Academic Research Insight Archives

Investors trade Cryptos and Trad-Fi Differently

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.

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.

From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses

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.

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.

High school financial education courses in the United States

This article provides detailed insights into how high school financial education policies are implemented at the local level.

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 [...]

Transaction costs for asset allocation and foreign exchange markets

Transaction costs have a first-order effect on the performance of currency portfolios. Proportional costs based on quoted bid–ask spread are relatively small, but when a fund is large, costs due to the trading volume price impact are sizable and quickly erode returns, leaving many popular strategies unprofitable.

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.

Crypto owners know Crypto…but not finance

We find that a significant share of Canadian Bitcoin owners have low crypto knowledge and low financial literacy. We also find gender differences in crypto literacy among Bitcoin owners, with female owners scoring lower in Bitcoin knowledge than male owners.

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.

How do macroeconomic announcements impact household spending?

What are the primary factors contributing to the steep and persistent decline in U.S. consumption growth during the Great Financial Crisis of 2008-2009?

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.

How to Track Retail Investor Activity in TAQ

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.

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.

Financial literacy in Canada: Not Bad, Eh?

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.

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.

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.

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

What Changes after Women Enter Top Management Teams?

How do female appointments to top management teams (TMTs) affect a firm's approach to knowledge-related strategic renewal?

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.

12 Next

Are You Ready to Become a Better Investor?

Get Updates Now

Recent Posts

Important Disclosures