Machine Learning Stock Market Prediction Studies: Review and Research Directions

Stock market investment strategies are complex and rely on an evaluation of vast amounts of data. In recent years, machine learning techniques have increasingly been examined to assess whether they can improve market forecasting when compared with traditional approaches. The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature. A systematic literature review methodology is used to identify relevant peer-reviewed journal articles from the past twenty years and categorize studies that have similar methods and contexts. Four categories emerge: artificial neural network studies, support vector machine studies, studies using genetic algorithms combined with other techniques, and studies using hybrid or other artificial intelligence approaches. Studies in each category are reviewed to identify common findings, unique findings, limitations, and areas that need further investigation. The final section provides overall conclusions and directions for future research

Redes neurais, lógica nebulosa e algoritmos genéticos: aplicações e possibilidades em finanças e contabilidade

There are problems in Finance and Accounting that can not be easily solved by means of traditional techniques (e.g. bankruptcy prediction and strategies for investing in common stock). In these situations, it is possible to use methods of Artificial Intelligence. This paper analyzes empirical works published in international journals between 2000 and 2007 that present studies about the application of Neural Networks, Fuzzy Logic and Genetic Algorithms to problems in Finance and Accounting. The objective is to identify and quantify the relationships established between the available techniques and the problems studied by the researchers. Analyzing 258 papers, it was noticed that the most used technique is the Artificial Neural Network. The most researched applications are from the field of Finance, especially those related to stock exchanges (forecasting of common stock and indices prices).Existem problemas em Finanças e Contabilidade que não podem ser resolvidos facilmente através de técnicas tradicionais - por exemplo, previsão de falências e estratégias para negociação em bolsas de valores. Nestes casos, uma das alternativas é o uso de métodos de inteligência computacional. Este artigo analisa pesquisas empíricas publicadas em periódicos internacionais entre 2000 e 2007 que apresentam estudos sobre a aplicação de redes neurais, lógica nebulosa e algoritmos genéticos a problemas da área de Finanças e Contabilidade. O objetivo é identificar e quantificar as relações estabelecidas entre as tecnologias disponíveis e os problemas estudados pelos pesquisadores. Analisando-se 258 artigos, percebeu-se que a técnica mais utilizada é a rede neural artificial. As aplicações mais pesquisadas são de Finanças, especialmente aquelas relacionadas a bolsas de valores (previsão de cotações de ações e de índices)

The Application of Rough Set Theory in Stock Price Forecasting (Case Study: Iran Saderat Bank)

This paper proposes a method based on rough set theory and by using technical indicators to predict the stock price. Rough set theory has several advantages; the most important advantage is that no additional information is needed about the initial data. In the proposed model, a number of technical indicators from the data of Bank Saderat Iran during a year were calculated and used as condition attributes in the decision table and the stock price fluctuation on the next day was selected as decision attribute. It should be noted that by using the correlation matrix analysis, the variables with the highest correlation with decision attribute were selected as conditional attributes. Using rough set theory and different discretization and reduction methods, some rules are extracted based on learning data and methods validity were computed based on control data. Comparing the return of this method and buy and hold method reveals the superiority of proposed model. Also, using data from different years with different price trends as inputs to the model and achieving satisfactory results is a promising reason for using and developing this method in stock price forecasting

The Critical Deciding Factors of Auditor Choice Decision：An Application of the Decision Tree Technique

[[abstract]]"In this paper, we apply the multinomial logit regression and decision tree approaches to examine the critical deciding factors that affect auditor choice decisions. We first examine the relationship between the auditor choice and firm specific factors. In order to get deeper insights about the association between auditor choice and firm's characteristics, this paper employs the decision tree approach, a data mining technique, to explore the priority of variables affecting audit choices. The decision tree approach is an innovative tool that can help researchers choose between alternative options and investigate the possible outcomes of choosing options. To the best of our knowledge, this study is one of few studies that use the decision tree techniques to investigate the sequential deciding factors of audit choice decision. By applying both the regression and CART approaches, we find that auditor choice is affected first by agency conflict, and then by operating efficiency consideration, funding needs, and operating performance consideration sequentially. 本研究利用多元選擇迴歸模型及決策樹技術，從代理問題、股權結構、營運績效、風險等構面，探討影響我國企業會計師選擇決策之關鍵決定因素。本研究不僅探討企業會計師選擇決策之關鍵決定因素，亦研究各決定因素在會計師選擇決策過程中的優先順序。由多元選擇迴歸的實證結果可知，我國企業之會計師選擇決策之關鍵決定因素主要包括現金流量權、最終股東控制權、負債資產比率、資產報酬率、資產周轉率、子公司家數、監督股東及負債新增比率有顯著關連。決策樹的實證結果則顯示影響企業會計師選擇五個關鍵決定因素之先後順序分別為負債資產比率、資產周轉率、負債新增比率、資產報酬率及是監督股東。本文的最主要貢獻之一是不但找出企業會計師選擇決策之關鍵決定因素，更進一步透過決策樹分析找出上述因素之先後順序。本文之研究結果使會計師了解企業選擇會計師決策以進一步研擬對策，對擴展會計師業務有參考價值。

Aplicación de la inteligencia artificial en las inversiones financieras

La aplicación de la Inteligencia Artificial (IA) en la inversión financiera es un área de investigación que ha atraído una gran atención desde los años 90, cuando se produjo un acelerado desarrollo tecnológico. Desde entonces, se han propuesto innumerables enfoques para tratar el problema de la predicción de precios en el mercado de valores, como por ejemplo la optimización de carteras, predicción bursátil mediante IA, análisis de sentimientos financieros y combinaciones que implican dos o más enfoques. Un analista de IA construido para digerir la información financiera de las empresas, la divulgación cualitativa y los indicadores macroeconómicos es capaz de batir a la mayoría de los analistas humanos en las previsiones del precio de las acciones y generar un exceso de rentabilidad en comparación con el seguimiento de los analistas humanos.Grado en Finanzas, Banca y Seguro

Fusion features ensembling models using Siamese convolutional neural network for kinship verification

Family is one of the most important entities in the community. Mining the genetic information through facial images is increasingly being utilized in wide range of real-world applications to facilitate family members tracing and kinship analysis to become remarkably easy, inexpensive, and fast as compared to the procedure of profiling Deoxyribonucleic acid (DNA). However, the opportunities of building reliable models for kinship recognition are still suffering from the insufficient determination of the familial features, unstable reference cues of kinship, and the genetic influence factors of family features. This research proposes enhanced methods for extracting and selecting the effective familial features that could provide evidences of kinship leading to improve the kinship verification accuracy through visual facial images. First, the Convolutional Neural Network based on Optimized Local Raw Pixels Similarity Representation (OLRPSR) method is developed to improve the accuracy performance by generating a new matrix representation in order to remove irrelevant information. Second, the Siamese Convolutional Neural Network and Fusion of the Best Overlapping Blocks (SCNN-FBOB) is proposed to track and identify the most informative kinship clues features in order to achieve higher accuracy. Third, the Siamese Convolutional Neural Network and Ensembling Models Based on Selecting Best Combination (SCNN-EMSBC) is introduced to overcome the weak performance of the individual image and classifier. To evaluate the performance of the proposed methods, series of experiments are conducted using two popular benchmarking kinship databases; the KinFaceW-I and KinFaceW-II which then are benchmarked against the state-of-art algorithms found in the literature. It is indicated that SCNN-EMSBC method achieves promising results with the average accuracy of 92.42% and 94.80% on KinFaceW-I and KinFaceW-II, respectively. These results significantly improve the kinship verification performance and has outperformed the state-of-art algorithms for visual image-based kinship verification

Previsão da direção de movimento de índices de ações usando um sistema Fuzzy

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Sócio-Econômico. Programa de Pós-Graduação em AdministraçãoEste trabalho apresenta um novo modelo para previsão da direção de índices de mercados usando um sistema fuzzy. O modelo proposto foi aplicado na previsão do IBOVESPA e do S&P 500. Foram utilizados quatro períodos de estudo: (P1) de 8 de janeiro de 1997 até 2 de fevereiro de 2005, utilizado para previsão do IBOVESPA e S&P 500; (P2) de 8 de janeiro de 1986 até 29 de dezembro de 2005, utilizado para a previsão do IBOVESPA; (P3) de 8 de janeiro de 1970 até 30 de dezembro de 2005, utilizado para a previsão do S&P 500; e (P4) de 8 de janeiro de 1993 até 29 de dezembro de 2005, utilizado para a previsão do IBOVESPA Futuro. Ao todo, foram utilizadas 1824 regras de inferência. Embora o modelo produza uma saída lingüística, foi possível delinear uma estratégia de investimento estatisticamente significante, que superou a rentabilidade da estratégia passiva na maioria dos períodos de teste. Somente não superou a estratégia passiva quando aplicado ao IBOVESPA no período P2 (de 1986 até 2005). Esse período englobou uma época de grande inflação no Brasil, isso pode indicar que talvez a estratégia não seja aplicável em situações de hiperinflação. Para o período P1, verificou-se que a estratégia fuzzy foi estatisticamente superior à estratégia passiva para o IBOVESPA (S&P 500) com um nível de significância de 10% (20%). Isso pode significar que é mais difícil obter ganhos anormais no mercado americano, teoricamente mais eficiente que o mercado brasileiro. Também para o P1 o rendimento da estratégia usando a lógica fuzzy foi estatisticamente superior em relação a uma estratégia usando a lógica clássica com um nível de significância de 10%, tanto para o IBOVESPA quanto para o S&P 500. É importante ressaltar que o presente modelo não pretende refutar outros modelos paramétricos ou não paramétricos, mas propor uma nova solução, baseada nos conceitos da lógica fuzzy. Além disso, o modelo proposto, com sua saída probabilística, pode ser utilizado como suporte à decisão, tendo em vista que o investidor pode possuir outras informações, confidenciais ou não, assim como pode ter até intuições a respeito de tendências políticas ou econômicas