Genetic programming (GP) is an evolutionary algorithm that optimizes a population of computer programs according to a fitness landscape determined by a program's ability to perform a user-defined task. The first experiments with GP were reported by Smith (1980) and Cramer (1985), and the seminal book is Koza (1992).
Financial Applications of Genetic Programming
Most cited paper:
Neely, Weller and Dittmar, (1996)
Park and Irwin's (2004) excellent technical analysis review paper includes 11 studies that implement GP: five were positive, two mixed and four negative.
Using genetic programming techniques to find technical trading rules, we find strong evidence of economically significant out-of-sample excess returns to those rules for each of six exchange rates, over the period 1981-1995. Further, when the dollar/deutschemark rules are allowed to determine trades in the other markets, there is a significant improvement in performance in all cases, except for the deutschemark/yen. Betas calculated for the returns according to various benchmark portfolios provide no evidence that the returns to these rules are compensation for bearing systematic risk. Bootstrapping results on the dollar/deutschemark indicate that the trading rules are detecting patterns in the data that are not captured by standard statistical models.
In this paper, we propose a new architecture to study artificial stock markets. This architecture rests on a mechanism called 'school' which is a procedure to map the phenotype to the genotype or, in plain English, to uncover the secret of success. We propose an agent-based model of 'school', and consider school as an evolving population driven by single-population GP (SGP). The architecture also takes into consideration traders' search behavior. By simulated annealing, traders' search density can be connected to psychological factors, such as peer pressure or economic factors such as the standard of living. This market architecture was then implemented in a standard artificial stock market. Our econometric study of the resultant arti"cial time series evidences that the return series is independently and identically distributed (iid), and hence supports the e$cient market hypothesis (EMH). What is interesting though is that this iid series was generated by traders, who do not believe in the EMH at all. In fact, our study indicates that many of our traders were able to "nd useful signals quite often from business school, even though these signals were short-lived.
not used?
Technical analysis is aimed at devising trading rules capable of exploiting short-term fluctuations on the financial markets. Recent results indicate that this market timing approach may be a viable alternative to the buy-and-hold approach, where the assets are kept over a relatively long time period. In this paper, we propose genetic programming as a means to automatically generate such short-term trading rules on the stock markets. Rather than using a composite stock index for this purpose, the trading rules are adjusted to individual stocks. Computational results, basedon historical pricing andtransaction volume data, are reportedfor 14 Canadian companies listedon the Toronto stock exchange market.
Conclusion: The results show that the trading rules generated by GP are generally bene3cial when the market falls or when it is stable. On the other hand, these rules do not match the buy-and-hold approach when the market is rising.
Based on predictions of stock-prices using genetic programming (or GP), a possibly profitable trading strategy is proposed. A metric quantifying the probability that a specific time series is GP-predictable is presented first. It is used to show that stock prices are predictable. GP then evolves regression models that produce reasonable one-day-ahead forecasts only. This limited ability led to the development of a single day-trading strategy (SDTS) in which trading decisions are based on GP-forecasts of daily highest and lowest stock prices. SDTS executed for fifty consecutive trading days of six stocks yielded relatively high returns on investment.
NEELY, C., P. WELLER and R. DITTMAR, 1997. Is technical analysis profitable in the foreign exchange market? A genetic programming approach. Journal of Financial and Quantitative Analysis. [Cited by 8] (0.94/year)
BUTLER, J.M., 1997. EDDIE beats the market, data mining and decision support through genetic programming. Developments, Reuters Limited. [Cited by 5] (0.58/year)
ZHANG, X.B., Y.K. TSE and W.S. CHAN, 2000. Detecting structural changes using genetic programming with an application to the Greater-China …. Statistics and Finance: An Interface. [Cited by 1] (0.18/year)
CHEN, S. and C. YEH, Genetic programming, predictability, and stock market efficiency. Proceedings of the Modelling and Control of National and …. [Cited by 5] (?/year)