Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.jvcir.2018.11.002 | Journal of Visual Communication and Image Representation |
Keywords | Field | DocType |
Downside risk evaluation,Big data hedge fund,Multi-objective neural network | Econometrics,Hedge fund,Downside risk,Pattern recognition,Vector autoregression,Multi-objective optimization,Artificial intelligence,Investment management,Local search (optimization),Big data,Mathematics,CVAR | Journal |
Volume | ISSN | Citations |
59 | 1047-3203 | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaoquan Cai | 1 | 52 | 12.23 |
| Guangcai Chen | 2 | 0 | 0.34 |
| Lining Xing | 3 | 0 | 0.34 |
| Jinghui Yang | 4 | 3 | 1.81 |
| Xu Tan | 5 | 25 | 3.93 |