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Search Results (3)

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Keywords = ex-ante beta

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16 pages, 894 KiB  
Article
Mean–Variance Portfolio Efficiency under Leverage Aversion and Trading Impact
by Chanaka Edirisinghe and Jaehwan Jeong
J. Risk Financial Manag. 2022, 15(3), 98; https://doi.org/10.3390/jrfm15030098 - 23 Feb 2022
Cited by 3 | Viewed by 3359
Abstract
This paper addresses the optimal rebalancing problem of a long–short portfolio with high net asset value under trading impact losses. The fund manager may employ leveraging as a tool to increase portfolio returns. However, to mitigate potential leverage risks, frequent rebalancing may become [...] Read more.
This paper addresses the optimal rebalancing problem of a long–short portfolio with high net asset value under trading impact losses. The fund manager may employ leveraging as a tool to increase portfolio returns. However, to mitigate potential leverage risks, frequent rebalancing may become necessary, which leads to significant slippage losses that dampen portfolio performance ex post. We consider the problem in an integrated framework by incorporating trading impact and leverage restrictions ex ante within a mean–variance framework, where leverage control is imposed using a chance constraint. The resulting mean–variance–leverage optimization model (MVL) is non-convex, and we develop an efficient scheme to obtain the optimal portfolio. We investigate how portfolio leverage modifies the MV efficient frontier in the presence of trading impact, and highlight the significant outperformance of the proposed model relative to the standard mean–variance model. Increased target means require less restrictions on leverage, which result in higher rates of slippage losses. Our analysis supports the notion that leverage restrictions contribute to choosing high beta assets, even in the presence of trading impact. Full article
(This article belongs to the Special Issue Dynamic Portfolio Investment with Changing Economic States)
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22 pages, 593 KiB  
Article
Integrating ESG Analysis into Smart Beta Strategies
by Federica Ielasi, Paolo Ceccherini and Pietro Zito
Sustainability 2020, 12(22), 9351; https://doi.org/10.3390/su12229351 - 11 Nov 2020
Cited by 10 | Viewed by 8031
Abstract
Smart beta strategy is an increasingly frequent approach to investment analysis for portfolio selection and optimization and it can be combined with environmental, social, and governance (ESG) considerations. In order to verify the impact of the integration between ESG and smart beta analysis, [...] Read more.
Smart beta strategy is an increasingly frequent approach to investment analysis for portfolio selection and optimization and it can be combined with environmental, social, and governance (ESG) considerations. In order to verify the impact of the integration between ESG and smart beta analysis, first we apply a portfolio rebalancing based on ESG scores on securities selected according to different smart beta strategies (ex-post ESG rebalancing approach). Secondly, we apply different smart beta approaches to sustainable portfolios, screened according to the issuers’ ESG scores (ex-ante ESG screening approach). We find that ESG rebalancing and screening are able to impact both on return and risk statistics, but with a different level of efficiency for each smart beta strategy. ESG rebalancing proves to be particularly efficient when it is applied to a “Value” portfolio. On the other hand, when smart beta is applied to ESG-screened portfolios, “Growth” is the strategy which shows the highest increase in risk-adjusted performance, particularly in the US. Minimum volatility proves to be the most efficient smart beta strategy for sustainable portfolios. In general, the increase in the level of sustainability does not deteriorate the risk-adjusted performances of most smart beta strategies. Full article
(This article belongs to the Special Issue New Economic and Financial Challenges for Social and Environmental Sustainability)
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13 pages, 224 KiB  
Article
Back to the Future Betas: Empirical Asset Pricing of US and Southeast Asian Markets
by Jordan French
Int. J. Financial Stud. 2016, 4(3), 15; https://doi.org/10.3390/ijfs4030015 - 20 Jul 2016
Cited by 4 | Viewed by 8214
Abstract
The study adds an empirical outlook on the predicting power of using data from the future to predict future returns. The crux of the traditional Capital Asset Pricing Model (CAPM) methodology is using historical data in the calculation of the beta coefficient. This [...] Read more.
The study adds an empirical outlook on the predicting power of using data from the future to predict future returns. The crux of the traditional Capital Asset Pricing Model (CAPM) methodology is using historical data in the calculation of the beta coefficient. This study instead uses a battery of Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) models, of differing lag and parameter terms, to forecast the variance of the market used in the denominator of the beta formula. The covariance of the portfolio and market returns are assumed to remain constant in the time-varying beta calculations. The data spans from 3 January 2005 to 29 December 2014. One ten-year, two five-year, and three three-year sample periods were used, for robustness, with ten different portfolios. Out of sample forecasts, mean absolute error (MAE) and mean squared forecast error (MSE) were used to compare the forecasting ability of the ex-ante GARCH models, Artificial Neural Network, and the standard market ex-post model. Find that the time-varying MGARCH and SGARCH beta performed better with out-of-sample testing than the other ex-ante models. Although the simplest approach, constant ex-post beta, performed as well or better within this empirical study. Full article
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