Evaluating hedge fund downside risk using a multi-objective neural network

Abstract

Predicting the downside risk of a hedge fund is the foundation of risk measurement. These predictions also provide conditions that can be used for designing and implementing risk prevention measures. Hence, this paper proposes a big data hedge fund downside risk evaluation model based on a multi-objective neural network. First, two evaluation indexes are defined. Then, local search is applied to merge parent and descendant populations. Only those individuals from the Pareto front are optimized. Experimental results suggest that this model and method is feasible and valid. Specifically, the VaR model is unable to estimate the possible extreme risk of a hedge fund. In contrast, the CVaR model can accurately measure the risks under extreme market conditions. However, a combination of VaR and CVaR can help a fund manager avoid extreme risks to a hedge fund.