elisabettabasilico

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.

The Wealth-Insurance Puzzle: Rethinking Risk Coverage and Affluence

A longstanding belief in household finance is that wealthier people should buy less insurance because they can afford to self-insure. But this new research turns that idea on its head. This analysis shows that wealthier U.S. households actually purchase more life and property insurance - not less.

Why the Last Few Minutes of Trading Might Matter More Than You Think

This paper reveals a striking pattern in U.S. stock markets: the prices of individual stocks often reverse direction at the very end of the trading day. Using high-frequency data, the authors find that the last few minutes—particularly the closing auction—are dominated by large institutional flows that cause temporary price pressure. This is followed by a reversal the next day.

Do Smart Machines Make Smarter Trades?

Can machine learning models help us exploit stock market anomalies more effectively? This paper says yes—but with a few important caveats. By applying gradient boosting algorithms to a wide array of established anomalies (like value, momentum, and quality), the authors show that machine learning methods can significantly improve the performance of long-short strategies.

The Hidden Effort Problem: Work more and get better results?

Increased executive effort correlates with positive earnings surprises, higher cumulative abnormal returns post-earnings announcements, and narrower credit default swap spreads. Moreover, portfolios constructed based on changes in executive effort demonstrate significant risk-adjusted returns, underscoring the tangible value of diligent leadership.

Raising Capital from Investor Syndicates with Strategic Communication

The structure of investor syndicates—hierarchical or flat—significantly impacts the flow of information and investment decisions. In hierarchical structures, differentiated incentives can lead to persuasive cascades, while flat structures promote truthful information sharing.

Working More to Pay the Mortgage

The study examines how households adjust their labor supply in response to changes in mortgage payments due to fluctuating interest rates.

A Good Sketch is Better than a Long Speech

In the evolving landscape of financial technology, innovative methods are emerging to assess creditworthiness. One such approach involves analyzing borrowers' facial expressions during loan applications to predict delinquency risk. This study explores this novel intersection of psychology, machine learning, and finance.

How Tiny Price Differences Help Track Small Investors’ Trades

This article explains how researchers studied small investors' trading habits by looking at tiny price differences, called subpennies, in stock trades. They found that the current method to identify these trades isn't very accurate. By using a new approach, they improved the accuracy, helping to better understand how small investors buy and sell stocks.

Making Factor Strategies Work for Everyone

This article explores the difference between tradable and on-paper (theoretical) risk factors in investing. Risk factors are strategies that help explain stock market returns, but many work only in theory and not in real life.

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