Return predictability and the ‘wisdom of crowds’: Genetic Programming trading algorithms, the Marginal Trader Hypothesis and the Hayek Hypothesis
Introduction
A voluminous literature exists on predicting stock market returns using different variables. However, most of the empirical studies suffer a major shortcoming – they find evidence in favour of in-sample return predictability but no presence of out-of-sample profitability. Goyal and Welch (2008) suggest that the predictive ability of nearly all stock return forecasting models does not hold up in out-of-sample validation. Moreover, a number of researchers argue that many studies finding predictability of stock returns might be spurious. This is based on the empirical assumption that the persistence of the forecasting variables and the correlation of their innovations with returns might bias the regression coefficients and negatively affect t-statistics (Stambaugh, 1999, Lewellen, 2004). These issues are exacerbated when researchers are data mining, taking into account many variables, and reporting only statistically significant findings (Ferson et al., 2003, Ferreira and Santa-Clara, 2011). Most prior studies investigate the relationship between the available information and the stock returns using simple linear regression. This is quite a restrictive approach as there is no evidence to support the assumption that the relationship between stock returns and the financial and economic variables in these studies is perfectly linear (Enke and Thawornwong, 2005).
In this paper we use Strongly Typed Genetic Programming (STGP), a new forecasting approach to predict stock market returns, based on intelligent agents interacting in an artificial stock market. Research on financial markets has rarely used STGP models although they offer considerable advantages compared to any other forecasting techniques including the ability to evolve through time and deal with non-linear relationships (see Manahov et al., 2014a, Manahov et al., 2014b, for recent papers describing STGP models and applying them in financial markets). We compare the STGP predictions to several appropriate benchmark forecasts on an in-sample and out-of-sample basis using a number of performance measures to investigate whether, to what extent and in which form, stock returns in excess of the risk free rate are indeed predictable and profitable compared to a buy-and-hold strategy with dividends and without dividends. This is done by measuring and quantifying the exact level of generated profit, taking into account the transaction costs of 85 basis points per round trip.
The other important question under investigation in this study relates to how stock markets incorporate information and generate prices. There are two competing hypotheses about how markets work – the Marginal Trader Hypothesis (MTH) and the Hayek Hypothesis. While the MTH postulates that a small fraction of savvy individuals are capable of setting market prices, the Hayek Hypothesis suggests that markets can operate in an efficient manner even when market participants have a limited knowledge of the surrounding environment. Blackwell (2011) and Blackwell and Pickford (2011) argue that the MTH is so intuitive to economists that it seems never to have been tested directly and examined rigorously. In real financial markets it is difficult to determine whether the market price is actually the ‘correct’ price for any asset because is impossible to know the ‘correct’ price (Blackwell, 2011). Artificial stock markets do not necessarily suffer from this limitation because the market price of the instrument in the market can reflect some underlying prediction and the accuracy of that prediction can be checked. In addition, in real markets it is clearly not possible to directly control the number and intelligence of the participants. Using our STGP models we conduct experiments which avoid the difficulties in testing the MTH and the Hayek Hypothesis and provide appropriate laboratory conditions for direct examination of both hypotheses. To test the MTH we investigate whether a small fraction of the genetic population, the ‘Best Performing Traders’, are capable of generating more accurate forecasts. We show that the forecasting process emerging from the collective behaviour (competition and co-evolution) of the entire set of traders in the market is a better predictor than that produced by any small subset of traders, even the ‘Best Performing Traders’. We demonstrate that enhanced genetic diversity leads to increased accuracy in forecasts because of more heterogeneous trading rules and behaviour. To examine the validity of the Hayek Hypothesis we populate another model with ‘Low Intelligence Traders’ in order to investigate their forecasting abilities. We provide evidence that the level of traders’ intelligence does not necessarily play a significant role in improving the quality of forecasts (this represents new evidence favourable to the Hayek Hypothesis in a broader environment). The reminder of this paper is organised as follows: Section 2 presents the background to the study and reviews the literature in the field; Section 3 discusses the experimental design; Sections 4 Simulation results – relevant for market prediction, 5 Simulation results – relevant to assessing the ‘wisdom of crowds’ discuss the simulation results relevant for prediction and assessing the ‘wisdom of crowds’ respectively and the paper concludes with overall remarks and discusses avenues for further research in Section 6.
Section snippets
Background
In this section we outline the background to the study and the relevant prior literature. In Section 2.1 we discuss the forecasting of financial markets and how Genetic Programming has previously been used in this domain. Section 2.2 outlines prior research on the implications of trader cognitive abilities for forecasting accuracy.
Developing initial trading rules
We use a STGP based artificial stock market model similar to that used in Manahov et al., 2014a, Manahov et al., 2014b and the most salient features of the model are described in those papers. For conciseness we refer readers to the previous papers for details and explanation of the model and here confine ourselves to describing the particular features of the model used in the experiment reported in this paper. Table 1 shows the main parameters of the STGP model used here.
Artificial stock market structure
We study traders’
Simulation results – relevant for market prediction
This section deals with the elements of our results that are particularly related to predictive accuracy. Section 4.1 outlines the results from our main experiment and Section 4.2 gives some robustness results on some constructed portfolios.
Simulation results – relevant to assessing the ‘wisdom of crowds’
This section deals with the elements of our results that are relevant for assessing the ‘wisdom of crowds’. Section 5.1 outlines the evidence about the MTH and Section 5.2 gives evidence about the Hayek Hypothesis.
Conclusions
Developing and selecting a plausible model are the two most important ingredients of the forecasting process. In this paper we compare the forecasting and in-sample performance of an STGP model with that of several benchmarking models. We apply formal statistical model selection criteria to establish the most accurate model for modelling the returns of a number of financial instruments and portfolios. We place heavy emphasis on genuine out-of-sample (ex ante) predictions. Our findings
References (54)
- et al.
Using genetic algorithms to find technical trading rules
J. Financ. Econ.
(1999) - et al.
Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional methods
Journal of Retailing and Consumer Services
(2001) - et al.
The use of data mining and neural networks for forecasting stock market returns
Expert Syst. Appl.
(2005) - et al.
Forecasting stock market returns: the sum of the parts is more than the whole
J. Financ. Econ.
(2011) - et al.
Another look at measures of forecasting accuracy
Int. J. Forecast.
(2006) - et al.
Stock prices and the efficient market hypothesis: evidence from a panel stationary test with structural breaks
Jpn. World Econ.
(2010) Predicting returns with financial ratios
J. Financ. Econ.
(2004)- et al.
Does high frequency trading affect technical analysis and market efficiency? And is so, how?
J. Int. Financ. Mark. Inst. Money
(2014) - et al.
A hybrid ARIMA and support vector machines model in stock price forecasting
Omega
(2005) - et al.
Generating trading rules on the stock markets with genetic programming
Comput. Oper. Res.
(2004)
Predictive regressions: theoretical results and applications
J. Financ. Econ.
Markets and the use of knowledge.
Testing the ‘Hayek hypothesis’ in experimental stock markets.
GP-evolved technical trading rules can outperform buy and hold
A quasi-experimental test of the Marginal Trader Hypothesis
Kyklos
The wisdom of the few or the wisdom of the many? An indirect test of the Marginal Trader Hypothesis
J. Econ. Financ.
Information and efficiency in political stock markets: using computerised markets to predict election results
Appl. Econ.
The curse of knowledge in economic settings: an experimental analysis
Journal of Political Economy
On the persistence in mutual fund performance
J. Financ.
Robustness of alternative non-linearity tests for SETAR models
J. Forecast.
Model uncertainty and forecast accuracy
J. Forecast.
Option pricing with genetic programming
Decimalization, ETFs and futures pricing efficiency
J. Futures Mark.
Adaptive evolutionary approach to option pricing via genetic programming
A Monte Carlo study of the forecasting performance of empirical SETAR models. Working paper
The Hayek hypothesis in experimental auctions: institutional effects and market power
Econ. Inq.
Common risk factors in the returns on stocks and bonds
J. Financ. Econ.
Multifactor explanations of asset pricing anomalies
J. Financ.
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