The role of analyst forecasts in the momentum effect

We evaluate the extent to which sell-side equity analysts can facilitate market efficiency when there is increasing uncertainty about a stock's future value. The prevalence of the 52-week-high momentum anomaly, that can be largely attributed to information uncertainty, provides a setting for examining the value and timing of analysts' earnings forecast revisions. Our study finds that analysts can provide value-relevant signals to investors by picking up indicators of momentum. The ability to identify under or over-valued stocks suggests that analysts are important information intermediaries in the price-continuation momentum effect. However, we also observe pervasive asymmetric reaction to good and bad news throughout our study that is consistent with incentive-driven reporting and optimistic biases. Nevertheless, analysts' forecast revisions are informative at different stages to re-establish stock prices back to their fundamental valuation

Sustainable IPO Proceeds' Disclosure and Survival of Companies

We examine the sustainable disclosure of IPO proceeds on 423 companies’ survival in the Malaysian market from 2000 to 2014. Using survival analysis, we find that the companies’ survival can be predicted by the proportion of IPO proceeds and their time frame, with debt repayment being the critical driver of companies’ survival. We provide empirical support for securities regulators to include strategic use and timeframe of utilization of IPO proceeds in their information disclosure requirements to protect investors’ interests and improve companies’ post-IPO survival. Keywords: Sustainable IPO Disclosure; IPO Proceeds; Survival Analysis; Malaysia eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under the responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians), and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia

Vine copulas: modelling systemic risk and enhancing higher-moment portfolio optimisation

Asymmetric dependence in equities markets has been shown to have detrimental effects on portfolio diversification as assets within the portfolio exhibit greater correlations during market downturns compared to market upturns. By applying the Clayton canonical vine copula (CVC) to model asymmetric dependence, we produce a measure of systemic risk across a portfolio of assets. In addition, we use the Clayton CVC to produce estimates of expected returns in an application to higher-moment portfolio optimisation and find evidence of an improvement in performance across a range of risk-adjusted return measures and the indices of acceptability

Diamonds vs. precious metals: what shines brightest in your investment portfolio?

Several studies explore the use of gold and other precious metals for protecting investors’ wealth during periods of market turmoil. However, alternative investments, although increasing in popularity, still remain unfamiliar to the majority of investors. We explore the safe haven and hedging properties of diamonds versus precious metals in an international study to evaluate diamonds as a viable investment alternative. Furthermore, we compare the performance between the returns of physical diamonds and diamond indices. Our analysis indicates superior performance by precious metals compared to diamonds. However, investors enjoy greater benefit from directly investing in physical diamonds rather than diamond indices. For investors looking to protect their assets against highly volatile market conditions, precious metals remain a better option. Investors should continue to keep abreast of developments with the evolution of the diamond investments industry and physical diamonds can be included in a portfolio for their downside diversification potential

Enhancing mean-variance portfolio selection by modeling distributional asymmetries

Why do mean–variance (MV) models perform so poorly? In searching for an answer to this question, we estimate expected returns by sampling from a multivariate probability model that explicitly incorporates distributional asymmetries. Specifically, our empirical analysis shows that an application of copulas using marginal models that incorporate dynamic features such as autoregression, volatility clustering, and skewness to reduce estimation error in comparison to historical sampling windows. Using these copula-based models, we find that several MV-based rules exhibit statistically significant and superior performance improvements even after accounting for transaction costs. However, we find that outperforming the naïve equally-weighted (1/N) strategy after accounting for transactions costs still remains an elusive task

The profitability of pairs training strategies: distance, cointegration and copula methods

We perform an extensive and robust study of the performance of three different pairs trading strategies—the distance, cointegration and copula methods—on the entire US equity market from 1962 to 2014 with time-varying trading costs. For the cointegration and copula methods, we design a computationally efficient two-step pairs trading strategy. In terms of economic outcomes, the distance, cointegration and copula methods show a mean monthly excess return of 91, 85 and 43 bps (38, 33 and 5 bps) before transaction costs (after transaction costs), respectively. In terms of continued profitability, from 2009, the frequency of trading opportunities via the distance and cointegration methods is reduced considerably, whereas this frequency remains stable for the copula method. Further, the copula method shows better performance for its unconverged trades compared to those of the other methods. While the liquidity factor is negatively correlated to all strategies’ returns, we find no evidence of their correlation to market excess returns. All strategies show positive and significant alphas after accounting for various risk-factors. We also find that in addition to all strategies performing better during periods of significant volatility, the cointegration method is the superior strategy during turbulent market conditions

The Strategic Allocation to Style-Integrated Portfolios of Commodity Futures

International audienceOur study lies at the intersection of the literature on the diversification benefits of commodity futures and the literature on style integration. It augments the traditional asset mix of investors with a long-short portfolio that integrates the styles that matter to the pricing of commodity futures. Treating the style-integrated portfolio of commodities as part of the strategic mix of investors is found to enhance out-of-sample performance and reduce crash risk compared to the alternatives considered thus far. The conclusion holds across traditional asset mix, portfolio allocation methods, integration strategies, and sub-periods. The diversification benefits of style integration also persist, albeit lower, in a long-only setting

The commodity risk premium and neural networks

International audienceThe paper uses linear and nonlinear predictive models to study the linkage between a set of 128 macroeconomic and financial predictors and the risk premium of commodity futures contracts. The linear models use shrinkage methods based on either naive averaging or principal components. The nonlinear models use feedforward deep neural networks (DNN) either as stand-alone or in conjunction with a long short-term memory network (LSTM). Out of the four specifications considered, the LSTM-DNN architecture best captures the risk premium, which underscores the need to estimate models that are both nonlinear and recurrent. The superior performance of the LSTM-DNN portfolio persists after accounting for transaction costs or illiquidity and is unrelated to previously-documented commodity risk factors

Canonical vine copulas in the context of modern portfolio management: are they worth it?

In the context of managing downside correlations, we examine the use of multi-dimensional elliptical and asymmetric copula models to forecast returns for portfolios with 3-12 constituents. Our analysis assumes that investors have no short-sales constraints and a utility function characterized by the minimization of Conditional Value-at-Risk (CVaR). We examine the efficient frontiers produced by each model and focus on comparing two methods for incorporating scalable asymmetric dependence structures across asset returns using the Archimedean Clayton copula in an out-of-sample, long-run multi-period setting. For portfolios of higher dimensions, we find that modeling asymmetries within the marginals and the dependence structure with the Clayton canonical vine copula (CVC) consistently produces the highest-ranked outcomes across a range of statistical and economic metrics when compared to other models incorporating elliptical or symmetric dependence structures. Accordingly, we conclude that CVC copulas are 'worth it' when managing larger portfolios