Editor’s Letter

To begin the issue, “Richard Ennis’s Insights” discusses the latent home-equity bias and its impact on public pension funds in light of the extraordinary valuation level of the US stock market.

Next, Nanigian examines the risk-adjusted performance of actively managed mutual funds versus passively managed mutual funds. He finds no statistically significant difference in performance between the two types of funds when the passively managed funds are compared to competitively priced actively managed funds. Podkaminer, Tollette, and Siegel evaluate various options to protect portfolios against inflation. They review each option and several types of liabilities to examine their historical and prospective responses to expected and unexpected inflation. Bruno, Esakia, and Goltz construct ESG strategies shown to outperform in popular papers and assess performance benefits to investors when accounting for sector and factor exposures. They find that most outperformance can be explained by their exposure to equity style factors that are mechanically constructed from balance sheet information. They conclude that claims on ESG outperformance in popular papers are not valid.

As we continue, Aw, Jiang, and Jiang propose classification-based machine learning algorithms for factor investing with Artificial Neural Network. Findings suggest some advantages of classification-based machine learning as it reduces the effect of market noise that overwhelms the non-linear association uncovered in regression-based machine learning. Issaoui, Perchet, Retière, Soupé, Yin, and Leote de Carvalho propose a framework that enables the industrialization of highly customized tactical asset allocation portfolios from a single set of investment views. They show that robust optimization brings the level of consistency required for full automation of portfolio construction. Adopting a factor-based risk model plays a key role in providing the required transparency of the allocation process.

Next, Fulkerson, Jordan, and Travis examine the ETF creations and redemptions around price deviations and find that the expected arbitrage trades are relatively rare in a broad sample of equity index ETFs. Their results suggest that several factors may discourage the built-in ETF arbitrage mechanism and that investors may receive poorer trade execution in these conditions as a result. Chang, Krueger, and Witte analyze the impact differences in expenses and other fund characteristics have on past returns and forward-looking ratings for equity index mutual funds. The individual benefits exist, whether looking backward with Morningstar star ratings or looking forward using either analyst ratings (ARs) or algorithms based on investment “pillars,” defined as quantitative ratings (QRs). Expense ratios, loads, past performance, and size can all be used as fund selection tools to maximize investor wealth.

To conclude the issue, Frankfurter presents a commentary regarding academic publishing.

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

Brian Bruce

Editor-in-Chief

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