There is strong empirical evidence demonstrating that momentum (both cross-sectional and time-series) provides information on the cross-section of returns of many risk assets and has generated alpha relative to existing asset pricing models. Ma, Yang, and Ye’s findings provide another test of both robustness and pervasiveness, increasing our confidence that the findings of momentum in asset prices are not a result of data mining.
The research literature on diversity in asset management, while promising, is limited with respect to the breadth of the evidence produced to date. We don't really understand the broad-based benefits of diversity nor how diversity delivers value in asset management. How does it really work? Is it the university, the college major, gender, race, the work experience? That is where this study comes into play. The authors propose a unifying concept called homophily to analyze the impact of diversity in asset management using hedge funds as their laboratory. Sociology describes homophily as groups of people that share common characteristics such as beliefs, values, education, and so on. In a team setting those characteristics make communication and relationship formation easier. Further, a large body of research in sociology specifically documents the presence of homophily with respect to education, occupation, gender, and race. Luckily, management teams within hedge funds can be characterized by just those dimensions.
There is strong empirical evidence demonstrating that momentum (both cross-sectional and time-series) provides information on the cross-section of returns of many risk assets and has generated alpha relative to existing asset pricing models.
This paper explores the applicability of the Bernanke-Blanchard (BB) model across diverse economies, revealing commonalities and differences in inflation dynamics post-pandemic.
The finding that the recommendations from SA articles resulted in statistically significant risk-adjusted alphas (returns unexplained by conventional academic models using factors such as the market, size, value, momentum, profitability, and quality for equity portfolios) is surprising given that the empirical evidence shows how difficult it is for institutional investors such as mutual funds to show outperformance beyond the randomly expected (as can be seen in the annual SPIVA Scorecards) because of market efficiency.
Higher volatility is associated with faster, initially stronger reversals, while lower turnover is associated with more persistent, ultimately stronger reversals
To date, the best metric we have for forecasting future equity returns and the ERP is current valuations. An interesting question is whether more complicated methods using newly developed machine learning models can provide superior forecasts.
The following factor performance modules have been updated on our Index website.
How do female appointments to top management teams (TMTs) affect a firm's approach to knowledge-related strategic renewal?
Momentum continues to receive much attention from researchers because of the strong empirical evidence.
Do-It-Yourself trend-following asset allocation weights for the Robust Asset Allocation Index
Can AI models improve on the failures in predicting returns strictly from a practical point of view? In this paper, the possibilities are tested with a battery of AI models including linear regression, dimensional reduction methods, regression trees and neural networks. These machine learning models may be better equipped to address the multidimensional nature of stock returns when compared to traditional sorting and cross-sectional regressions used in factor research. The authors hope to overcome the drawbacks and confirm the results of traditional quant methods. As it turns out, those hopes are only weakly fulfilled by the MLM framework.
Hibbert, Kang, Kumar and Mishra provided us with yet another explanation: social media is providing analysts with information that reduces their forecasting errors. The result has been an increase in market efficiency, leading to a reduction in the PEAD anomaly. The bottom line is that the ability to generate alpha continues to be under assault—trying to outperform the market by stock selection is becoming even more of a loser’s game.
The implications of wishful thinking and subjective belief choice for endogenous disagreement among investors are substantial and can vary with economic conditions:
New research reveals that the performance of the hedge fund industry has not been as bad as the results from studies that relied on hedge fund data providers.
The justification for neutralizing sectors in factor strategies is a work in progress. To date, academic researchers haven't had an empirical model to mimic the impact of removing sector "effects" on the measurement and performance of factor strategies. The authors develop and test a two-component model to address the question of, "Is Sector Neutrality in Factor Investing a Mistake?"
While the empirical research on cross-sectional (long-short) momentum has shown that returns have been high, investors have also experienced huge drawdowns—momentum exhibits both high kurtosis and negative skewness. Since 1926 there have been several momentum crashes that featured short, but persistent, periods of highly negative returns. For example, from June to August 1932, the momentum portfolio lost about 91%, followed by a second drawdown from April to July 1933.
The following factor performance modules have been updated on our Index website.[ref]free access for financial professionals[/ref] Factor Performance Factor Premiums Factor Data Downloads
Given the significant growth of investment in private markets, there have been increasing demands for greater transparency in the operation and structure of private market funds. This paper aims to address questions such as whether fees are set uniformly within most funds, and if not, by how much do they vary.
Studies have found that there is a correlation between stock market downturns and an increase in hospital admissions for mental illness, an increase in domestic violence, deteriorating mental health among retirees, and increased depression rates.
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