FinTech

WORKING PAPER

Anomalies With Early Exit 

KumarPrabhalaRanjan

Profitability, value, and momentum strategies produce significant and robust profits over 50 years in large capitalization stocks and value-weighted portfolios when using machine-learning-determined strategies to exit trades.

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PUBLISHED PAPERS

The Relationship Dilemma: Why Do Banks Differ in the Pace at Which They Adopt New Technology?

​Authored by Prachi Mishra, Nagpurnanand Prabhala, and Raghuram G. Rajan

The Review of Financial Studies 35 (2022) 3418–3466

© The Author(s) 2021. Published by Oxford University Press on behalf of The Society for Financial Studies.

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New tech or practices are often beneficial to businesses. Why then are some institutions slow to adopt them? Stickiness – habits -- created by past practices inhibit the adoption of better ones today.

Context, Language Modeling, and Multimodal Data in Finance

Authored by Sanjiv Das, Connor Goggins, John He, George Karypis, Sandeep Krishnamurthy, Mitali Mahajan, Nagpurnanand Prabhala, Dylan Slack, Rob van Dusen, Shenghua Yue, Sheng Zha, and Shuai Zheng

​ContextMultimodalLearning

The Journal of Financial Data Science, Summer 2021

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Pre-trained language models show materially improved performance when enhanced with contextual SEC filings data. Using context-enhanced language models also dominates predictions from the standard dictionary-based approaches used in finance. 

The Promises and Pitfalls of Robo-Advising

Authored by Francesco D’Acunto, Nagpurnanand Prabhala, and Alberto G. Rossi

DacuntoPrabhalaRossi

The Review of Financial Studies , May 2019, Vol. 32, No. 5, Special Issue: To FinTech and Beyond (May 2019), pp. 1983-2020

Adopters of a robo-advisor become more attentive to investing and have better-diversified portfolios with less volatility and better returns. More interestingly, adopters exhibit declines in behavioral biases, including the disposition, trend-chasing, and rank effect.

Venture Capital Communities

Authored by Amit Bubna, Sanjiv R. Das, and Nagpurnanand Prabhala

BubnaDasPrabhala

JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS Vol. 55, No. 2, Mar. 2020, pp. 621–651

COPYRIGHT 2019, MICHAEL G. FOSTER SCHOOL OF BUSINESS, UNIVERSITY OF WASHINGTON, SEATTLE, WA 98195

doi:10.1017/S002210901900005X

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Using computational methods from the physical sciences, we show that VCs self-organize into “communities,” or non-exclusionary groups with fuzzy boundaries. The three communities in the data are roughly ordered by their age and reach, with members similar to each other in age, connectedness, and functional style. Firms funded by community VCs exit faster and have more innovation, especially when they are early-stage firms without an innovation history.

Product Market Threats, Payouts, and Financial Flexibility

Authored by Gerard Hoberg, Gordon Phillips, and Nagpurnanand Prabhala

HobergPrabhalaPhillipsJF

The Journal of Finance , FEBRUARY 2014, Vol. 69, No. 1 (FEBRUARY 2014), pp.

293-324 

The paper develops a new measure of competitive threats faced by firms called “fluidity” from textual data in annual corporate filings. Fluidity explains key financial policies of firms, specifically what cash is held and how much is paid out as dividends or repurchases. 

Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending

Authored by Mingfeng Lin, Nagpurnanand R. Prabhala, and Siva Viswanathan

LinPrabhalaViswanathan

Management Science , January 2013, Vol. 59, No. 1 (January 2013), pp. 17-35

Small retail investors who lend in peer-to-peer (P2P) lending markets show a significant – and perhaps surprising -- degree of sophistication. Loan funding probabilities, credit spreads, and ex-post default rates disentangle and reflect in reasonable ways the “soft” credit information implied by friendship networks.