Abstract
The problem of finding the optimal portfolio for investors is called the portfolio optimization problem. Such problem mainly concerns the expectation and variability of return (i.e., mean and variance).
Although the variance would be the most fundamental risk measure to be minimized, it has several drawbacks.
Conditional Value-at-Risk (CVaR) is a relatively new risk measure that addresses some of the shortcomings of well-known variance-related risk measures, and because of its computational efficiencies, it has gained popularity.
CVaR is defined as the expected value of the loss that occurs beyond a certain probability level (β).
However, portfolio optimization problems that use CVaR as a risk measure are formulated with a single β and may output significantly different portfolios depending on how the β is selected.
We confirm even small changes in β can result in huge changes in the whole portfolio structure.
In order to improve this problem, we propose RM-CVaR: Regularized Multiple β-CVaR Portfolio.
We perform experiments on well-known benchmarks to evaluate the proposed portfolio.
Compared with various portfolios, RM-CVaR demonstrates a superior performance of having both higher risk-adjusted returns and lower maximum drawdown.
Keywords
- AI for trading: AI for portfolio analytics
- AI for risk and security: AI for financial risk analytics
- AI for risk and security: AI for institutional risk modeling
- AI for wealth: AI for roboadvising
- Other areas: Financial decision-support system
- AI for trading: General
