Robert B. Willumstad School of Business • Adelphi University, New York
Bold New Thinking

Fall 2025/Issue 9

Dear Friends and Colleagues,

I am delighted to share our latest edition of the Adelphi Business Review. This year, we feature four journal articles that were recognized by the Robert B. Willumstad School of Business through the Bender Award program and one additional paper by Professor Robert Goldberg, who was recently recognized as a Best Undergraduate Business Professor by Poets&Quants. Our winning “Corporate Social Responsibility Outstanding Paper,” by Zahra Sedighi-Maman, PhD, focuses on using machine learning models to predict adverse drug reactions. This research supports the Willumstad School’s focus on reducing inequalities by contributing to safer prescription drug use for all patients, including vulnerable groups, through more efficient and accessible tools for monitoring adverse drug reactions. Read more about her study, along with our other featured papers below.

Adverse drug reactions (ADRs) are costly and risky to monitor using traditional methods. This study builds machine-learning models that combine readily available demographic data (e.g., age, gender) with nonclinical drug attributes (chemical, molecular, biological) to predict 30 common, severe ADRs reported via the FDA Adverse Event Reporting System database, 2012–2023). A streamlined deep learning model using 20 top predictors delivers performance comparable to a 2,315-feature model. The result is a parsimonious, scalable approach that can lower monitoring costs, spotlight high-risk patient-drug profiles, and guide safer trial design, labeling and post-market surveillance.

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This study examines how traditional gasoline vehicle (GV) automakers transition toward electric vehicle (EV) production under different government subsidy schemes. Using a game-theoretic model that incorporates production costs, charging station efficiency and product competition, the paper analyzes a monopolistic automaker’s decisions to enter or exit the EV and GV markets under four policy settings: no subsidy, purchase subsidy, station subsidy and a combination of both. Results reveal that while both purchase and station subsidies promote EV market entry, neither sufficiently motivates automakers to fully abandon GV production.

The study provides strategic insights for automakers’ electrification planning and offers policymakers guidance on designing effective subsidy mechanisms to accelerate the transition toward sustainable mobility.

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This study was motivated by a stock trader who conducts delta hedging to ensure his portfolio of assets grows at the risk-free interest rate. To implement delta hedging, the trader must estimate “gamma,” which is the second derivative of the value of a stock option as a function of the underlying stock price. This work suggests a practical and efficient way to estimate his gamma.

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Principal component analysis (PCA) is widely used for dimensionality reduction but often lacks interpretability when applied to large-scale data. This study addresses this issue by proposing a least angle sparse principal component analysis (LA-SPCA) method that efficiently identifies sparse principal components by minimizing the angle to their counterparts from ordinary PCA. This transforms the nonconvex optimization problem of traditional SPCA into a polynomial-time process while preserving interpretability and explained variance. Experimental results demonstrate that LA-SPCA offers an effective and computationally efficient solution for analyzing ultrahigh-dimensional data.

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Treasury bond yields include an adjustment to the long-term expectation of short-term rates. This adjustment, known as the term premium, can be positive or negative depending on market conditions.

This article models and demonstrates the impact of the magnitude and sign of the interest-rate term premium on optimal asset allocation, uses survey data to show the occurrence of a negative-term premium, and models the relationship between the sign of the term premium and the sign of the covariance between Treasury bonds and equity returns. It also explores how the sign of the term premium is driven by the competing influences of the diversification benefit and the need to be compensated for bearing the systematic risk of volatile rates. Modeling this relationship is the focus of Professor Goldberg's ongoing research.

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