from the listed companies of the Saudi Arabian Stock Exchange from January 2007 to December 2011. The stock returns were predicted using the linear form of asset pricing models (capital asset pricing model as well as Fama and French three factor model). In addition, non-linear models were also estimated by using various artificial neural network techniques, and adaptive neural fuzzy inference systems. Six portfolios of stock predictors are combined using: average, weighted average, and genetic algorithm optimized weighted average. Moreover, value-based management models were applied to the investment decision-making process in combination with stock prediction model results for both the shareholders’ perspective and the share prices’ perspective. The results from this study indicate that the ANN technique can be used to predict stock portfolio returns; the investment decisions and the behaviour of stock prices, optimized by the genetic algorithm weighted average, provided better results in terms of error and prediction accuracy compared to the simple linear form of stock price prediction models. The Fama and French model of stock prediction is better suited to Saudi Arabian Stock Exchange investment activities in comparison to the conventional capital assets pricing model. Moreover, the multi-stage type1 model, which is a combination of Fama and French predicted stock returns and a value-based management model, gives more accurate results for the stock market decision-making process for investment or divestment decisions, as well as for observing variation in and the behaviour of stock prices on the Saudi stock market. Furthermore, the study also designed a graphic user interface in order to simplify the decision-making process based upon Fama and French and value-based management, which might help Saudi investors to make investment decisions quickly and with greater precision. Finally, the study also gives some practical implications for investors and regulators, along with proposing future research in this area

Data mining in computational finance

Computational finance is a relatively new discipline whose birth can be traced back to early 1950s. Its major objective is to develop and study practical models focusing on techniques that apply directly to financial analyses. The large number of decisions and computationally intensive problems involved in this discipline make data mining and machine learning models an integral part to improve, automate, and expand the current processes. One of the objectives of this research is to present a state-of-the-art of the data mining and machine learning techniques applied in the core areas of computational finance. Next, detailed analysis of public and private finance datasets is performed in an attempt to find interesting facts from data and draw conclusions regarding the usefulness of features within the datasets. Credit risk evaluation is one of the crucial modern concerns in this field. Credit scoring is essentially a classification problem where models are built using the information about past applicants to categorise new applicants as ‘creditworthy’ or ‘non-creditworthy’. We appraise the performance of a few classical machine learning algorithms for the problem of credit scoring. Typically, credit scoring databases are large and characterised by redundant and irrelevant features, making the classification task more computationally-demanding. Feature selection is the process of selecting an optimal subset of relevant features. We propose an improved information-gain directed wrapper feature selection method using genetic algorithms and successfully evaluate its effectiveness against baseline and generic wrapper methods using three benchmark datasets. One of the tasks of financial analysts is to estimate a company’s worth. In the last piece of work, this study predicts the growth rate for earnings of companies using three machine learning techniques. We employed the technique of lagged features, which allowed varying amounts of recent history to be brought into the prediction task, and transformed the time series forecasting problem into a supervised learning problem. This work was applied on a private time series dataset

Machine learning techniques for predicting the stock market using daily market variables

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligencePredicting the stock market was never seen as an easy task. The complexity of the financial systems makes it extremely difficult for anything or anyone to predict what the future of prices holds, let it be a day, a week, a month or even a year. Many variables influence the market’s volatility and some of these may even be the gut feeling of an investor on a specific day. Several machine learning techniques were already applied to forecast multiple stock market indexes, some presenting good values of accuracy when it comes to predict whether the prices will go up or down, and low values of error when dealing with regression data. This work aims to apply some state-of-the-art algorithms and compare their performance with Long Short-term Memory (LSTM) as well as between each other. The variables used to this empirical work were the prices of the Dow Jones Industrial Average (DJIA) registered for every business day, from January 1st of 2006 to January 1st of 2018, for 29 companies. Some changes and adjustments were made to the original variables to present different data types to the algorithms. To ensure good quality and certainty when evaluating the flexibility and stability of each model, the error measure used was the Root Mean Squared Error and the Mann-Whitney U test was also applied to assess statistical significance of the results obtained.Prever a bolsa nunca foi considerado ser uma tarefa fácil. A complexidade dos sistemas financeiros torna extremamente difícil que um ser humano ou uma máquina consigam prever o que o futuro dos preços reserva, seja para um dia, uma semana, um mês ou um ano. Muitas variáveis influenciam a volatilidade do mercado e algumas podem até ser a confiança de um investidor em apostar em determinada empresa, naquele dia específico. Várias técnicas de aprendizagem automática foram aplicadas ao longo do tempo para prever vários índices de bolsas, algumas apresentando bons valores de precisão quando se tratou de prever se os preços subiam ou desciam e outras, baixos valores de erro ao lidar com dados de regressão. Este trabalho tem como objetivo aplicar alguns dos mais conhecidos algoritmos e comparar os seus desempenhos com o Long Short-Term Memory (LSTM), e entre si. As variáveis utilizadas para a elaboração deste trabalho empírico foram os preços da Dow Jones Industrial Average (DJIA) registados para todos os dias úteis, de 1 de Janeiro de 2006 a 1 de Janeiro de 2018, para 29 empresas. Algumas alterações e ajustes foram efetuados sobre as variáveis originais de forma a construír diferentes tipos de dados para posteriormente dar aos algoritmos. Para garantir boa qualidade e veracidade ao avaliar a flexibilidade e estabilidade de cada modelo, a medida de erro utilizada foi o erro médio quadrático da raíz e, de seguida, o teste U de Mann-Whitney foi aplicado para avaliar a significância estatística dos resultados obtidos