Prediction of Interday Stock Prices Using Developmental and Linear Genetic Programming

Abstract

Abstract.A developmental co-evolutionary genetic programming approach (PAM DGP) is compared to a standard linear genetic programming (LGP) implementation for trading of stocks across market sectors. Both implementations were found to be impressively robust to market fluctuations while reacting efficiently to opportunities for profit, where PAM DGP proved slightly more reactive to market changes than LGP. PAM DGP outperformed, or was competitive with, LGP for all stocks tested. Both implementations had very impressive accuracy in choosing both profitable buy trades and sells that prevented losses, where this occurred in the context of moderately active trading for all stocks. The algorithms also appropriately maintained maximal investment in order to profit from sustained market upswings.

A new approach for the quantification of qualitative measures of economic expectations

“…Chen et al (2008) analysed the performance of GP to financial trading. Kaboudan (2000), Larkin and Ryan (2008), and Wilson and Banzhaf (2009) Applications of GP in macroeconomics have been very limited. The first application of GP is that of Koza (1992), who used GP to reassess the exchange equation relating the price level, gross national product, money supply, and the velocity of money.…”

Interday foreign exchange trading using linear genetic programming

“…The motivation for the use of LGP applied to foreign exchange markets is to analyze the behavior of an LGP trading system that has previously shown promise in stock market trading, as described in [16]. It is often recommended to test the robustness of trading systems using alternative markets [7].…”

Let the Data Do the Talking: Empirical Modelling of Survey-Based Expectations by Means of Genetic Programming

“…GP has also been applied to to model short-term capital flows (Yu et al, 2004), to forecast exchange rates (Álvarez-Díaz and Álvarez, 2005), and for stock price forecasting (Chen et al, 2008;Kaboudan, 2000;Larkin and Ryan, 2008;Wilson and Banzhaf, 2009). Wilson and Banzhaf (2009) compared a developmental co-evolutionary GP approach to standard linear GP for interday stock prices prediction.…”

“…GP has also been applied to to model short-term capital flows (Yu et al, 2004), to forecast exchange rates (Álvarez-Díaz and Álvarez, 2005), and for stock price forecasting (Chen et al, 2008;Kaboudan, 2000;Larkin and Ryan, 2008;Wilson and Banzhaf, 2009). Wilson and Banzhaf (2009) compared a developmental co-evolutionary GP approach to standard linear GP for interday stock prices prediction. Alexandridis et al (2017) have recently compared the forecasting performance of GP in the context of weather derivatives pricing with other state-of-the-art machine learning algorithms and classic linear approaches, finding that non-linear methods outperform the alternative linear models significantly.…”