Research
Measuring the Economic and Academic Impact of Philanthropic Funding: The Breast Cancer Research Foundation
Vasileva, Detelina, Larry Norton, Marc Hurlbert, and Andrew W. Lo (2022), Measuring the Economic and Academic Impact of Philanthropic Funding: The Breast Cancer Research Foundation, Journal of Investment Management 20 (1), 5-24.
View abstract
Hide abstract
Using survey data gathered from grantees of the nonprofit Breast Cancer Research Foundation (BCRF), we investigated the commercial and non-commercial impacts of their research funding. We found significant impact in both domains. Commercially, 19.5% of BCRF grantees filed patents, 35.9% had a project that has reached clinical development, and 12 companies have or will be spun off from existing projects, thus creating 127 new jobs. Non-commercially, 441 graduate students have been trained by 116 grantees, 767 postdoctoral fellows have been trained by 137 grantees, 66% of grantees have used funding for faculty salaries, 93% have achieved collaboration with other researchers, and 42.7% have enacted process improvements in research methodology. Econometric analysis identifies BCRF funding and associated process improvements as key factors associated with the likelihood to file patents. However, we also found that the involvement of more than one institution in a collaborative project had a negative impact on subsequent development. This may point to frictions introduced by multi-university interactions.
Lo, Andrew W., and Ruixun Zhang (2024), Quantifying the Impact of Impact Investing, Management Science 70 (10), 7161-7186.
View abstract
Hide abstract
We propose a quantitative framework for assessing the financial impact of any form of impact investing, including socially responsible investing; environmental, social, and governance (ESG) objectives; and other nonfinancial investment criteria. We derive conditions under which impact investing detracts from, improves on, or is neutral to the performance of traditional mean-variance optimal portfolios, which depends on whether the correlations between the impact factor and unobserved excess returns are negative, positive, or zero, respectively. Using Treynor–Black portfolios to maximize the risk- adjusted returns of impact portfolios, we derive an explicit and easily computable measure of the financial reward or cost of impact investing as compared with passive index bench-marks. We illustrate our approach with applications to biotech venture philanthropy, a semiconductor research and development consortium, divesting from “sin” stocks, ESG investments, and “meme” stock rallies such as GameStop in 2021.
Davis, Randall, Andrew W. Lo, Sudhanshu Mishra, Arash Nourian, Manish Singh, Nicholas Wu, and Ruixun Zhang (2023), Explainable Machine Learning Models of Consumer Credit Risk, The Journal of Financial Data Science 5 (4), 9–39.
View abstract
Hide abstract
In this work, the authors create machine learning (ML) models to forecast home equity credit risk for individuals using a real-world dataset and demonstrate methods to explain the output of these ML models to make them more accessible to the end user. They analyze the explainability for various stakeholders: loan companies, regulators, loan applicants, and data scientists, incorporating their different requirements with respect to explanations. For loan companies, they generate explanations for every model prediction of creditworthiness. For regulators, they perform a stress test for extreme scenarios. For loan applicants, they generate diverse counterfactuals to guide them with steps toward a favorable classification from the model. Finally, for data scientists, they generate simple rules that accurately explain 70%–72% of the dataset. Their study provides a synthesized ML explanation framework for all stakeholders and is intended to accelerate the adoption of ML techniques in domains that would benefit from explanations of their predictions.
Bandi, Federico M., Shomesh E. Chaudhuri, Andrew W. Lo, Andrea Tamoni (2021), Spectral factor models, Journal of Financial Economics 142, 214-238.
View abstract
Hide abstract
We represent risk factors as sums of orthogonal components capturing fluctuations with cycles of different length. The representation leads to novel spectral factor models in which systematic risk is allowed—without being forced—to vary across frequencies. Frequency- specific systematic risk is captured by a notion of spectral beta. We show that traditional factor models restrict the spectral betas to be constant across frequencies. The restriction can hide horizon-specific pricing effects that spectral factor models are designed to re- veal. We illustrate how the methods may lead to economically meaningful dimensionality reduction in the factor space.
Siah, Kien Wei, Qingyang Xu, Kirk Tanner, Olga Futer, John J. Frishkopf, and Andrew W. Lo (2021), Accelerating glioblastoma therapeutics via venture philanthropy, Drug Discovery Today 26 (7), 1744-1749. https://doi.org/10.1016/j.drudis.2021.03.020.
View abstract
Hide abstract
Development of curative treatments for glioblastoma (GBM) has been stagnant in recent decades largely because of significant financial risks. A portfolio-based strategy for the parallel discovery of breakthrough therapies can effectively reduce the financial risks of potentially transformative clinical trials for GBM. Using estimates from domain experts at the National Brain Tumor Society (NBTS), we analyze the performance of a portfolio of 20 assets being developed for GBM, diversified across different development phases and therapeutic mechanisms. We find that the portfolio generates a 14.9% expected annualized rate of return. By incorporating the adaptive trial platform GBM AGILE in our simulations, we show that at least one drug candidate in the portfolio will receive US Food and Drug Administration (FDA) approval with a probability of 79.0% in the next decade.
Lo, Andrew W., and Alexander Remorov (2021), Algorithmic Models of Investor Behavior, Journal of Systematic Investing 1 (1), 1-29.
View abstract
Hide abstract
We propose a heuristic approach to modeling investor behavior by simulating combinations of simpler systematic investment strategies associated with well-known behavioral biases—in functional forms motivated by an extensive review of the behavioral finance literature—using parameters calibrated from historical data. We compute the investment performance of these heuristics individually and in pairwise combinations using both simulated and historical asset-class returns. The mean-reversion or momentum nature of a heuristic can often explain its effect on performance, depending on whether asset returns are consistent with such dynamics. These algorithms show that seemingly irrational investor behavior may, in fact, have been shaped by evolutionary forces and can be effective in certain environments and maladaptive in others.
Life sciences intellectual property licensing at the Massachusetts Institute of Technology
Huang, Samuel, Kien Wei Siah, Detelina Vasileva, Shirley Chen, Lita Nelsen & Andrew W. Lo (2021), Life sciences intellectual property licensing at the Massachusetts Institute of Technology, Nature Biotechnology 39, 293–301 .
View abstract
Hide abstract
Academic institutions play a central role in the biotech industry through technology licensing and the creation of startups, but few data are available on their performance and the magnitude of their impact. Here we present a systematic study of technology licensing by one such institution, the Massachusetts Institute of Technology (MIT). Using data on the 76 therapeutics-focused life sciences companies formed through MIT’s Technology Licensing Office from 1983 to 2017, we construct several measures of impact, including MIT patents cited in the Orange Book, capital raised, outcomes from mergers and acquisitions, patents granted to MIT intellectual property licensees, drug candidates discovered and US drug approvals—a key benchmark of innovation in the biopharmaceutical industry. As of December 2017, Orange Book listings for four approved small-molecule drugs cite MIT patents, but another 31 FDA-approved drugs (excluding candidates acquired after phase 3) had some involvement of MIT licensees. Fifty-five percent of the latter were either a new molecular entity or a new biological entity, and 55% were granted priority review, an indication that they address an unmet medical need. The methodology described here may be a useful framework for other academic institutions to track outcomes of intellectual property in the therapeutics domain.
Parkinson’s Patients’ Tolerance for Risk and Willingness to Wait for Potential Benefits of Novel Neurostimulation Devices: A Patient-Centered Threshold Technique Study
Hauber, Brett, Brennan Mange, Mo Zhou, Shomesh Chaudhuri, Heather L. Benz, Brittany Caldwell, John P. Ruiz, Anindita Saha, Martin Ho, Stephanie Christopher, Dawn Bardot, Margaret Sheehan, Anne Donnelly, Lauren McLaughlin, Katrina Gwinn, Andrew Lo, and Murray Sheldon (2021), Parkinson's Patients' Tolerance for Risk and Willingness to Wait for Potential Benefits of Novel Neurostimulation Devices: A Patient-Centered Threshold Technique Study, MDM Policy & Practice 6 (1), https://doi.org/10.1177/2381468320978407.
View abstract
Hide abstract
BACKGROUND: Parkinson’s disease (PD) is neurodegenerative, causing motor, cognitive, psychological, somatic, and autonomic symptoms. Understanding PD patients’ preferences for novel neurostimulation devices may help ensure that devices are delivered in a timely manner with the appropriate level of evidence. Our objective was to elicit preferences and willingness-to-wait for novel neurostimulation devices among PD patients to inform a model of optimal trial design.
METHODS: We developed and administered a survey to PD patients to quantify the maximum levels of risks that patients would accept to achieve potential benefits of a neurostimulation device. Threshold technique was used to quantify patients’ risk thresholds for new or worsening depression or anxiety, brain bleed, or death in exchange for improvements in “on-time,” motor symptoms, pain, cognition, and pill burden. The survey elicited patients’ willingness to wait to receive treatment benefit. Patients were recruited through Fox Insight, an online PD observational study.
RESULTS: A total of 2740 patients were included and a majority were White (94.6%) and had a 4-year college degree (69.8%). Risk thresholds increased as benefits increased. Threshold for depression or anxiety was substantially higher than threshold for brain bleed or death. Patient age, ambulation, and prior neurostimulation experience influenced risk tolerance. Patients were willing to wait an average of 4 to 13 years for devices that provide different levels of benefit.
CONCLUSIONS: PD patients are willing to accept substantial risks to improve symptoms. Preferences are heterogeneous and depend on treatment benefit and patient characteristics. The results of this study may be useful in informing review of device applications and other regulatory decisions and will be input into a model of optimal trial design for neurostimulation devices.
A Cost/Benefit Analysis of Clinical Trial Designs for COVID-19 Vaccine Candidates
Berry, Donald, Scott Berry, Peter Hale, Leah Isakov, Andrew W. Lo, Kien Wei Siah, Chi Heem Wong (2020), A Cost/Benefit Analysis of Clinical Trial Designs for COVID-19 Vaccine Candidates, PLoS ONE 15(12): e0244418.
View abstract
Hide abstract
We compare and contrast the expected duration and number of infections and deaths averted among several designs for clinical trials of COVID-19 vaccine candidates, including traditional and adaptive randomized clinical trials and human challenge trials. Using epidemiological models calibrated to the current pandemic, we simulate the time course of each clinical trial design for 756 unique combinations of parameters, allowing us to determine which trial design is most effective for a given scenario. A human challenge trial provides maximal net benefits—averting an additional 1.1M infections and 8,000 deaths in the U.S. compared to the next best clinical trial design—if its set-up time is short or the pandemic spreads slowly. In most of the other cases, an adaptive trial provides greater net benefits.
Chaudhuri, Shomesh E., and Andrew W. Lo (2024),Financially adaptive clinical trials via option pricing analysis, Journal of Econometrics 240 (2), 105026. https://doi.org/10.1016/j.jeconom.2020.08.012.
View abstract
Hide abstract
The regulatory approval process for new therapies involves costly clinical trials that can span multiple years. When valuing a candidate therapy from a financial perspective, industry sponsors may terminate a program early if clinical evidence suggests market prospects are not as favorable as originally forecasted. Intuition suggests that clinical trials that can be modified as new data are observed, i.e., adaptive trials, are more valuable than trials without this flexibility. To quantify this value, we propose modeling the accrual of information in a clinical trial as a sequence of real options, allowing us to systematically design early-stopping decision boundaries that maximize the economic value to the sponsor. In an empirical analysis of selected disease areas, we find that when a therapy is ineffective, our adaptive financing method can decrease the expected cost incurred by the sponsor in terms of total expenditures, number of patients, and trial length by up to 46%. Moreover, by amortizing the large fixed costs associated with a clinical trial over time, financing these projects becomes less risky, resulting in lower costs of capital and larger valuations when the therapy is effective.