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New evidence about the profitability of small and large stocks and the role of volume obtained using Strongly Typed Genetic Programming

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  • Manahov, Viktor
  • Hudson, Robert
  • Linsley, Philip

Abstract

We employ a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm to develop trading rules based on a survival of the fittest principle. Employing returns data for the Russell 1000, Russell 2000 and Russell 3000 indices the STGP method produces greater returns compared to random walk benchmark forecasts, and the forecasting models are statistically significant in respect of their predictive effectiveness for all three indices both in- and out-of-sample. Using one-step-ahead STGP models to investigate the differences in return patterns between small and large stocks we demonstrate the superiority of models developed for small-cap stocks over those developed for large-cap stocks, indicating that small stocks are more predictable. We also investigate the relationship between trading volume and returns, and find that trading volume has negligible predictive strength, implying it is not advantageous to develop volume-based trading strategies.

Suggested Citation

  • Manahov, Viktor & Hudson, Robert & Linsley, Philip, 2014. "New evidence about the profitability of small and large stocks and the role of volume obtained using Strongly Typed Genetic Programming," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 299-316.
    • Handle: RePEc:eee:intfin:v:33:y:2014:i:c:p:299-316
    DOI: 10.1016/j.intfin.2014.08.007
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    Cited by:

    1. Manahov, Viktor & Hudson, Robert & Hoque, Hafiz, 2015. "Return predictability and the ‘wisdom of crowds’: Genetic Programming trading algorithms, the Marginal Trader Hypothesis and the Hayek Hypothesis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 85-98.

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    More about this item

    Keywords

    Forecasting and simulation; Small Stocks; Agent-based modelling; Artificial stock market; Genetic programming; Capital asset pricing model; Efficiency;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G1 - Financial Economics - - General Financial Markets
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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