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

Non Linear Modelling of Financial Data Using Topologically Evolved Neural Network Committees

Most of artificial neural network modelling methods are difficult to use as maximising or minimising an objective function in a non-linear context involves complex optimisation algorithms. Problems related to the efficiency of these algorithms are often mixed with the difficulty of the a priori estimation of a network's fixed topology for a specific problem making it even harder to appreciate the real power of neural networks. In this thesis, we propose a method that overcomes these issues by using genetic algorithms to optimise a network's weights and topology, simultaneously. The proposed method searches for virtually any kind of network whether it is a simple feed forward, recurrent, or even an adaptive network. When the data is high dimensional, modelling its often sophisticated behaviour is a very complex task that requires the optimisation of thousands of parameters. To enable optimisation techniques to overpass their limitations or failure, practitioners use methods to reduce the dimensionality of the data space. However, some of these methods are forced to make unrealistic assumptions when applied to non-linear data while others are very complex and require a priori knowledge of the intrinsic dimension of the system which is usually unknown and very difficult to estimate. The proposed method is non-linear and reduces the dimensionality of the input space without any information on the system's intrinsic dimension. This is achieved by first searching in a low dimensional space of simple networks, and gradually making them more complex as the search progresses by elaborating on existing solutions. The high dimensional space of the final solution is only encountered at the very end of the search. This increases the system's efficiency by guaranteeing that the network becomes no more complex than necessary. The modelling performance of the system is further improved by searching not only for one network as the ideal solution to a specific problem, but a combination of networks. These committces of networks are formed by combining a diverse selection of network species from a population of networks derived by the proposed method. This approach automatically exploits the strengths and weaknesses of each member of the committee while avoiding having all members giving the same bad judgements at the same time. In this thesis, the proposed method is used in the context of non-linear modelling of high-dimensional financial data. Experimental results are'encouraging as both robustness and complexity are concerned.Imperial Users onl

Impact of internal corporate social responsibility factors on the employee’s innovation climate in the medical diagnostics industry

This study examined the relationship between employee-driven corporate social responsibility (CSR) factors and employee innovation in U.S. medical diagnostic companies during the respiratory syndrome coronavirus (COVID) pandemic. This study examined what employee-driven CSR factors affect such motivation of employees toward innovation. The research population was employees who have worked in operation, quality control, research, technical, and management departments of medical diagnostics companies in the United States of America. The investigator used a survey questionnaire for this correlation design study. Employees’ responses were analyzed based on education level, gender, and job function using descriptive analysis, t-test, and ANOVA-test. The theoretical framework consisted of the theory of corporate social responsibility and the expectancy theory of motivation. The study questions focused on nine predictors of employee-driven CSR, including employees’ rewards and recognition, empowerment, resources, engagement, and decision-making involvement, horizontal communication, vertical communication, employee job satisfaction, employee training, and leadership relationships as dependent variables and their impact on employee innovation climate as independent variables. Correlation and multiple regressions were conducted to determine the underlying relationship of the variables. The result indicated a significant relationship between employee-driven CSR and employee innovation. In addition, the study revealed that nine employee-driven CSR factors explained about 50% of employee innovation as predictor variables. Job satisfaction had the most significant impact on employee innovation climate, followed by Horizontal communication. In conclusion, this study recognized job satisfaction as the most critical employee motivational factor to innovate through quantitative research, which was also a characteristic of employee-driven CSR. The value of employee-driven CSR factors’ influence on innovation can contribute to both theory and practice. This research may highlight how medical diagnostics business leaders foster innovation through employee-driven CSR

Emergent Forms of IT Governance to Support Global eBusiness Models

A critical aspect of global e-business information technology (IT) governance is ensuring that it is integrated and that it generates economic viability of a company. Poorly thought through purposes will result in poor IT governance, the aim is to improve IT governance and business efficiency and effectiveness. A normative framework for global e-business IT governance is developed in this paper drawing on research evidence from information systems development and organization study. It proposes fundamental re-directions in global e-business IT governance thinking and it applies to companies that seek to integrate Internet, Intranet and Web technologies into their business activities in some form of an e-business model. Such integration is termed the fusion of IT and business to develop an e-business. The framework explains and elaborates ebusiness strategies for coping with emergent organizations and planned aspects of IT. The basic premise of the proposed framework is that organization, especially virtual organization, is both planned and emergent, diverging from the dominant premise of central control in IT governance

Employee Compensation: Research and Practice

[Excerpt] An organization has the potential to remain viable only so long as its members choose to participate and engage in necessary role behaviors (March & Simon, 1958; Katz & Kahn, 1966). To elicit these contributions, an organization must provide inducements that are of value to its members. This exchange or transaction process is at the core of the employment relationship and can be viewed as a type of contract, explicit or implicit, that imposes reciprocal obligations on the parties (Barnard, 1936; Simon, 1951; Williamson, 1975; Rousseau, 1990). At the heart of that exchange are decisions by employers and employees regarding compensation

Success factors in mergers and acquisitions : complexity theory and content analysis perspectives

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Aerospace management techniques: Commercial and governmental applications

A guidebook for managers and administrators is presented as a source of useful information on new management methods in business, industry, and government. The major topics discussed include: actual and potential applications of aerospace management techniques to commercial and governmental organizations; aerospace management techniques and their use within the aerospace sector; and the aerospace sector's application of innovative management techniques

ME-EM 2018-19 Annual Report

Table of Contents Faculty Research Enrollment & Degrees Department News Graduates Faculty & Staff Alumni Donors Contracts & Grants Patents & Publicationshttps://digitalcommons.mtu.edu/mechanical-annualreports/1000/thumbnail.jp