Bridging the divide in financial market forecasting: machine learners vs. financial economists

An extensive benchmark in financial time series forecasting is performed.Best machine learning(ML) methods out-perform best econometric methods.The ML methodology employed significantly affects forecasting accuracy.Market maturity, forecast horizon & model-assessment method affect forecast accuracy.Evidence against the informational value of technical indicators. Financial time series forecasting is a popular application of machine learning methods. Previous studies report that advanced forecasting methods predict price changes in financial markets with high accuracy and that profit can be made trading on these predictions. However, financial economists point to the informational efficiency of financial markets, which questions price predictability and opportunities for profitable trading. The objective of the paper is to resolve this contradiction. To this end, we undertake an extensive forecasting simulation, based on data from thirty-four financial indices over six years. These simulations confirm that the best machine learning methods produce more accurate forecasts than the best econometric methods. We also examine the methodological factors that impact the predictive accuracy of machine learning forecasting experiments. The results suggest that the predictability of a financial market and the feasibility of profitable model-based trading are significantly influenced by the maturity of the market, the forecasting method employed, the horizon for which it generates predictions and the methodology used to assess the model and simulate model-based trading. We also find evidence against the informational value of indicators from the field of technical analysis. Overall, we confirm that advanced forecasting methods can be used to predict price changes in some financial markets and we discuss whether these results question the prevailing view in the financial economics literature that financial markets are efficient.

[1]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[2]  J. Suykens,et al.  Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research , 2015, Eur. J. Oper. Res..

[3]  Chia-Hui Ho,et al.  An Improved Support Vector Regression Modeling for Taiwan Stock Exchange Market Weighted Index Forecasting , 2005, 2005 International Conference on Neural Networks and Brain.

[4]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[5]  E. Fama EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[6]  Marc-André Mittermayer,et al.  Forecasting Intraday stock price trends with text mining techniques , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[7]  S. B. Thompson,et al.  Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? , 2008 .

[8]  K. French Stock returns and the weekend effect , 1980 .

[9]  E. Fama,et al.  Size, Value, and Momentum in International Stock Returns , 2011 .

[10]  Yevgeniy V. Bodyanskiy,et al.  Neural network approach to forecasting of quasiperiodic financial time series , 2006, Eur. J. Oper. Res..

[11]  Ning Zhu,et al.  Up Close and Personal: Investor Sophistication and the Disposition Effect , 2006, Manag. Sci..

[12]  Chih-Fong Tsai,et al.  Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches , 2010, Decis. Support Syst..

[13]  Ping-Feng Pai,et al.  A hybrid ARIMA and support vector machines model in stock price forecasting , 2005 .

[14]  Mark Grinblatt,et al.  What Makes Investors Trade? , 2000 .

[15]  Xin Li,et al.  Application of Neural Networks in Financial Data Mining , 2007, International Conference on Computational Intelligence.

[16]  Baikunth Nath,et al.  A fusion model of HMM, ANN and GA for stock market forecasting , 2007, Expert Syst. Appl..

[17]  Ammar Belatreche,et al.  Evaluating machine learning classification for financial trading: An empirical approach , 2016, Expert Syst. Appl..

[18]  Sheng-Tzong Cheng,et al.  Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices , 2008 .

[19]  B. Mandelbrot When Can Price Be Arbitraged Efficiently? A Limit to the Validity of the Random Walk and Martingale Models , 1971 .

[20]  John Yearwood,et al.  Predicting Australian Stock Market Index Using Neural Networks Exploiting Dynamical Swings and Intermarket Influences , 2003, J. Res. Pract. Inf. Technol..

[21]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[22]  Chenn-Jung Huang,et al.  Application of wrapper approach and composite classifier to the stock trend prediction , 2008, Expert Syst. Appl..

[23]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[24]  Arash Ghanbari,et al.  Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting , 2010, Knowl. Based Syst..

[25]  Luis E. Zárate,et al.  Applying Artificial Neural Networks to prediction of stock price and improvement of the directional prediction index - Case study of PETR4, Petrobras, Brazil , 2013, Expert Syst. Appl..

[26]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[27]  R. Thaler,et al.  Does the Stock Market Overreact , 1985 .

[28]  D. Karemera,et al.  Random Walks and Market Efficiency Tests of Latin American Emerging Equity Markets: A Revisit , 1999 .

[29]  Soushan Wu,et al.  Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets , 2006 .

[30]  Phichhang Ou,et al.  Prediction of Stock Market Index Movement by Ten Data Mining Techniques , 2009 .

[31]  Valentina Corradi,et al.  Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries , 2005 .

[32]  Hsinchun Chen,et al.  Textual analysis of stock market prediction using breaking financial news: The AZFin text system , 2009, TOIS.

[33]  B. Malkiel The Efficient Market Hypothesis and Its Critics , 2003 .

[34]  Trevor Cohn,et al.  Day trading profit maximization with multi-task learning and technical analysis , 2014, Machine Learning.

[35]  G. Constantinides The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence: Discussion , 1985 .

[36]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

[37]  Sahil Shah,et al.  Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques , 2015, Expert Syst. Appl..

[38]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[39]  Muzafar Shah Habibullah,et al.  Private capital flows, stock market and economic growth in developed and developing countries: A comparative analysis , 2010 .

[40]  Ömer Kaan Baykan,et al.  Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange , 2011, Expert Syst. Appl..

[41]  Pei-Chann Chang,et al.  A neural network with a case based dynamic window for stock trading prediction , 2009, Expert Syst. Appl..

[42]  Clarence N. W. Tan,et al.  Evaluating the Application of Neural Networks and Fundamental Analysis in the Australian Stockmarket , 2005, Computational Intelligence.

[43]  E. Fama,et al.  Efficient Capital Markets : II , 2007 .

[44]  C. H. Chen,et al.  An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network , 2001, Fuzzy Sets Syst..

[45]  E. Fama,et al.  Common risk factors in the returns on stocks and bonds , 1993 .

[46]  Stefan Lessmann,et al.  Towards a methodology for measuring the true degree of efficiency in a speculative market , 2011, J. Oper. Res. Soc..

[47]  Khaled Rasheed,et al.  Stock market prediction with multiple classifiers , 2007, Applied Intelligence.

[48]  E. Fama Market Efficiency, Long-Term Returns, and Behavioral Finance , 1997 .

[49]  Ming-Chi Lee,et al.  Using support vector machine with a hybrid feature selection method to the stock trend prediction , 2009, Expert Syst. Appl..

[50]  Donald B. Keim SIZE-RELATED ANOMALIES AND STOCK RETURN SEASONALITY Further Empirical Evidence , 1983 .

[51]  David J. Hand,et al.  Classifier Technology and the Illusion of Progress , 2006, math/0606441.

[52]  Shouyang Wang,et al.  Forecasting stock market movement direction with support vector machine , 2005, Comput. Oper. Res..

[53]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[54]  Zeng Zhen-yu The Empirical Analysis of Long-term Memory in Stock Market , 2003 .

[55]  Ying-Wong Cheung,et al.  A search for long memory in international stock market returns , 1995 .

[56]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[57]  Ajith Abraham,et al.  Real stock trading using soft computing models , 2005, International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II.

[58]  Shuxiang Xu,et al.  A novel approach for determining the optimal number of hidden layer neurons for FNN’s and its application in data mining , 2008 .

[59]  A. Lo Long-Term Memory in Stock Market Prices , 1989 .

[60]  Michel Ballings,et al.  Evaluating multiple classifiers for stock price direction prediction , 2015, Expert Syst. Appl..

[61]  An-Sing Chen,et al.  Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index , 2001, Comput. Oper. Res..

[62]  A. Menkveld High frequency trading and the new market makers , 2013 .

[63]  Stijn Claessens,et al.  Stock market development and internationalization: Do economic fundamentals spur both similarly? , 2006 .

[64]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

[65]  B. LeBaron,et al.  Simple Technical Trading Rules and the Stochastic Properties of Stock Returns , 1992 .

[66]  Yudong Zhang,et al.  Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network , 2009, Expert Syst. Appl..

[67]  Kimon P. Valavanis,et al.  Surveying stock market forecasting techniques - Part II: Soft computing methods , 2009, Expert Syst. Appl..

[68]  Christoph Lattemann,et al.  High-Frequency Trading , 2011 .

[69]  Olivier Darné,et al.  Small Sample Properties of Alternative Tests for Martingale Difference Hypothesis , 2011 .

[70]  Michele Marchesi,et al.  A hybrid genetic-neural architecture for stock indexes forecasting , 2005, Inf. Sci..

[71]  Jae H. Kim,et al.  Are Asian stock markets efficient? Evidence from new multiple variance ratio tests ☆ , 2008 .

[72]  M. C. Jensen Some Anomalous Evidence Regarding Market Efficiency , 1978 .

[73]  Ralph Neuneier,et al.  Experiments in predicting the German stock index DAX with density estimating neural networks , 1996, IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr).

[74]  Wu Meng,et al.  Application of Support Vector Machines in Financial Time Series Forecasting , 2007 .

[75]  Yuehjen E. Shao,et al.  Incorporating feature selection method into support vector regression for stock index forecasting , 2012, Neural Computing and Applications.

[76]  Luciano Zunino,et al.  Forbidden patterns, permutation entropy and stock market inefficiency , 2009 .

[77]  Wei-Sen Chen,et al.  Using neural networks and data mining techniques for the financial distress prediction model , 2009, Expert Syst. Appl..

[78]  John M. Griffin,et al.  Do Market Efficiency Measures Yield Correct Inferences? A Comparison of Developed and Emerging Markets , 2010 .

[79]  Manfred Steiner,et al.  Portfolio optimization with a neural network implementation of the coherent market hypothesis , 1997, Eur. J. Oper. Res..

[80]  Kamil Zbikowski,et al.  Using Volume Weighted Support Vector Machines with walk forward testing and feature selection for the purpose of creating stock trading strategy , 2015, Expert Syst. Appl..

[81]  Mark J. Kamstra,et al.  Neural network forecast combining with interaction effects , 1999 .

[82]  Michael J. Schill,et al.  The Illusory Nature of Momentum Profits , 2004 .

[83]  F. Eugene FAMA, . Market efficiency, long-term returns, and behavioral finance, Journal of Financial Economics . , 1998 .

[84]  Nicolas Huck,et al.  Pairs trading and outranking: The multi-step-ahead forecasting case , 2010, Eur. J. Oper. Res..

[85]  M. T. Greene,et al.  Long-term dependence in common stock returns , 1977 .

[86]  George Athanasopoulos,et al.  Forecasting: principles and practice , 2013 .

[87]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[88]  Chris Brooks,et al.  Introductory Econometrics for Finance , 2002 .