| Summary: | This thesis considers genetic programming (GP) for evolving financial trading strategies. The traditional approach in the literature is to represent a trading strategy, or a program, as a single decision tree. This thesis proposes a general multiple tree framework for dynamic decision making, where evaluation is contingent on the previous output ofthe program. The conditional multiple tree structure nests the single tree as a special case. TheoreticalIy, it is a superior alternative, but in practice this is not always the case. It depends on the underlying problem, and is basically a manifestation ofOckham srazor. The framework is validated on artificial data, and hereafter it is applied to two real financial problems: statistical arbitrage and high-frequency foreign exchange trading. In contrast to a pure arbitrage, that guarantees a sure profit, a statistical arbitrage strategy only produces a riskless profit in the limit. Both schemes are self-funding. In this thesis, single and dual trees are used to evolve statistical arbitrage strategies on banking stocks within the Euro Stoxx index. Both single and dual trees are capable of discovering significant statistical arbitrage strategies, even in the presence of a realistic market impact. A finding that points to weak form market inefficiencies. Moreover, it is found that the dual trees provide a more robust response, compared to the single trees, when the market impact is increased. The foreign exchange application considers a novel quad tree structure for evolving trading strategies. Each ofthe four trees serve different functions, i.e., long entry, long exit, short entry and short exit. Within this framework, the effects of money management are investigated for investors with different utility functions. Money management refers to the way in which practitioners use stop orders to control risk and take profits. Despite being widely used, it is found that money management has a detrimental effect on utility. JEL classifications: CO, CI5, C45, C53, C6I, C63, GIl Keywords: Genetic programming, optimization, trading strategies, market efficiency, intraday data, statistical arbitrage, portfolio construction, foreign exchange and money management.
|
|---|
