By quantifying how non-performance-based fees dominate the cost structure, this research questions whether current fee models effectively align with investor interests, which could influence future fee arrangements and industry standards.
The authors of the research discussed developed a machine learning model that can accurately predict trading volume for individual stocks. They then demonstrated how this model can be used to construct a portfolio that outperforms a traditional market-cap weighted portfolio.
If the task is to identify a firm’s true profitability, can AI outsmart seasoned analysts?
The hurdles to adding alpha for active managers are getting higher—investment practitioners make use of it as soon as or shortly after it is available.
Access to automated online credit boosts sales, transactions, and customer capital for firms, particularly in regions underserved by traditional banks.
AI-powered growth concentrates among larger firms and is associated with higher industry concentration. Our results highlight that new technologies like AI can contribute to growth and superstar firms through product innovation.
Because the Sahm rule focuses solely on the unemployment rate, caution is warranted before assuming it is signaling a recession.
How can textual analysis of business news, specifically The Wall Street Journal (WSJ), be used to measure the state of the economy?
An index-tracking approach generally lacks flexibility, which detracts from performance, leaving returns on the table. Intelligent design can overcome such issues. For example, an S&P 500 Index could choose to rebalance one month ahead of the scheduled reconstitution, minimizing the impact of reconstitution. Direct index funds are already engaging in such strategies with ETFs.
Trailing twelve-month P/E ratios account for 91% of the variation in analysts’ price targets. We construct a new kind of asset-pricing model around this fact and show that it explains the market response to earnings surprises.
While the skewness metric did demonstrate that it could select funds with managers skilled a security selection, the fund’s expenses and implementation meant that the fund was just about able to cover its expenses, and that was before the negative impact of active management on after-tax returns—and the finding was not statistically significant at even the 10% level of confidence.
The paper examines key factors that influence the performance and success of private equity investments. Specifically, it focuses on the importance of manager selection, the role of LP sophistication and skill, the relationship between fund size and performance, the potential misalignment of incentives between GPs and LPs, and the benefits and risks associated with co-investment opportunities.
This article describes digital filters derived from time series regression models that can be used as technical analysis tools. The filters are analyzed from a digital signal processing (DSP) frequency domain perspective to illustrate their properties. Example charts of the filters applied to the S&P 500 index are also included.
Retail traders are contrarian in stocks and gold, yet the same traders follow a momentum-like strategy in cryptocurrencies. The differences are not explained by individual characteristics, investor composition, inattention, differences in fees, or preference for lottery-like assets. We conjecture that retail investors have a model where cryptocurrency price changes affect the likelihood of future widespread adoption, which leads them to further update their price expectations in the same direction.
Joseph Liberman, Stanley Krasner, Nathan Sosner, and Pedro Freitas, authors of the September 2023 study “Beyond Direct Indexing: Dynamic Direct Long-Short Investing,” examined if the utilization of leverage and long-short strategies motivated by the literature on factor-based investing could improve on the tax benefits of direct indexing and tax-loss harvesting.
This paper provides new evidence on the efficacy of prioritizing transactions so as to focus portfolio turnover on the trades that offer the strongest signals and hence the highest potential performance impact.
Private debt funds are a rapidly growing segment of the private capital market. Isil Erel, Thomas Flanagan, and Michael Weisbach, authors of the April 2024 [...]
An AI analyst trained to digest corporate disclosures, industry trends, and macroeconomic indicators surpasses most analysts in stock return predictions. AI wins when information is transparent but voluminous. Humans provide significant incremental value in “Man + Machine,” which also substantially reduces extreme errors.
Evergreen funds are a relatively new concept in the private equity (PE) world compared to traditional closed-end funds. They were introduced to address the negatives of the traditional way to invest in private equity which had been in the form of partnerships.
The authors effectively argue the case for intrinsic value and DCF based approaches to building Value factor strategies. The traditional value measures, especially the book-to-market ratio, are described as ineffective in today's market environment.
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