Return predictability and the ‘wisdom of crowds’: Genetic Programming trading algorithms, the Marginal Trader Hypothesis and the Hayek Hypothesis

Viktor Manahov, Robert Hudson and Hafiz Hoque

Journal of International Financial Markets, Institutions and Money, 2015, vol. 37, issue C, 85-98

Abstract: We develop profitable stock market forecasts for a number of financial instruments and portfolios using a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based trading algorithm. The STGP-based trading algorithm produces one-day-ahead return forecasts for groups of artificial traders with different levels of intelligence and different group sizes. The performance of the algorithm is compared with a number of benchmark forecasts and these comparisons clearly demonstrate the short-term superiority of the STGP-based method in many circumstances. Subsequently we provide detailed analysis of the impact of trader cognitive abilities and trader numbers on the accuracy of forecasting rules which allows us to conduct new experimental tests of the Marginal Trader and the Hayek Hypotheses. We find little support for the Marginal Trader Hypothesis but some evidence for the Hayek Hypothesis.

Keywords: Forecasting and simulation; Agent-based modelling; Artificial stock market; Genetic Programming; Marginal Trader Hypothesis; Hayek Hypothesis (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfin:v:37:y:2015:i:c:p:85-98

DOI: 10.1016/j.intfin.2015.02.009

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