(Jose) Alejandro Lopez-Lira

(Jose) Alejandro Lopez-Lira

Contact Information

Research Interests: Asset Pricing, Machine Learning, Banking, Macro Finance, Financial Frictions

Links: CV

Overview

I am a PhD Candidate in the Finance Department. My research covers topics in Empirical Asset Pricing, Machine Learning and Textual Analysis.  ​​I will be available for interviews at the AFA 2020 Annual Meeting in San Diego, CA.

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Research

  • Alejandro Lopez-Lira (Work In Progress), Risk Factors That Matter: Textual Analysis of Risk Disclosures for the Cross-Section of Returns.

    Abstract: Using unsupervised machine learning, I introduce interpretable and economically relevant risk factors that characterize the cross-section of returns better than the leading factor models, furthermore, I do not use any information from the past returns to select the risk factors. I exploit natural language processing techniques to identify from the firms’ risk disclosures the types of risks that firms face, quantify how much each firm is exposed to each type of risk, and employ the firms’ exposure to each type of risk to construct a 4-factor model. The risk factors roughly correspond to Technology and Innovation Risk, Demand Risk, Production Risk and International Risk.

  • Alejandro Lopez-Lira, Demand-Driven Risk and the Cross-Section of Expected Returns.

    Abstract: Firms that concentrate their activities towards goods with higher income elasticity are more exposed to demand-driven risk, since the consumption of high-consumption households is more exposed to aggregate shocks. These firms earn higher risk-adjusted equity returns. A portfolio that goes long on the most exposed firms and short on the least exposed gets an abnormal risk-adjusted annual return of 7.5%. This risk does not seem to be coming from competition. A portfolio that goes long in firms exposed to demand-driven risk and competitive pressure and short on firms not exposed to demand-driven risk nor competitive pressure earns an abnormal risk-adjusted annual return of 14%.

Awards and Honors

WFA Cubist Systematic Strategies Ph.D. Candidate Award for Outstanding Research, 2019
Irwin Friend Doctoral Fellowship in Finance, Wharton, 2019
Best Paper, European Investment Forum Research Prize, Cambridge, 2019
Best Paper in the Investment Track, Baltimore Area Finance Conference, 2019
Rodney L. White Center for Financial Research Grant, Wharton, 2019
The Mack Institute for Innovation Management Research Grant, 2019
George James Term Fund Travel Award, Wharton, 2019
Jacob Levy Fellowship , Wharton, 2019
Rodney L. White Center for Financial Research Grant, Wharton, 2018
The Mack Institute for Innovation Management Research Grant, 2018

    In the News

    • Using a Company’s Own Words to Assess Its Risks, Knowledge@Wharton - 03/22/2019 Description

      When analysts or academics want to assess the risks that a company faces, they usually look at macroeconomic factors or internal firm metrics such as a declining sales trend to calculate those risks. But research from Wharton doctoral candidate Alejandro Lopez-Lira takes a different approach.

      He asked this question: What if, instead of letting the outside world tell us what risks a company faces, we let the company tell us itself? After all, a company knows its business best. Lopez-Lira used machine learning to read through the annual reports of all U.S. public companies to find out which risks they identified as the most serious ones they face. And the results can be surprising.

      His findings are in the paper, “Risk Factors That Matter: Textual Analysis of Risk Disclosures for the Cross-Section of Returns.” His research was supported the Mack Institute and the Rodney L. White Center for Financial Research.

    Activity

    In the News

    Using a Company’s Own Words to Assess Its Risks

    When analysts or academics want to assess the risks that a company faces, they usually look at macroeconomic factors or internal firm metrics such as a declining sales trend to calculate those risks. But research from Wharton doctoral candidate Alejandro Lopez-Lira takes a different approach.

    He asked this question: What if, instead of letting the outside world tell us what risks a company faces, we let the company tell us itself? After all, a company knows its business best. Lopez-Lira used machine learning to read through the annual reports of all U.S. public companies to find out which risks they identified as the most serious ones they face. And the results can be surprising.

    His findings are in the paper, “Risk Factors That Matter: Textual Analysis of Risk Disclosures for the Cross-Section of Returns.” His research was supported the Mack Institute and the Rodney L. White Center for Financial Research.

    Knowledge@Wharton - 03/22/2019
    All News