Stocks aren’t always the best in the long-run
Advisors and managers will have to adopt a more nuanced view of risk as recognition of the frequency of equity underperformance becomes widespread.
About Tommi Johnsen, PhD
Tommi Johnsen is the former Director of the Reiman School of Finance and an Emeritus Professor at the Daniels College of Business at the University of Denver. She has worked extensively as a research consultant and investment advisor for institutional investors and wealth managers in quantitative methods and portfolio construction. She taught at the graduate and undergraduate levels and published research in several areas including: capital markets, portfolio management and performance analysis, financial applications of econometrics and the analysis of equity securities. In 2019, Dr. Johnsen published “Smarter Investing” with Palgrave/Macmillan, a top 10 in business book sales for the publisher. She received her Ph.D. from the University of Colorado at Boulder, with a major field of study in Investments and a minor in Econometrics. Currently, Dr. Johnsen is a consultant to wealthy families/individuals, asset managers, and wealth managers.Do sell-side analysts say “buy” while whispering “sell”?
Managers are more likely to vote for analysts who exhibit greater “say-buy/whisper-sell” behavior toward these man agers. This suggests that analysts reduce the accuracy of their public recommendations, thereby maintaining the value of their private advice to funds.
Frog in the Pan Momentum: International Evidence
Underreaction to continuous news plays a key role in generating momentum internationally.
Factors are global, respectable and repeatable
The propagation of factors actually reflect valid characteristics of the markets and market fluctuations.
Rethinking Asset Growth in Asset Pricing Models
Measures of asset growth add considerable explanatory power to asset pricing models, but wait, there’s a twist. The formulation for measuring asset growth in risk [...]
Can Artificial Intelligence outsmart seasoned equity analysts?
If the task is to identify a firm’s true profitability, can AI outsmart seasoned analysts?
Artificial intelligence, firm growth, and product innovation
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.
Analysts set price targets using trailing P/E ratios
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.
Can smart rebalancing improve factor portfolios?
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.
Fixing the poor performance of the book-to-market ratio
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.
Factors and Taxes
As a result of the trading required to capture the premiums that drive factor strategies investors may face significant tax liabilities. The challenge for the [...]
Using Bayesian Solutions to Resolve the Factor Zoo
We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high-dimensional problems.
Overconfidence May Lead to Poor Emergency Planning
How does the perception of the need to hold emergency cash relate to overconfidence in one's degree of financial literacy? The answer is surprising.
Creating Better Factor Portfolio via AI
Trading costs, discontinuous trading, missed trades, and other frictions, along with asset management fees can cause a shortfall between live and paper portfolios. The focus of this paper is to test an effective rebalancing method that prioritizes trades with the strongest signals to capture more of the factor premia while reducing turnover and trading costs.
Complexity is a virtue in return prediction
Simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations.
Does Diversity add value to asset management?
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.
How Volatility and Turnover Affect Return Reversals
Higher volatility is associated with faster, initially stronger reversals, while lower turnover is associated with more persistent, ultimately stronger reversals
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.
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?"
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.