Hybrid journal (It can contain Open Access articles) ISSN (Print) 2048-2361 - ISSN (Online) 2048-237X Published by Inderscience Publishers[449 journals]

Authors:Puneet K. Bindlish, Sharda S. Nandram Pages: 99 - 113 Abstract: Representing continuously compounded returns in seven asset classes, by their four bilateral gamma parameter estimates, a multiclass classification support vector machine is trained, on a sample of less than one percent of the data, to predict the asset class from which the returns were obtained. The asset classes considered are equities, volatility, commodities, foreign exchange, credit and bond indices and returns of hedge funds. Linear classification is observed to perform poorly. The use of seven binary learners makes some improvement and twenty one, one on one, binary learners deliver a good classification algorithm, also performing well out of sample. Keywords: bilateral gamma model; digital moment estimation; asset allocation Citation: International Journal of Portfolio Analysis and Management, Vol. 2, No. 2 (2018) pp. 99 - 113 PubDate: 2018-06-26T23:20:50-05:00 DOI: 10.1504/IJPAM.2018.092647 Issue No:Vol. 2, No. 2 (2018)

Authors:Ruilin Tian, Fariz Huseynov, Wei Zhang Pages: 141 - 168 Abstract: This paper investigates tactical investment strategies for investors to survive financial crises. Compared with the buy-and-hold strategy, the buy-and-sell strategy is much more effective in mitigating downside risk before, during, and after a crisis by restricting the left-tail volatility of portfolio returns through CVaR constraints. The paper also studies investors' optimal turnovers around a crisis under the buy-and-hold strategy. Considering investors' heterogeneous behaviours, we find the wealth-weighted average optimal turnover across all investors during a crisis is much higher than that before or after the crisis. This indicates investors who enter the market before a crisis may be better off by leaving their portfolios untouched during the market downturn. In addition, the downside risk control model can detect a market downturn earlier than the mean-variance model therefore it helps to 'spread out' the required asset adjustments over a longer horizon than the crisis period itself. Keywords: financial crisis; global diversification; downside risk management; CVaR; investment turnover; portfolio analysis Citation: International Journal of Portfolio Analysis and Management, Vol. 2, No. 2 (2018) pp. 141 - 168 PubDate: 2018-06-26T23:20:50-05:00 DOI: 10.1504/IJPAM.2018.092646 Issue No:Vol. 2, No. 2 (2018)

Authors:Hanene Ben Salah, Ali Gannoun, Mathieu Ribatet Pages: 169 - 197 Abstract: The downside risk (DSR) model for portfolio optimisation allows to overcome the drawbacks of the classical mean-variance model concerning the asymmetry of returns and the risk perception of investors. This optimisation model deals with a positive definite matrix that is endogenous with respect to the portfolio weights and hence yields to a non-standard optimisation problem. In this paper we develop a new method and an algorithm to solve this optimisation problem which typically yields to a smoother portfolio frontier. Our proposal is based on non-parametric estimation, using kernel methods of mean and median. An application to the French and Brazilian stock markets is given. Keywords: downside risk; kernel method; non-parametric estimation; semivariance; portfolio allocation Citation: International Journal of Portfolio Analysis and Management, Vol. 2, No. 2 (2018) pp. 169 - 197 PubDate: 2018-06-26T23:20:50-05:00 DOI: 10.1504/IJPAM.2018.092642 Issue No:Vol. 2, No. 2 (2018)