This paper explores the effectiveness of the BJZZ algorithm, developed by Boehmer, Jones, Zhang, and Zhang (2021), in identifying and signing retail trades executed off exchanges with subpenny price improvements.
Volatility laundering causes the risk-adjusted returns and the diversification benefits of private equity to be significantly overstated. However, the problem of volatility laundering is not a problem for all private investments, specifically not for high-quality, floating rate, private credit.
Simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations.
Both investment motives and investment experience are important determinants for investors’ ability to assess (impact) investment opportunities. While investor preference can justify accepting a lower return as the cost of expressing their values, the halo effect should not play a role in making that assessment—both economic theory and empirical evidence should lead investors to expect lower returns on sustainable investments.
The following factor performance modules have been updated on our Index website.
Do-It-Yourself trend-following asset allocation weights for the Robust Asset Allocation Index are posted here. (Note: free registration required) Request a free account here if you [...]
This study is important because it provides valuable insights into the current state of financial literacy in Canada, its relationship to retirement planning, and the factors that influence financial literacy outcomes.
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.
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