Artificial Intelligence: the Past, the Present and the Future
This article examines the state of Artificial Intelligence (AI). We examine its history with an eye toward what it may mean for the world in years to come.
About Elisabetta Basilico, PhD, CFA
Dr. Elisabetta Basilico is a seasoned investment professional with an expertise in "turning academic insights into investment strategies." Research is her life's work and by combing her scientific grounding in quantitative investment management with a pragmatic approach to business challenges, she’s helped several institutional investors achieve stable returns from their global wealth portfolios. Her expertise spans from asset allocation to active quantitative investment strategies. Holder of the Charter Financial Analyst since 2007 and a PhD from the University of St. Gallen in Switzerland, she has experience in teaching and research at various international universities and co-author of articles published in peer-reviewed journals. She and co-author Tommi Johnsen published a book on research-backed investment ideas, titled Smarte(er) Investing. How Academic Insights Propel the Savvy Investor. You can find additional information at Academic Insights on Investing.Does International Diversification Work?
In this article, the authors examine the research on the benefits of international diversification. Some argue that because equity markets generally crash simultaneously, there are no benefits to having equity diversification. The evidence from this paper rejects this hypothesis.
How Pervasive is Corporate Fraud?
In this article, we examine the research on the pervasiveness of corporate fraud (misconduct or alleged fraud), which is one of the (less emphasized) costs of public ownership.
Do Investors Pay Less Attention to Women (Fund Managers)? YES
This article examines the research on gender bias and fund management. Specifically, we will focus on the gender-based attention bias.
Institutional Investors and Corporate Carbon Footprint
This table of emissions and carbon intensity is relevant to the question of institutional investor influence over the carbon footprint.
Can Investors Save the Planet? Unlikely.
Can the planet earth be saved by investors? Find out what the research says!
Automation and Asset Pricing Theory
In this article about asset pricing theory, we examine the research on the impact of technological advances that displace human labor in favor of machine capital to asset pricing.
Bigger is Not Always Better in Asset Management
Pastor, Stambaugh, and Taylor (2015) and Zhu (2018) provide significant evidence of decreasing returns to scale (DRS) at both the fund and industry levels. The authors examine the robustness of their inferences after Adams, Hayunga, and Mansi (2021) critique the above two studies.
Investing in Deflation, Inflation, and Stagflation Regimes
In this article, we examine the research on investing during inflationary regimes such as deflation, inflation, and stagflation. Factors perform relatively well in all regimes on a real basis.
Does Momentum work in Option Markets?
This paper explores the question of option momentum by examining what the research says about the performance of option investments across different stocks.
The Cross Section of Stock Returns Pre-CRSP data: Value and Momentum are confirmed as robust anomalies
We study the cross-section of stock returns using a novel constructed database of U.S. stocks covering 61 years of independent data.
Are ESG strategies more effective for societal impact or performance?
In this article, we examine the research addressing the question of to what extent, if any, ESG strategies improve investment performance on a risk-adjusted basis, or if they are more effectively used for the societal impact they potentially have.
Pulling the curtain back: what do millionaires invest in?
In this article, we examine the academic research about what millionaires invest in.
Gender Pay Gap Transparency
In this article, we examine what the research says about gender pay gap transparency. We look at the research questions and academic insights with an eye toward why it matters.
ESG Ratings how do they Compare Across Data Providers?
Investments aligned with environmental, social, and governance (ESG) principles are rapidly growing globally. In the exchange traded fund (ETF) industry, this gives rise to the power of ESG rating firms that have the influence to direct capital flows into ETFs tracking the indexes. This article examines the issues of substantial ESG rating divergence across rating firms, the impact on investors’ choices, and the influence on the ETF industry. The divergence appears to be the greatest in social and governance components, and is often qualitative in nature. The author found that certain economic sectors are more prone to ESG rating divergence than others. She presents a case study about two ESG ETFs that are viewed quite differently under various rating lenses, and offers suggestions to investors, advisors, and analysts on how to research ESG ETFs, given the major rating divergence. The article concludes with ways the ETF industry could improve its practices collectively to better serve investors with clarity and to sustain the growth of ESG impact investments.
Do Equity Markets Care About Income Inequality?
Do equity markets care about income inequality? We address this question by examining equity markets’ reaction and investors’ portfolio rebalancing in response to the first-time disclosure of the ratio of CEO to median worker pay by U.S. public companies in 2018. We find that firms’ disclosing higher pay ratios experience significantly lower abnormal announcement returns. Additional evidence suggests that equity markets “dislike” high pay dispersion rather than high CEO pay or low worker pay. Firms whose shareholders are more inequality-averse experience a more pronounced negative market response to high pay ratios compared to firms with less inequality-averse shareholders. Finally, we find that during 2018 more inequality-averse investors rebalance their portfolios away from high pay ratio stocks relative to other investors. Overall, our results suggest that equity markets are concerned about high within-firm pay dispersion, and investors’ attitude towards income inequality is a channel through which high pay ratios negatively affect firm value.
Mining Credit Card Data for Stock Returns
Using a unique dataset of individual transactions-level data for a universe of U.S. consumer facing stocks, we examine the information content of consumer credit and debit card spending in explaining future stock returns. Our analysis shows that consumer spending data positively predict various measures of a company’s future earnings surprises up to three quarters in the future. This predictive power remains strong in both large- and small-cap universes of consumer discretionary firms in our sample and is robust to the type of transactions data considered (credit card, debit card, or both), although the relationship is stronger in the small-cap universe where informational asymmetries are more pronounced. Based on this empirical observation we build a simple long-short strategy that takes long/short positions in the top/bottom tercile of stocks ranked on our real-time sales signal. The strategy generates statistically and economically significant returns of 16% per annum net of transaction costs and after controlling for the common sources of systematic factor returns. A simple optimization exercise to form (tangency) mean-variance efficient portfolios of factors leads to an optimal factor allocation that assigns almost 50% weight to our long-short portfolio. Our results suggest that consumer transaction level data can serve as a more accurate and persistent signal of a firm’s growth potential and future returns.
Do Stocks Efficiently Predict Recessions?
I find that returns are predictably negative for several months after the onset of recessions, becoming high only thereafter. I identify business cycle turning points by estimating a state-space model using macroeconomic data. Conditioning on the business cycle further reveals that returns exhibit momentum in recessions, whereas in expansions they display the mild reversals expected from discount rate changes. A strategy exploiting this pattern produces positive alphas. Using analyst forecast data, I show that my findings are consistent with investors' slow reaction to recessions. When expected returns are negative, analysts are too optimistic and their downward expectation revisions are exceptionally high.
Can We Measure Inflation with Twitter
Drawing on Italian tweets, we employ textual data and machine learning techniques to build new real-time measures of consumers’ inflation expectations. First, we select keywords to identify tweets related to prices and expectations thereof. Second, we build a set of daily measures of inflation expectations around the selected tweets, combining the Latent Dirichlet Allocation (LDA) with a dictionary-based approach, using manually labeled bi-grams and tri-grams. Finally, we show that Twitter-based indicators are highly correlated with both monthly survey-based and daily market-based inflation expectations. Our new indicators anticipate consumers’ expectations, proving to be a good real-time proxy, and provide additional information beyond market-based expectations, professional forecasts, and realized inflation. The results suggest that Twitter can be a new timely source for eliciting beliefs.
Does Intangible-Adjusted Book-to-Market Work?
The book-to-market ratio has been widely used to explain the cross-sectional variation in stock returns, but the explanatory power is weaker in recent decades than in the 1970s. I argue that the deterioration is related to the growth of intangible assets unrecorded on balance sheets. An intangible-adjusted ratio, capitalizing prior expenditures to develop intangible assets internally and excluding goodwill, outperforms the original ratio significantly. The average annual return on the intangible-adjusted highminus-low (iHML) portfolio is 5.9% from July 1976 to December 2017 and 6.2% from July 1997 to December 2017, vs. 3.9% and 3.6% for an equivalent HML portfolio