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.

How does inflation impact trading?

When inflation rises, trading behavior changes in systematic ways: liquidity deteriorates, bid-ask spreads widen, and investors trade less on fundamentals and more on short-term noise.

When the Machine Becomes the Portfolio Manager

Today, machines are not only processing data but interpreting narratives, forecasting returns, and constructing investment theses once reserved for humans. This paper examines how AI is reshaping the role of the discretionary PM, arguing that the edge isn’t disappearing — it’s migrating.

Thematic Investing: a Risk-Based Perspective

Candès, Hastie, Hogan, Kahn, Luo, and Spector develop a novel framework to measure whether thematic baskets capture real, coherent risks that matter for investors. Their findings challenge conventional risk models and highlight both the dangers and opportunities of betting on investment “themes.”

A TIPS Ladder Plus Stocks: Retirement Planning Solved?

A sufficient portfolio consists solely of a ladder of inflation-indexed bonds, such as U.S. Treasury Inflation-Protected Securities (TIPS), and a stock market index fund. We explain theoretically and demonstrate empirically how this strategy is less risky and more effective at maximizing lifetime retirement income than are methods commonly used by financial advisors.

Why did credit marketplaces ditch peer-to-peer?

Most platforms now intermediate—pooling loans into short-dated portfolios and, increasingly, offering bank-like products that absorb liquidity risk. Why did credit marketplaces evolve away from pure peer-to-peer? This paper quantifies the welfare value of those design choices.

Equity duration and predictability

Equity duration has increased dramatically. As firms reinvest more and delay payouts to the future, asset prices become more sensitive to changes in expected returns rather than fundamentals.

Designing Risk Scenarios

This paper rethinks how financial regulators should design stress tests. Rather than treating stress testing as a pass/fail assessment, the authors show it should be viewed as an exercise in information gathering.

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.

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