Back to members

Martin Szydlowski

Institution

HKUST

PhD Year

2013

mszydlowski@ust.hk

FTG Membership

Member

Website

https://sites.google.com/site/martinszydl/home

Featured Work

Sep 16, 2025

Snehal Banerjee, Martin Szydlowski

Trading against Algorithms: Price Dynamics and Risk-sharing in a Market with Q-learners

We study pricing dynamics and risk-sharing in a market with rational investors and a Q-learning trader. The Q-learner’s trading generates a feedback loop in prices: their demand for the risky security depends on their perceived benefit from trading, which in turn, depends on realized returns. We show that this loop generates state-dependent stochastic volatility, predictable returns, and novel price dynamics which depend on the mass...

Sep 22, 2020

Martin Szydlowski | Working Paper No. 00045-01

Monitor Reputation and Transparency

We study the disclosure policy of a regulator overseeing a monitor with reputation
concerns, such as a bank or an auditor. The monitor oversees a manager, who chooses
how much to manipulate given the monitor's reputation. Reputational incentives are
strongest for intermediate reputations and uncertainty about the monitor is valuable.
Instead of providing transparency, the regulator's disclosure keeps the monitor's reputation

Sep 21, 2018

Martin Szydlowski | Working Paper No. 00046-00

The Market for Conflicted Advice

We present a model of the market for advice in which advisers have conflicts of
interest and compete for heterogeneous customers through information provision. The
competitive equilibrium features information dispersion and partial disclosure. While
conflicted fees lead to distorted information, they are irrelevant for customers’ welfare:
banning conflicted fees only improves the information quality, not customers’ welfare.
Instead, financial literacy...

Aug 18, 2018

Martin Szydlowski

Monitor Reputation and Transparency

We study the disclosure policy of a regulator who oversees a monitor with reputation concerns. The monitor faces a strategic agent, who chooses how much to manipulate in response to the monitor’s reputation. Manipulation increases the arrival rate of a “bad news” signal, but the agent manipulates less for higher reputations. This leads to a unique “Shirk-Work-Shirk” equilibrium in which the monitor only exerts effort...