Other Insights

Postpandemic Inflation in Eleven Economies

This paper explores the applicability of the Bernanke-Blanchard (BB) model across diverse economies, revealing commonalities and differences in inflation dynamics post-pandemic.

Social Media: The Value of Seeking Alpha’s Recommendations

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.

Can Machine Learning Improve Factor Returns? Not Really

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.

Social Media, Analyst Behavior and Market Efficiency

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.  

Is Sector Neutrality in Factor Investing a Mistake?

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?"

Minimizing the Risk of Cross-Sectional Momentum Crashes

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.

Fee Variation in Private Equity

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.

Valuing Artificial Intelligence (AI) Stocks

While there is literature that describes the "domain" of artificial intelligence, there are very few, if any that analyze the valuation and pricing of AI stocks. The authors attempt to fill the void with a two part methodology.

Economic Momentum

Strong empirical evidence demonstrates 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.

Personality Differences and Investment Decision-Making

This study offers valuable information to provide insights into the underlying mechanisms driving investment behavior. For example, recognizing the impact of Neuroticism on belief formation and risk perception can help explain why some investors exhibit greater aversion to stock market volatility. Similarly, understanding how Openness influences risk preferences can shed light on why certain individuals are more willing to take investment risks than others.

Tracking Error is a Feature, Not a Bug

The benefits of diversification are well known. In fact, it’s been called the only free lunch in investing. Investors who seek to benefit from diversification of the sources of risk and return of their portfolios must accept that adding unique sources of risk means that their portfolio will inevitably experience what is called tracking error—a financial term used as a measure of the performance of a portfolio relative to the performance of a benchmark, such as the S&P 500.

Short Campaigns by Hedge Funds

Our analysis highlights the importance of short campaigns for understanding the economic impact of activist hedge funds.

Breaking Bad Momentum Trends

In their two papers, Goulding, Harvey, and Mazzoleni showed that observed market corrections and rebounds carry predictive information about subsequent returns and showed how that information could be utilized to enhance the performance of trend-following strategies by dynamically blending slow and fast momentum strategies based on four-state cycle-conditional information.

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