Robust reward–risk ratio portfolio optimization

Abstract

In this paper, we propose robust portfolio optimization models for reward–risk ratios utilizing Omega, semi‐mean absolute deviation ratio, and weighted stable tail adjusted return ratio (STARR). We address the uncertainty in returns on assets by varying them in symmetric bounded intervals. The proposed robust reward–risk ratios preserve linearity in the resulting models, and hence are tractable. However, the robust models involve a sizably voluminous number of constraints, especially when the number of assets and scenarios is large. We employ the cutting plane algorithm to solve the proposed models in a much reduced time efficiently. We evaluate the performance of the robust reward–risk ratio models on the listed stocks of FTSE 100, Nikkei 225, S&P 500, and S&P BSE 500. The robust portfolio optimization models are found to outperform their conventional counterpart models in terms of statistics measured by the standard deviation, value at risk (VaR), conditional value at risk (CVaR), Sharpe ratio, and STARR ratio.

Econometric Modeling to Measure the Efficiency of Sharpe’s Ratio with Strong Autocorrelation Portfolios

“…Preliminary analysis of these specifications shows that the effect described in equations ( 6), (8), and (9), where the autocorrelation effect distorts the mean value, standard deviation, and Sharpe's ratio, also causes a degradation in the correlation between Sharpe's ratio and other performance measures, which may alter the conclusions described by Eling and Shuhmacher [23] and Eling [25] and complements what is mentioned by Zakamouline [36].…”

“…Sharpe's ratio shows an inverse relationship between the expected return and the risk level of a given asset and is measured by the standard deviation of the asset. Amenc et al [8] mentioned that 80% of managers use Sharpe's ratio for the evaluation of their portfolios [9,10]. is measure of market returns and risk is widely used by investors when they consider that the Sharpe property responds to the generation of data from normally distributed returns, and Sharpe's ratio has been widely accepted because of the direct linkage that can be drawn from modern portfolio theory, which was first proposed by Markowitz [11].…”

A Comprehensive Soft Computing-Based Approach to Portfolio Management by Discarding Undesirable Stocks

“…Due to that, numerous works have improved the model, creating more risks measures and proposing restrictions that bring them closer to practical aspects of stock market trading [43]. In consequence, many optimizations methods based on exact algorithms (e.g., [1], [2], [12], [61]- [65]), heuristic and hybrid optimization (e.g., [3], [6], [10], [66]- [73]) have been proposed to solve the emerging portfolio optimization models [24], [43], [58].…”

“…There are several approaches in the literature that focus mainly on portfolio optimization (e.g., [1]- [12]), while other works focus mainly on portfolio selection (e.g., [13]- [18]), and some other focus only on forecasting the stock returns (e.g., [6], [19]). Furthermore, some works describe methodologies for up to two of these stages; for example, [20] and [21] focus on price forecasting as the first step of an overall methodology that is complemented with stock selection; the works [22]- [24] focus on both stock selection and portfolio optimization.…”

Worst-case analysis of Gini mean difference safety measure

“…The worst-case analysis of the same is performed under the uncertainty sets considered by Zhu and Fukushima [48] and Kapsos et al [20]. Sehgal and Mehra (2019) [38] put forward robust models for Omega, semi-MAD, and weighted STARR ratios, by varying the uncertain input returns in bounded and symmetric intervals.…”