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

Authored by Viktor Manahov, Robert Hudson, Hafiz Hoque

Date Published: 2015

DOI: 10.1016/j.intfin.2015.02.009

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

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. (C) 2015 Elsevier B.V. All rights reserved.

Tags

Performance knowledge Technical analysis Efficiency information Prediction Stock-market Auctions Forecast accuracy Regressions