FinTech
Fintech, or "financial technology", refers to firms using new technology to compete with traditional financial methods in the delivery of financial services. Artificial intelligence, blockchain, cloud computing, and big data are regarded as the "ABCDs" of 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.
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
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.
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
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.
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.
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