Working Papers

Machine-Readable Data and Financial Experts in Asset Management

Should financial experts (e.g., buy-side asset managers and analysts) fear the rise of algorithms? As machine-readable (clean and structured) data are essential for the development and functioning of algorithms, I study this question by investigating whether financial experts benefit from more machine-readable data in information production in asset management. I first develop a model in which an institutional investor’s performance and asset holdings depend on two inputs: the amount of machine-readable data and the number of financial experts, and derive how changes induced by an increase in the amount of machine-readable data depend on the relation between the two inputs. Exploiting an exogenous regulatory shock that makes corporate filings more machine-readable, I find that institutions with more financial experts experience larger performance improvement than institutions with fewer financial experts, consistent with financial experts benefiting from more machine-readable data. This result helps evaluate the likelihood of algorithms replacing high-skilled financial practitioners.

Buy-Side and Sell-Side Reserch: Implications of Separating Equity Research Payment from Brokerage Service

(Joint work with Wei Zhao )

The recent MiFID II regulation in Europe made delegated asset managers’ spending on sell-side analyst research more transparent to their clients. This transparency requirement has attracted a lot of media attention and resistance from the industry. We study theoretically the impact of this transparency on asset managers’ information production. Focusing on the agency problem between asset managers and their clients, we show that transparency decreases the use of sell-side research but stimulates more buy-side research activities. These results a decrease in the number of sell-side analysts and an increase in buy-side analysts, which is consistent with empirical findings. Our model has additional predictions on managers’ performance, liquidity, and social welfare.

Work in Progress

Financial Advisors in the Private Market

Financial advisors intermediate more than 20% of capital raised non-financial firms in the private market. I provide evidence that they mitigate search frictions between capital seekers and investors. Using web-scraped misconduct records of advisors as a proxy for reputation, I find that low reputation advisors charge higher commission rates on average. To explain this result, I propose a random search model in which low quality firms pay higher commission rates and endogenously match more with advisors with low reputation.