Concept Drift Mining of Fundamental Variables in China Stock Market

We adopt the classical Fama and French (1992) [E.F. Fama, K.R. French, The Cross-Section of Expected Stock Returns, The Journal of Finance, 47 (1992) 427 - 465.] approach to investigate the effect of Beta, size, leverage, book value to market value (B/P), and earnings to price (E/P) ratio on the average monthly returns in the China Stock Market (CSM) from July 1998 to June 2011. The phenomenon of concept drift was found in this research. Our results indicate that the B/P has a significant negative correlation with the monthly average returns during this period, in contrast to the positive correlation found by a previous study by Wang and Di Iorio (2007) [Y. Wang, A. Di Iorio, The cross section of expected stock returns in the Chinese A-share market, Global Finance Journal, 17 (2007) 335 - 349.] that covered from July 1996 to June 2002. Meanwhile the small-firm effect remains significant. Thus, while the behavioral or institutional factors may account for the counter-intuitive finding of the effect of value in the transitional economy, the relationship between fundamental characteristics and stock returns is more complex in China and warrants more rigorous investigation. Further, we suggest that autonomic and cloud computing system has a number of potential applications in stock market concept drift mining.

[1]  E. Fama,et al.  The Cross‐Section of Expected Stock Returns , 1992 .

[2]  Žliobait . e,et al.  Learning under Concept Drift: an Overview , 2010 .

[3]  Pei-Chann Chang,et al.  Trend discovery in financial time series data using a case based fuzzy decision tree , 2011, Expert Syst. Appl..

[4]  Josef Lakonishok,et al.  New Paradigm or Same Old Hype in Equity Investing? , 2000 .

[5]  Padraig Cunningham,et al.  A case-based technique for tracking concept drift in spam filtering , 2004, Knowl. Based Syst..

[6]  Alexey Tsymbal,et al.  The problem of concept drift: definitions and related work , 2004 .

[7]  David Enke,et al.  A hybrid stock trading system for intelligent technical analysis-based equivolume charting , 2009, Neurocomputing.

[8]  Franklin Allen,et al.  Using genetic algorithms to find technical trading rules , 1999 .

[9]  Nikolaus Hautsch,et al.  When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions , 2011 .

[10]  G. Vechkanov.  Investments , 2014, Canadian Medical Association journal.

[11]  WangFenghua,et al.  What Determines Chinese Stock Returns , 2006 .

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

[13]  Michel Pasquier,et al.  Financial Market Trading System With a Hierarchical Coevolutionary Fuzzy Predictive Model , 2009, IEEE Transactions on Evolutionary Computation.

[14]  Amalia Di Iorio,et al.  The cross section of expected stock returns in the Chinese A-share market , 2007 .

[15]  Pei-Chann Chang,et al.  A Hybrid System Integrating a Wavelet and TSK Fuzzy Rules for Stock Price Forecasting , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[16]  Tim Brailsford,et al.  Selecting Macroeconomic Variables as Explanatory Factors of Emerging Stock Market Returns , 1999 .

[17]  Andre Lucas,et al.  Stock selection, style rotation, and risk , 2002 .

[18]  Herbert Y. T. Lam,et al.  Fundamental variables and the cross-section of expected stock returns: the case of Hong Kong , 2003 .

[19]  Michael W. Covel.  Trend Following: How Great Traders Make Millions in Up or Down Markets , 2004 .

[20]  Mieko Tanaka-Yamawaki,et al.  Adaptive use of technical indicators for the prediction of intra-day stock prices , 2007 .

[21]  Hussein Dourra,et al.  Investment using technical analysis and fuzzy logic , 2002, Fuzzy Sets Syst..

[22]  Robert W. Colby,et al.  The Encyclopedia of Technical Market Indicators , 1988 .

[23]  Akbar Esfahanipour,et al.  A genetic programming model to generate risk-adjusted technical trading rules in stock markets , 2011, Expert Syst. Appl..

[24]  J. Vu.  Value versus Growth: The International Evidence , 1999 .

[25]  Gholam Ali Montazer,et al.  Design and implementation of fuzzy expert system for Tehran Stock Exchange portfolio recommendation , 2010, Expert Syst. Appl..

[26]  Fenghua Wang,et al.  What Determines Chinese Stock Returns? , 2003 .

[27]  Rishi K. Narang.  Inside the Black Box: The Simple Truth About Quantitative Trading , 2009 .

[28]  Philip S. Yu,et al.  Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.

[29]  J. Lewellen.  The Cross Section of Expected Stock Returns , 2014 .

[30]  E. Fama,et al.  Value Versus Growth: The International Evidence , 1997 .

[31]  E. Fama,et al.  Risk, Return, and Equilibrium: Empirical Tests , 1973, Journal of Political Economy.

[32]  Keith S. K. Lam,et al.  The relationship between size, book-to-market equity ratio, earnings–price ratio, and return for the Hong Kong stock market , 2002 .

[33]  Massimiliano Kaucic,et al.  Investment using evolutionary learning methods and technical rules , 2010, Eur. J. Oper. Res..

[34]  D. Robert,et al.  EFFECT OF MACROECONOMIC VARIABLES ON STOCK MARKET RETURNS FOR FOUR EMERGING ECONOMIES: BRAZIL, RUSSIA, INDIA, AND CHINA , 2008 .

[35]  Martin J. Pring,et al.  Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points , 1980 .

[36]  Monica Lam,et al.  Neural network techniques for financial performance prediction: integrating fundamental and technical analysis , 2004, Decis. Support Syst..

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

[38]  P. Henry.  Stock Market Liberalization, Economic Reform, and Emerging Market Equity Prices , 2000 .

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