Trading TP ₂ option violations

Quantitative Finance · 2025-08-03 · 1 citations

A Comparison of Topic Models for Financial News Headlines

The Journal of Financial Data Science · 2025-03-15

Does Overnight News Explain Overnight Returns?

SSRN Electronic Journal · 2025-01-01

Factor Model Selection Using the ICAPM

SSRN Electronic Journal · 2025-01-01

Trading TP 2 Option Violations

SSRN Electronic Journal · 2025-01-01

Importance Sampling for Latent Dirichlet Allocation

2025-12-07

Does Overnight News Explain Overnight Returns?

ArXiv.org · 2025-07-06

Over the past 30 years, nearly all the gains in the U.S. stock market have been earned overnight, while average intraday returns have been negative or flat. We find that a large part of this effect can be explained through features of intraday and overnight news. Our analysis uses a collection of 2.4 million news articles. We apply a novel technique for supervised topic analysis that selects news topics based on their ability to explain contemporaneous market returns. We find that time variation in the prevalence of news topics and differences in the responses to news topics both contribute to the difference in intraday and overnight returns. In out-of-sample tests, our approach forecasts which stocks will do particularly well overnight and particularly poorly intraday. Our approach also helps explain patterns of continuation and reversal in intraday and overnight returns. We contrast the effect of news with other mechanisms proposed in the literature to explain overnight returns.

Stress Testing Spillover Risk in Mutual Funds

Management Science · 2024-08-30 · 5 citations

We develop a framework to quantify the vulnerability of mutual funds to fire-sale spillover losses. We account for the first-mover incentive that results from the mismatch between the liquidity offered to redeeming investors and the liquidity of assets held by the funds. In our framework, the negative feedback loop between investors’ redemptions and price impact from asset sales leads to an aggregate change in funds’ net asset value, which is determined as a fixed point of a nonlinear mapping. We show that a higher concentration of first movers increases the aggregate vulnerability of the system as measured by the ratio between endogenous losses triggered by fund redemptions and exogenous losses caused by initial price shocks only. When calibrated to U.S. mutual funds, our model shows that, in stressed market scenarios, spillover losses are significantly amplified through a nonlinear response to initial shocks that results from the first-mover incentive. Higher spillover losses provide a stronger incentive to redeem early, further increasing fire-sale losses and the transmission of shocks through overlapping portfolio holdings. This paper was accepted by David Simchi-Levi, finance. Funding: The research of A. Capponi has been supported by the National Science Foundation–Civil Mechanical and Manufacturing Innovation) CAREER grant [Grant 1752326]. The research of M. H. Weber has been supported by the NUS Start-Up grant [Grant A-0004587-00-00] and the Singapore MOE Tier 1 grant [Grant A-8000966-00-00]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03443 .

Time Variation in the News–Returns Relationship—ERRATUM

Journal of Financial and Quantitative Analysis · 2024-10-04

Should Bank Stress Tests Be Fair?

Management Science · 2024-04-02

Regulatory stress tests have become one of the main tools for setting capital requirements at the largest U.S. banks. The Federal Reserve uses confidential models to evaluate bank-specific outcomes for bank-specific portfolios in shared stress scenarios. As a matter of policy, the same models are used for all banks, despite considerable heterogeneity across institutions; individual banks have contended that some models are not suited to their businesses. Motivated by this debate, we ask, what is a fair aggregation of individually tailored models into a common model? We argue that simply pooling data across banks treats banks equally but is subject to two deficiencies: it may distort the impact of legitimate portfolio features, and it is vulnerable to implicit misdirection of legitimate information to infer bank identity. We compare various notions of regression fairness to address these deficiencies, considering both forecast accuracy and equal treatment. In the setting of linear models, we argue for estimating and then discarding centered bank fixed effects as preferable to simply ignoring differences across banks. We also discuss extensions to nonlinear models. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02060 .