Hedging without sweat: a genetic programming approach
Journal article, Peer reviewed
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- Articles (FIN) 
Original versionQuantitative Finance Letters 2013, 1(1):41-46 10.1080/21649502.2013.813166
Hedging in the presence of transaction costs leads to complex optimization problems. These problems typically lack closed-form solutions, and their implementation relies on numerical methods that provide hedging strategies for specific parameter values. In this paper, we use a genetic programming algorithm to derive explicit formulas for near-optimal hedging strategies under nonlinear transaction costs. The strategies are valid over a large range of parameter values and require no information about the structure of the optimal hedging strategy.