Combining Ordinal Financial Predictions with Genetic Programming
Ordinal data play an important part in financial forecasting. For example, advice from expert sources may take the form of “bullish”, “bearish” or “sluggish”, or “buy” or “do not buy”. This paper describes an application of using Genetic Programming (GP) to combine investment opinions. The aim is to combine ordinal forecast from different opinion sources in order to make better predictions. We tested our implementation, FGP (Financial Genetic Programming), on two data sets. In both cases, FGP generated more accurate rules than the individual input rules.
KeywordsGenetic Programming Point Forecast Technical Rule Genetic Programming System Expert Source
Unable to display preview. Download preview PDF.
- 1.Angeline, P. & Kinnear, K. E., (ed.), Advances in genetic programming II, MIT Press. 1996. Blume, L., Easley, D. & O’Hara, M., Market statistics and technical analysis: the role of volume, Journal of finance, 49, (1994), 153–181.Google Scholar
- 2.Brock, W., Lakonishok, J. & LeBaron, B., Simple technical trading rules and the stochastic properties of stock returns, Journal of Finance, 47, (1992), 1731–1764.Google Scholar
- 4.Chen, S-H. & Yeh, C-H., Speculative trades and financial regulations: simulations based on genetic programming, Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr), New York City, (1997), 123–129.Google Scholar
- 7.Goldberg, D.E., Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989.Google Scholar
- 8.Granger, C.W.J., Forecasting, in Newman, P., Milgate, M. & Eatwell, J. (ed.), New palgrave dictionary of money and finance, Macmillan, London, (1992), 142–143.Google Scholar
- 9.Holland, J. H., Adaptation in natural and artificial system, University of Michigan Press, 1975.Google Scholar
- 10.Kohavi, R., A study of cross-validation and bootstrap for accuracy estimation and model selection, Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), (1995), 1137–1143.Google Scholar
- 12.Koza, J., Goldberg, D., Fogel, D. & Riolo, R. (ed.), Proceedings of the First Annual Conference on Genetic programming, MIT Press, 1996.Google Scholar
- 13.Lobo, G., Alternative methods of combining security analysts’ and statistical forecasts of annual corporate earnings, International Journal of Forecasting, (1991), 57–63.Google Scholar
- 14.Neely, C., Weller, P. & Ditmar, R., Is technical analysis in the foreign exchange market profitable? a genetic programming approach, in Dunis, C. & Rustem, B. (ed.), Proceedings, Forecasting Financial Markets: Advances for Exchange Rates, Interest Rates and Asset Management, London. 1997.Google Scholar
- 17.Wall, K. & Correia, C., A preference-based method for forecast combination, Journal of Forecasting, (1989), 269–192.Google Scholar