Editor’s Letter

To open this issue, Ennis presents an analysis of the performance of 43 of the largest individual endowments over the past 11 years and reveals that none outperformed with statistical significance, while one in four underperformed with statistical significance. Kimura, Schwaiger, Sharma, and Ang document significant spreads in style factors—value, size, quality, momentum, and low volatility—in each of the style box categories. They build multifactor portfolios within each style box, thus giving access to five style factors that can stay within a style box category, and the portfolios had information ratios of about 0.8 over June 2003 to December 2020.

Next, Bessembinder quantifies long-run stock market outcomes regarding the increases or decreases (relative to a T-bill benchmark) in shareholder wealth. Findings show that the stock market wealth creation, which is concentrated in a few top-performing firms, has increased over time and was particularly strong during the most recent three years when five firms accounted for 22% of net wealth creation. Livnat and Singh employ a time-series specific machine learning (ML) algorithm to classify future stock returns into three potential outcome categories. In addition to information about analysts’ revisions of their earnings forecasts, this study uses a signal constructed from unstructured data. Results show that the ML algorithm improves the accuracy of predictions in the three classes beyond random classification of future stock returns.

As we continue, Izadi studies a sample of ETNs that track commodity futures indices and investigates the relationship between premiums and returns for a sample of ETNs issued. This study focuses on noise trading and return predictability and their impact on the informational efficiency in the ETN markets. Ghaidarov provides a methodology for extending the application of restricted stock discount models to situations where trading restrictions are stochastic or perpetual. The model introduces a simple and robust approach for quantifying illiquidity discounts for private equity investments, which is of practical importance to valuation consultants, regulators, and risk managers.

To conclude the issue, Hambusch, Michayluk, Terhaar, and Van de Venter examine ethical decision-making related to insider trading. Using case study scenarios, they shed light on differences in evaluating the use of material nonpublic information when insider trading’s expected outcomes benefit clients versus the investment professional trading on inside information.

As always, we welcome your submissions. We value your comments and suggestions, so please email us at journals{at}investmentresearch.org.

TOPICS: Factor-based models, portfolio construction, performance measurement

Brian Bruce

Editor-in-chief

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