I am currently working as a Wallenberg Postdoctoral Fellow under the supervision of Alexander Ljungqvist in the Department of Finance at the Stockholm School of Economics and at the Swedish House of Finance.
My research interests are corporate finance, behavioral finance, financial economics, and macroeconomics with particular focus on the following: the links between labor and finance, firms’ R&D financing, the links between accounting and finance, the managerial side of behavioral finance, networks of firms, and the effects of network formations on media of exchange.
I will present my job market paper, “Competition for Talent: Evidence from a Network of Labor Market Peers,” at the AFA 2021 Annual Meeting and will be available for interviews at both the EEA 2020 and AFA 2021 Annual Meetings.
Stockholm School of Economics
Drottninggatan 98, 2456B
110 60 Stockholm, Sweden
“Competition for Talent: Evidence from a Network of Labor Market Peers” (Selected for presentation at the AFA 2021 Annual Meeting)
I construct a novel network of labor market peers that is denser and more centralized compared to product and capital market networks. Using my labor market network, I provide robust evidence that focal firms spend more on R&D and suffer more talent outflows when their labor market peers increase the benefits they offer their talented employees. Focal firms use capital markets to finance their labor market responses, issuing stock and increasing cash holdings. The findings highlight the predictive effect of peer firms’ labor market actions and provide evidence that ties labor markets and capital markets together.
“Information Externalities among Listed Firms” (With Alexander Ljungqvist, Selected for presentation at the AFA 2022 Annual Meeting)
We establish the presence of sizeable information externalities across firms listed on U.S. stock exchanges using staggered non-marginal increases in disclosure at peer firms unaccompanied by changes in mandatory disclosure at focal firms. We find that a peer firm’s mandatory disclosure improves the focal firm’s trading liquidity directly by reducing adverse selection risk and indirectly by crowding in both voluntary disclosure and analyst information production at the focal firm. Positive information externalities, and the complementarities they operate through, support regulators’ use of mandatory disclosure to improve the market-wide information environment.
“Financing R&D, Financial Constraints, and Employment” (With James Brown)
This study examines the speed of labor adjustment in high-tech and non-high-tech firms and the effect of balance sheet liquidity (cash holdings) on employment changes in response to demand shocks. It offers robust evidence that firms in the high-tech sector, which account for most R&D, adjust employment toward the target employment slowly. The finding supports that adjustment costs for labor in high-tech firms are high. This study also documents that firms with more cash holdings show fewer employment changes in response to consumer demand shocks. These effects are amplified within financially constrained firms. The results suggest that cash holdings may help financially constrained firms to maintain stable employment in response to consumer demand shocks, particularly for high-tech, young, and small firms.
“Contrast Effects in Investment and Financing Decisions” (With Elizabeth Hoffman, R&R at Management Science)
The effects of prior positive or negative stimuli (contrast effects) have not been extensively studied in a financial context. This study develops an experimental design to examine whether contrast effects distort the risk attitudes of individuals under a choice-based elicitation procedure. We find that individuals exposed to a positive stimulus amplify risk-seeking in investment decisions as opposed to individuals exposed to a negative stimulus. However, individuals behave similarly in making financing decisions regardless of different economic stimuli. We find that, on average, individuals spend 16% more time making financing decisions than investment decisions. The results provide robust evidence that contrast effects can lead to mistakes in investment decisions and suggest that financing decisions may require more mental effort than investment decisions.
“Emergence of Goods as Media of Exchange in Different Types of Trade Networks”
This study uses an agent-based computer model to examine how trade networks influence the emergence of goods as media of exchange in a decentralized economy. This model implements the evolutionary process of the Kiyotaki-Wright (KW) model (1989), which explains the endogenous emergence of multiple media of exchange. Unlike previous experimental findings, this paper finds that all the agents behave according to the KW model, where some agents prefer to accept a higher storage cost good over a lower storage cost good because they speculate having a shorter wait for trading their consumption goods. In this study, the KW model is expanded to different types of trade networks, and shows that trade networks can cause agents to adopt speculative strategies. This leads to changes in the emergence of multiple goods as media of exchange across different trade networks.
“Predicting Returns: Evidence from Labor Market-Related News Articles”
This study documents that simple trading strategies based on the sentiment information of labor market-related news articles predict stock returns. I identify the average sentiment scores of labor market-related news articles of each firm daily and use them to predict stock returns. Compared to sentiment scores from capital market-related news articles and product market-related news articles, those from labor market-related articles are incorporated in stock prices more slowly and are better predictors of future stock returns, with strong effects from news articles than from press releases. These findings shed light on the speed of information assimilation on stock prices.
Research in Progress
“Automation Risk and Premium” (With Peter Orazem)
We develop an index of automatable occupations and link it to the relative price of non-automatable jobs to automatable jobs, or automation premium. We find that automation premium sharply increased before the Great Recession, while the relative quantity of non-automatable jobs to automatable jobs sharply increased after the Great Recession. The driving force of the sharp increase in automation premium before the Great Recession is the rise of high-skill automation. High-skill automation displaces high-skill automatable labor and increases the demand for high-skill non-automatable labor, not the demand for low-skill non-automatable labor. To explain these results, we use a model in which workers across different education groups with the same likelihood of automation are imperfect substitutes. We find that low-skill non-automatable labor and high-skill non-automatable labor are imperfect substitutes. Our findings show that automation is a significant threat to jobs and that the demand for non-automatable labor, particularly for high-skill non-automatable labor, has consistently increased.
“To Redact or Not to Redact?”
“R&D Investment Sensitivity to Payout Policies: Cash Dividends and Stock Buybacks”
“Forecasting the State Tax Revenue of California”
“The Economic Effects of Environmental Risks: The Case of the Indian Ocean Tsunami”
“Fiat Money as a Medium of Exchange in Wilhite’s Four Different Trade Networks”
Teaching Statement: available upon request
Teaching Award: Teaching Excellence Award, Iowa State University, 2019