An intelligent recommender system for stock trading

Abstract. Generating consistent profits from stock markets is considered to be a challenging task, especially due to the nonlinear nature of the stock price movements. Traders need to have a deep understanding of the market behavior patterns in order to trade successfully. In this study, a GA optimized technical indicator decision tree-SVM based intelligent recommender system is proposed, which can learn patterns from the stock price movements and then recommend appropriate one-day-ahead trading strategy. The recommender system takes the task of identifying stock price patterns on itself, allowing even a lay-user, who is not well versed in stock market behavior, to trade profitably on a consistent basis. The efficacy of the proposed system is validated on four different stocks belonging to two different stock markets (India and UK) over three different time frames for each stock. Performance of the proposed system is validated using fifteen different measures. Performance is compared with traditional technical indicator based trading and the traditional buy and hold strategy. Results indicate that the proposed system is capable of generating profits for all the stocks in both the stock markets considered.

[1]  Binoy B. Nair,et al.  Stock Market Prediction Using a Hybrid Neuro-fuzzy System , 2010, 2010 International Conference on Advances in Recent Technologies in Communication and Computing.

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

[3]  Binoy B. Nair,et al.  Artificial intelligence applications in financial forecasting - a survey and some empirical results , 2015, Intell. Decis. Technol..

[4]  Michel Verleysen,et al.  Forecasting Financial Time Series through Intrinsic Dimension Estimation and Non-Linear Data Projection , 1999, IWANN.

[5]  J. Platt.  Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[6]  Tong-Seng Quah,et al.  Improving returns on stock investment through neural network selection , 1999 .

[7]  Ganapati Panda,et al.  On the development of improved adaptive models for efficient prediction of stock indices using clonal-PSO (CPSO) and PSO techniques , 2008 .

[8]  David J. Doorn.  Consequences of Hodrick–Prescott filtering for parameter estimation in a structural model of inventory behaviour , 2006 .

[9]  N. P. Landsman,et al.  A random walk down Wall Street , 2008 .

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

[11]  Binoy B. Nair,et al.  A GA-artificial neural network hybrid system for financial time series forecasting , 2011 .

[12]  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.

[13]  A. Cowles,et al.  Stock Market Forecasting , 1944 .

[14]  E. Fama.  Random Walks in Stock Market Prices , 1965 .

[15]  Byung Ro Moon,et al.  Stock prediction based on financial correlation , 2005, GECCO '05.

[16]  E. Otranto,et al.  Signal Extraction in Continuous Time and the Generalized Hodrick- Prescott Filter , 2003 .

[17]  Arthur Kraft,et al.  Determinants of Common Stock Prices: A Time Series Analysis , 1977 .

[18]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[19]  A. Cowles.  Can Stock Market Forecasters Forecast , 1933 .

[20]  S. Achelis.  Technical analysis a to z , 1994 .

[21]  Binoy B. Nair,et al.  A Stock Market Trend Prediction System Using a Hybrid Decision Tree-Neuro-Fuzzy System , 2010, 2010 International Conference on Advances in Recent Technologies in Communication and Computing.

[22]  Chi-Jie Lu,et al.  Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes , 2012, Expert Syst. Appl..

[23]  P. Samuelson.  Proof that Properly Anticipated Prices Fluctuate Randomly , 2015 .

[24]  Binoy B. Nair,et al.  A Genetic Algorithm Optimized Decision Tree- SVM based Stock Market Trend Prediction System , 2010 .

[25]  Kin Keung Lai,et al.  Mining Stock Market Tendency Using GA-Based Support Vector Machines , 2005, WINE.

[26]  Magdalene Marinaki,et al.  Ant colony and particle swarm optimization for financial classification problems , 2009, Expert Syst. Appl..

[27]  R. Edwards,et al.  Technical Analysis of Stock Trends , 1966 .

[28]  Amit Mitra,et al.  Forecasting daily foreign exchange rates using genetically optimized neural networks , 2002 .

[29]  N. R. Sakthivel,et al.  A Decision Tree- Rough Set Hybrid System for Stock Market Trend Prediction , 2010 .

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

[31]  E. Fama.  Random Walks in Stock Market Prices , 1965 .

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

[33]  N. R. Sakthivel,et al.  Predicting stock market trends using hybrid ant-colony-based data mining algorithms: an empirical validation on the Bombay Stock Exchange , 2011 .

[34]  E. Prescott,et al.  Postwar U.S. Business Cycles: An Empirical Investigation , 1997 .

[35]  E. Fama.  The Behavior of Stock-Market Prices , 1965 .