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Session Overview
HH-3: Financial & Robo-Advising
Saturday, 24/Aug/2019:
11:00 - 12:30

Session Chair: Marcin Kacperczyk, Imperial College Business School
Location: D -111

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Who Benefits from Robo-advising? Evidence from Machine Learning

Alberto G Rossi1, Stephen Utkus2

1University of Maryland, United States of America; 2Vanguard, United States of America

Discussant: Christopher Hansman (Imperial College London)

We study the largest US hybrid robo-adviser by assets under management, Vanguard Personal Advisor Services (PAS). Across all clients, PAS reduces investors holdings in money market mutual funds and increases bond holdings. It reduces the holdings of individual stocks and US active mutual funds, and moves investors towards low-cost indexed mutual funds. Finally, it increases investors’ international diversification and investors’ overall risk-adjusted performance. From sign-up, it takes approximately six months for PAS to adjust investors’ portfolios to the new allocations. We use a machine learning algorithm, known as Boosted Regression Trees (BRT), to explain the cross- sectional variation in the effects of PAS on investors’ portfolio allocation and performance. The investors that benefit the most from robo-advising are the clients with little investment experience, as well as the ones that have high cash-holdings and high trading volume pre-adoption. Clients with little mutual fund holdings and clients invested in high-fee active mutual funds also display significant performance gains.

efa2019-HH-3-1366-Who Benefits from Robo-advising Evidence from Machine Learning.pdf

Assessing Risk using Self-Regulatory Organization Disclosures

Stephen Dimmock2, William Gerken1

1University of Kentucky, United States of America; 2Nanyang Technological University

Discussant: Tarik Umar (Rice University)

We examine the effect of mandatory disclosure of misconduct on reputation management by firms and individual employees. Using a panel data set of Central Registration Depository public disclosures by financial advisors, we exploit a set of NASD/FINRA rule changes that substantially increased disclosure of past misconduct. In 2004, NASD Rule 2130 (later FINRA Rule 2080) prohibited expungement of misconduct records except for allegations that are clearly spurious and required judicial approval for all expungements. We find that prior to the 2004 rule change certain information useful for predicting future misconduct was obfuscated. However, even after the 2004 rule change, the expungement process still removed valuable information, and a later rule change did little to improve the situation. Using these changes in disclosure policy, we find that advisory firms are likely to dismiss employees for misconduct only when it is disclosed. Similarly, the labor market for advisors penalizes advisors only when an incident of misconduct is publicly disclosed - not for the misconduct itself.

efa2019-HH-3-1915-Assessing Risk using Self-Regulatory Organization Disclosures.pdf

Unlocking Clients

Umit Gurun1, Noah Stoffman2, Scott Yonker3

1UT - Dallas, United States of America; 2Indiana University, United States of America; 3Cornell University, United States of America

Discussant: Ben Charoenwong (National University of Singapore)

We investigate the impact of non-compete agreements (NCAs) on the market for financial advice using variation in firm-level adoption of the Broker Protocol, which partially relaxed enforcement of NCAs. We show that while overall adviser departures do not increase, the associated costs do, as advisers move strategically to firms to which they can legally transfer client assets. When NCAs are not enforced, client fees and advisers' propensities to engage in misconduct increase, and firms appear less willing to fire advisers for misconduct. Our findings question whether the costs of ``unlocking'' clients from their advisory firms outweigh the benefits.

efa2019-HH-3-1957-Unlocking Clients.pdf

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