Exploiting stock data: a survey of state of the art computational techniques aimed at producing beliefs regarding investment portfolios

Selecting an investment portfolio has inspired several models aimed at optimising the set of securities which an investtor may select according to a number of specific decision criteria such as risk, expected return and planning horizon. The classical approach has been developed for supporting the two stages of portfolio selection and is supported by disciplines such as econometrics, technical analysis and corporative finance. However, with the emerging field of computational finance, new and interesting techniques have arisen in line with the need for the automatic processing of vast volumes of information. This paper surveys such new techniques which belong to the body of knowledge concerning computing and systems engineering, focusing on techniques particularly aimed at producing beliefs regarding investment portfolios.

[1]  Fl Chung,et al.  Financial time series indexing based on low resolution clustering , 2004 .

[2]  J. Murphy.  Technical Analysis of the Financial Markets , 1999 .

[3]  M. Kaboudan.  Genetic Programming Prediction of Stock Prices , 2000 .

[4]  S. Ross,et al.  Economic Forces and the Stock Market , 1986 .

[5]  David Heckerman,et al.  Bayesian Networks for Data Mining , 2004, Data Mining and Knowledge Discovery.

[6]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[7]  Harry M. Markowitz,et al.  The Early History of Portfolio Theory: 1600–1960 , 1999 .

[8]  G. Edwards,et al.  The Theory of Investment Value. , 1939 .

[9]  T. Hellström,et al.  Predicting the Stock Market , 1998 .

[10]  Silvano Cincotti,et al.  Clustering of financial time series with application to index and enhanced index tracking portfolio , 2005 .

[11]  Philippe Artzner,et al.  Coherent Measures of Risk , 1999 .

[12]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

[13]  Chi-Chung Lam,et al.  FINANCIAL TIME SERIES FORECASTING BY NEURAL NETWORK USING CONJUGATE GRADIENT LEARNING ALGORITHM AND MULTIPLE LINEAR REGRESSION WEIGHT INITIALIZATION , 2000 .

[14]  Magnus Lie Hetland,et al.  Evolutionary Rule Mining in Time Series Databases , 2005, Machine Learning.

[15]  Yi Zhang,et al.  Entropy-based subspace clustering for mining numerical data , 1999, KDD '99.

[16]  Han Tong Loh,et al.  Applying rough sets to market timing decisions , 2004, Decis. Support Syst..

[17]  Michael P. Clements,et al.  Forecasting economic and financial time-series with non-linear models , 2004 .

[18]  Michel Verleysen,et al.  Non-linear financial time series forecasting application to the Bel 20 stock market index , 2000 .

[19]  David N. Nawrocki.  A Brief History of Downside Risk Measures , 1999 .

[20]  Tina Hviid Rydberg.  Realistic Statistical Modelling of Financial Data , 2000 .

[21]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[22]  Ran El-Yaniv,et al.  Competitive solutions for online financial problems , 1998, CSUR.

[23]  Shouyang Wang,et al.  Criteria, Models and Strategies in Portfolio Selection , 2002 .

[24]  Thomas Hellström,et al.  Predictable Patterns in Stock Returns , 1998 .

[25]  W. Sharpe.  CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK* , 1964 .

[26]  Enrico G. De Giorgi,et al.  A Note on Portfolio Selections under Various Risk Measures , 2002 .

[27]  C. Tan,et al.  NEURAL NETWORKS FOR TECHNICAL ANALYSIS: A STUDY ON KLCI , 1999 .

[28]  Ah Chung Tsoi,et al.  Rule inference for financial prediction using recurrent neural networks , 1997, Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr).

[29]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[30]  S. Ekern.  Taxation, Political Risk and Portfolio Selection , 1971 .

[31]  Dick van Dijk,et al.  Stock Selection Strategies in Emerging Markets , 2003 .

[32]  G. Darbellay,et al.  The entropy as a tool for analysing statistical dependences in financial time series , 2000 .

[33]  Pierre Michaud,et al.  Clustering techniques , 1997, Future Gener. Comput. Syst..

[34]  Rabah Amir,et al.  Market Selection and Survival of Investment Strategies , 2001 .

[35]  Vassilis S. Kodogiannis,et al.  Forecasting Financial Time Series using Neural Network and Fuzzy System-based Techniques , 2002, Neural Computing & Applications.

[36]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[37]  Fabrizio Lillo,et al.  CORRELATION BASED HIERARCHICAL CLUSTERING IN FINANCIAL TIME SERIES , 2005 .

[38]  P. P. Shenoy,et al.  Bayesian Network Models of Portfolio Risk and Return , 2000 .

[39]  James M. Hutchinson,et al.  A radial basis function approach to financial time series analysis , 1993 .

[40]  Francis Eng Hock Tay,et al.  Financial Forecasting Using Support Vector Machines , 2001, Neural Computing & Applications.