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The pupose of the paper is to study the usefulness of Piotroski (2000)'s F-score in separating winners and losers in Vietnam.

The authors adopt a portfolio analysis and regression analysis on a sample of 501 of listed firms between 2009 and 2019 in Vietnam.

The authors find that a hedge strategy that buys high-F-score firms and sells low-F-score firms yield market-adjusted return of over 30 percent annually, which is statistically and economically significant. The hedge strategy based on F-score is not only profitable for value (high book-to-market [BM]) firms but also earn abnormal returns in a sample of growth (low BM) firms, suggesting that the usefulness of F-score strategy is not just a phenomenon in value firms as documented in previous literature.

Whilst the authors' paper documents economically significant returns obtained from the F-score strategy, the authors do not examine what drives the abnormal returns.

The results provide supporting evidence for the use of financial statement analysis as a screening tool to improve the performance of value investment in Vietnam stock market and for the training of financial reporting and fundamental analysis in universities.

The authors' research is the first study examining the F-score strategy in Vietnam that provides insights about the usefulness of fundamental analysis in separating winners and losers in a frontier market and contributes to the literature on fundamental analysis and market efficiency in emerging and frontier markets.

In asset management, what if clients want to purchase protection from risk factors, under the form of variance risk premia. This paper aims to address this topic by developing a portfolio optimization framework based on the criterion of the minimum variance risk premium (VRP) for any investor selecting stocks with an expected target return while minimizing the risk aversion associated to the portfolio according to “good” and “bad” times.

To accomplish this portfolio selection problem, the authors compute variance risk-premium as the difference from high-frequencies' realized volatility and options' implied volatility stemming from 19 stock markets, estimate a 2-state Markov-switching model on the variance risk-premia and optimize variance risk-premia portfolios across non-overlapping regions. The period goes from March 16, 2011, to March 28, 2018.

The authors find that optimized portfolios based on variance-covariance matrices stemming from VRP do not consistently outperform the benchmark based on daily returns. Several robustness checks are investigated by minimizing historical, realized or implicit variances, with/without regime switching. In a boundary case, accounting for the realized variance risk factor in portfolio decisions can be seen as a promising alternative from a portfolio performance perspective.

As a new management “style”, the realized volatility approach can, therefore, bring incremental value to construct the conditional covariance matrix estimates.

The authors assess the portfolio performance determined by the variance-covariance matrices that are derived by four models: “naive” (Markowitz returns benchmark), non-switching VRP, maximum likelihood regime-switching VRP and Bayesian regime switching VRP. The authors examine the best return-risk combination through the calculation of the Sharpe ratio. They also assess another different portfolio strategy: the risk parity approach.