GP is very widely used in the areas of financial trading, time series prediction and economic modelling and it is impossible to describe all its applications. It this section we will hint at just a few areas.
Chen has written more than 60 papers on using GP in finance and economics. Recent papers have looked at the modelling of agents in stock markets (Chen and Liao, 2005), game theory (Chen, Duffy, and Yeh, 2002), evolving trading rules for the S&P 500 (?) and forecasting the Heng-Sheng index (Chen, Wang, and Zhang, 1999).
The efficient markets hypothesis is a tenet of economics. It is founded on the idea that everyone in a market has "perfect information" and acts "rationally". If the efficient markets hypothesis held, then everyone would see the same value for items in the market and so agree the same price. Without price differentials, there would be no money to be made from the market itself. Whether it is trading potatoes in northern France or dollars for yen, it is clear that traders are not all equal and considerable doubt has been cast on the efficient markets hypothesis. So, people continue to play the stock market. Game theory has been a standard tool used by economists to try to understand markets but is increasingly supplemented by simulations with both human and computerised agents. GP is increasingly being used as part of these simulations of social systems.
Neely, Weller, and Dittmar (1997), Neely and Weller (1999, 2001) and Neely ( 2003) of the US Federal Reserve Bank used GP to study intra-day technical trading on the foreign exchange markets to suggest the market is "efficient" and found no evidence of excess returns. This negative result was criticised by Marney, Miller, Fyfe, and Tarbert (2001). Later work by Neely, Weller, and Ulrich (2006) suggested that data after 1995 are consistent with Lo's adaptive markets hypothesis rather than the efficient markets hypothesis. Note that here GP and computer tools are being used in a novel data-driven approach to try and resolve issues which were previously a matter of dogma.
From a more pragmatic viewpoint, Kaboudan shows GP can forecast international currency exchange rates (Kaboudan, 2005), stocks (Kaboudan, 2000) and stock returns (Kaboudan, 1999). Tsang and his co-workers continue to apply GP to a variety of financial arenas, including: betting (?), forecasting stock prices (Li and Tsang, 1999; ?; ?), studying markets (Martinez-Jaramillo and Tsang, 2007), approximating Nash equilibrium in game theory (Jin, 2005; Jin and Tsang, 2006; ?) and arbitrage (?). Dempster and HSBC also use GP in foreign exchange trading (Austin, Bates, Dempster, Leemans, and Williams, 2004; Dempster and Jones, 2000; Dempster, Payne, Romahi, and Thompson, 2001). Pillay has used GP in social studies and teaching aids in education, e.g. (Pillay, 2003). As well as trees (Koza, 1990), other types of GP have been used in finance, e.g. (Nikolaev and Iba, 2002).
Since 1995 the International Conference on Computing in Economics and Finance (CEF) has been held every year. It regularly attracts GP papers, many of which are on-line. In 2007 Brabazon and O'Neill established the European Workshop on Evolutionary Computation in Finance and Economics (EvoFIN). EvoFIN is held with EuroGP.