For many benchmark predictor variables, short-horizon return predictability in the U.S. stock market is local in time as short periods with significant predictability (“pockets”) are interspersed with long periods with no return predictability.
Can machine-learning methods be used to predict the performance of active mutual funds, specifically in terms of alpha net of all costs? Answer: yes.
Hence, the major research question examined here is: What is the impact of industrial pollution on market pricing? Short answer: polluters have earned higher returns.
The article discusses the importance of integrating psychology into the field of financial planning and highlights the need to understand and address the psychological aspects of financial decision-making and client relationships.
Momentum investors utilize different timeframes to identify high momentum equities: past 6, 9, 12 months as an example. Obviously, there is a significant degree of overlap in momentum stocks identified across various past time frames. However, there has been little research focused on understanding the characteristics of momentum stocks formed on six and 12 months that overlap one another. The authors refer to the subset as “overlapping” stocks and suggest they constitute the largest proportion of the profitability of the momentum strategy.
The article introduces a concept called "impact elasticity," which measures how a firm's environmental impact changes in response to shifts in its cost of capital (the "E" in "ESG"). It finds that the dominant sustainable investing strategy, which favors green firms and punishes brown firms by altering their cost of capital, can be counterproductive.
When a small subset of companies makes up a large portion of a portfolio, for better or worse their returns will have a greater impact on overall portfolio results.
If inflation surges, how long on average, will it take to subside to a reasonable target rate of 2%?
This paper focuses on "organization capital," representing intangible assets in a firm's key employees that is not captured by classic value measures such as book-to-market. The authors propose a structural model to analyze the impact of organizational capital on asset prices and argue that shareholders perceive firms with high levels of organizational capital to be riskier than those with more physical capital.
The relationship between financial markets and ESG investing is obscured by the lack of clarity regarding motivations for investing in ESG strategies. Is the motive to align the investor’s values with the ESG theme? Or is the ESG term a misnomer for a set of stocks that are systematically undervalued, for some reason as a function of its ESG characteristics?
In this study, we introduce a novel measure of information content (Human-AI Differences, HAID) by exploiting the discrepancy between answers to questions at earnings calls provided by corporate executives and those given by several context-preserving Large Language Models (LLM) such as ChatGPT, Google Bard, and an open source LLM.
The Jegadeesh and Titman (1993) paper on momentum established that an equity trading strategy consisting of buying past winners and selling past losers, reliably produced risk-adjusted excess returns. The Jegadeesh results have been replicated in international markets and across asset classes. As this evidence challenged and contradicted widely accepted notions of weak-form market efficiency, the academic community took notice and started churning out research. As a result, a very large number of academic studies were published on momentum. The article summarized here has conveniently summarized 47 articles deemed as the highest quality and published in either the Journal of Finance, the Review of Financial Studies, or the Journal of Financial Economics, all three considered premier journals in the finance discipline. It is difficult to understate the importance of having a well-curated summary of momentum research. Keep it in your library.
Forbearance is important and we argue that performance evaluation should be multifaceted, akin to a Bayesian decision-maker who conducts continued due diligence and updates beliefs about returns with process information.
The implications of the competitive landscape for ETFs are mixed. On one hand, they have truly democratized investing. Investors now have access to the benefits of financial markets in one instrument that provides diversification at very low fees. Recently advertised fees on broad-based bond funds have fallen to 3bps. On the other hand, ETF providers have been able to satisfy investor demand for increasingly specialized products even though the evidence suggests they underperform. Are investors becoming worse off due to the effectiveness of the marketing strategies by providers of specialized ETFs?
The article explores the limitations of traditional country-level stock market indexes that are constructed based on the domicile of issuing firms.
Cryptocurrency and the various forms of infrastructure are currently in a stage of rapid innovation. If the manipulation embedded in PADs is widespread then the confidence in and integrity of the crypto market will suffer.
Trend follower nerd alert: This study is important because it offers a comprehensive analysis of TS momentum strategies, its unifying framework that links performance to underlying variables, and its practical implications for investors seeking to enhance their understanding of momentum investing and improve their portfolio performance.
The correlation between stocks and bonds should be a critical component of any asset allocation decision, as it impacts not only the overall risk of a diversified multi-asset class portfolio but also the risk premia one should expect to receive for taking risk in different asset classes. The problem for investors is that the correlation between stocks and bonds fluctuates extensively across time and economic regimes.
Short term return anomalies are generally dismissed in the academic literature "because they seemingly do not survive after accounting for market frictions.” In this research, short term “factors” are taken seriously and the authors argue the standard parameters may not apply for short horizons.
The findings from this Hidden Markov Model analysis provide policymakers with valuable insights into the nature and behavior of inflation regimes. This information can inform the design and implementation of monetary, fiscal, and regulatory policies to effectively manage inflation, stabilize the economy, and promote sustainable economic growth.
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