A comparison of minimum variance and maximum Sharpe ratio portfolios for mainstream investors

PurposeMotivated by the recent theoretical rehabilitation of mean-variance analysis, the authors revisit the question of whether minimum variance (MinVar) or maximum Sharpe ratio (MaxSR) investment weights are preferable in practical portfolio formation.Design/methodology/approachThe authors answer this question with a focus on mainstream investors which can be modeled by a preference for simple portfolio optimization techniques, a tendency to cling to past asset characteristics and a strong interest in index products. Specifically, in a rolling-window approach, the study compares the out-of-sample performance of MinVar and MaxSR portfolios in two asset universes covering multiple asset classes (via investable indices and their subindices) and for two popular input estimation methods (full covariance and single-index model).FindingsThe authors find that, regardless of the setting, there is no statistically significant difference between MinVar and MaxSR portfolio performance. Thus, the choice of approach does not matter for mainstream investors. In addition, the analysis reveals that, contrary to previous research, using a single-index model does not necessarily improve out-of-sample Sharpe ratios.Originality/valueThe study is the first to provide an in-depth comparison of MinVar and MaxSR returns which considers (1) multiple asset classes, (2) a single-index model and (3) state-of-the-art bootstrap performance tests.

Simulating Multi-Asset Classes Prices Using Wasserstein Generative Adversarial Network: A Study of Stocks, Futures and Cryptocurrency

Financial data are expensive and highly sensitive with limited access. We aim to generate abundant datasets given the original prices while preserving the original statistical features. We introduce the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) into the field of the stock market, futures market and cryptocurrency market. We train our model on various datasets, including the Hong Kong stock market, Hang Seng Index Composite stocks, precious metal futures contracts listed on the Chicago Mercantile Exchange and Japan Exchange Group, and cryptocurrency spots and perpetual contracts on Binance at various minute-level intervals. We quantify the difference of generated results (836,280 data points) and original data by MAE, MSE, RMSE and K-S distances. Results show that WGAN-GP can simulate assets prices and show the potential of a market simulator for trading analysis. We might be the first to look into multi-asset classes in a systematic approach with minute intervals across stocks, futures and cryptocurrency markets. We also contribute to quantitative analysis methodology for generated and original price data quality.

Dynamic Conditional Bias-Adjusted Carry Cost Rate Futures Hedge Ratios

This paper proposes new dynamic conditional futures hedge ratios and compares their hedging performances along with those of common benchmark hedge ratios across three broad asset classes. Three of the hedge ratios are based on the upward-biased carry cost rate hedge ratio, where each is augmented in a different bias-mitigating way. The carry cost rate hedge ratio augmented with the dynamic conditional correlation between spot and futures price changes generally: (1) provides the highest hedging effectiveness and (2) has a statistically significantly higher hedging effectiveness than the other hedge ratios across assets, sub-periods, and rolling window sizes.

Subjective Return Expectations, Perceptions, and Portfolio Choice

Exploiting a representative sample of the French population by age, wealth, and asset classes, we document novel facts about their expectations and perceptions of stock market returns. Both expectations and perceptions of returns are very dispersed, significantly lower than their data counterparts, and a substantial portion of the variation in the former is explained by dispersion in the latter. Consistent with portfolio choice models under incomplete information, a conditional risk-return trade-off explains the intensive margin, while at the extensive margin, only expected returns matter. Despite accounting for survey measurement error in subjective return expectations, ’muted sensitivities’ at both portfolio choice margins obtain, getting consistently (i) bigger when excluding informed non-participants, and (ii) smaller, for inertial and professionally delegated portfolios.

Catastrophe bond market is set for strong growth

Significance As an alternative source of capital to traditional reinsurance, catastrophe (cat) bond issuance, a securitised type of insurance against catastrophe-linked losses, is reaching new highs. In the current low interest rate environment, there has been strong investor demand for these bonds. Impacts As natural disasters increase, the chance of a catastrophe occurring in these bonds' three-to-five-year lifespan rises, weighing on returns. If the number of natural disasters with a global impact rises, cat bond returns may become more correlated with other asset classes. More catastrophe bonds that meet ESG criteria are likely to be issued.

Systematic risk in the biopharmaceutical sector: a multiscale approach

It is well documented that the biopharmaceutical sector has exhibited weak financial returns, contributing to underinvestment. Innovations in the industry carry high risks; however, an analysis of systematic risk and return compared to other asset classes is missing. This paper investigates the time–frequency interconnectedness between stocks in the biotech sector and ten asset classes using daily cross-country data from 1995 to 2019. We capture investors' heterogeneous investment horizons by decomposing time series according to frequencies. Using a maximal overlap discrete wavelet transform (MODWT) and a dynamic conditional correlation (DCC)-Student-t copula, diversification potentials are revealed, helping investors to reap the benefits of investing in biotech. Our findings indicate that the underlying assets exhibit nonlinear asymmetric behavior that strengthens during periods of turmoil.

How Many Stocks Are Sufficient for Equity Portfolio Diversification? A Review of the Literature

Using extensive and comprehensive databases to select a subset of research papers, we aim to critically analyze previous empirical studies to identify certain patterns in determining the optimal number of stocks in well-diversified portfolios in different markets, and to compare how the optimal number of stocks has changed over different periods and how it has been affected by market turmoil such as the Global Financial Crisis (GFC) and the current COVID-19 pandemic. The main methods used are bibliometric analysis and systematic literature review. Evaluating the number of assets which lead to optimal diversification is not an easy task as it is impacted by a huge number of different factors: the way systematic risk is measured, the investment universe (size, asset classes and features of the asset classes), the investor’s characteristics, the change over time of the asset features, the model adopted to measure diversification (i.e., equally weighted versus optimal allocation), the frequency of the data that is being used, together with the time horizon, conditions in the market that the study refers to, etc. Our paper provides additional support for the fact that (1) a generalized optimal number of stocks that constitute a well-diversified portfolio does not exist for whichever market, period or investor. Recent studies further suggest that (2) the size of a well-diversified portfolio is larger today than in the past, (3) this number is lower in emerging markets compared to developed financial markets, (4) the higher the stock correlations with the market, the lower the number of stocks required for a well-diversified portfolio for individual investors, and (5) machine learning methods could potentially improve the investment decision process. Our results could be helpful to private and institutional investors in constructing and managing their portfolios and provide a framework for future research.

Bursts and Regularization in Financial Markets: Deterministic or Stochastic?

We analyze empirical finance data, such as the Financial Stress Index, a number of asset classes (swaps, equity and bonds), market (emerging vs. developed), issuer (corporate vs. government bond), maturity (short vs. long) data, asking whether the recently observed alternations between calm periods and financial turmoil can be modelled in a low-dimensional deterministic manner, or whether they demand for their description a stochastic model. We find that a deterministic model performs at least as well as one of the best stochastic models, but may provide additional insight into the essential mechanisms that drive financial markets.

Regulatory Treatment of Changes in Fair Value and the Composition of Banks' Investment Portfolios

In the wake of the financial crisis of 2007-2008, Basel III recommended that bank regulators include changes in the fair value of available-for-sale (AFS) debt securities in Tier 1 capital. However, the U.S. implementation allowed smaller banks to continue excluding these changes through a one-time opt out election. This paper investigates a potential impact of this opt out provision by examining the investment decisions of smaller banks in the 1990's when changes in the fair value of AFS debt securities were temporarily included in regulatory capital. Using a sample of smaller banks and a difference-in-differences research design, we find that low-capitalized banks reduced their investments in more volatile asset classes (e.g., corporate bonds, non-agency MBS) and increased their investments in less volatile asset classes (e.g., treasuries and municipal bonds) after changes in fair value were included in regulatory capital. These findings suggest that providing smaller banks with an opt out election potentially allows low-capitalized, riskier banks to continue to hold more volatile securities in their AFS portfolios.