Conference Agenda

Geographical clustering is an essential feature of the venture capital (VC) industry as proximity helps VCs to acquire soft information about early-stage companies and to conduct post-investment activities. However, whether the VC investment model based on in-person interactions is still justified in the age of online communication technologies remains an open question. In this paper, we address this question by using an unexpected interruption in face-to-face meetings during the recent pandemic. We document that VCs respond to this change by breaking their traditional norm: they invest in more distant startups. We find that this evolution goes along with selection criteria and syndication process changes despite some persisting behaviors. Thus, our study helps to understand how VCs revisit their investment model and sheds light on the value of in-person interactions for the VC industry.

This paper examines how the market evaluates corporate social responsibility (CSR) programs. Stock prices increase upon the news on CSR programs when the programs address social issues frequently discussed by newspapers. Companies tend to increase CSR efforts to address social issues discussed more often. However, there are other behavioral patterns in the implementation of CSR programs that decrease stock prices. I further find a strong effect of financial performance on the market reaction, while the effect turns weaker if a CSR program addresses issues of high public concern. Overall, this paper sheds light on how the market views CSR programs and whether companies implement CSR programs in a way that increases stock prices.

Theory suggests that financial reporting bias is determined to a large extent by the information that market participants have. When investors learn more about firm fundamentals, misreporting decreases, and when investors obtain new information about managers' incentives to misreport, reporting bias increases (Fischer and Stocken, 2004). Nevertheless, we know little about how to measure these two types of uncertainty and their effects on financial misreporting. I propose a simple structural framework to estimate the amount of fundamental and misreporting incentives information known by market participants. On average, investors know 76.5% of current earnings and 36.8% of managers' incentives to misreport the current earnings before the current earnings report arrives. The estimates suggest that a 1% increase in the market's fundamental information (misreporting incentives information) can increase accounting quality by 0.885% (reduce accounting quality by 0.158%). At the same time, resolution of both fundamental and misreporting incentives uncertainty improves price efficiency, highlighting a potential trade-off that regulators concerned with financial misreporting and price informativeness may face.

Machine learning methods for big data trade off bias for precision in prediction. To understand the implications for financial markets, I formulate a trading model with a prediction technology where investors optimally choose a biased estimator. The model identifies a novel cost of complexity that arises endogenously. This effect makes it optimal to ignore costless signals and introduces in- and out-of-sample return predictability that is not driven by priced risk or behavioral biases. Empirically, the model can explain patterns of vanishing predictability of the equity risk premium. The model calibration is consistent with a technological shift following the rise of private computers and the invention of the internet. When allowing for heterogeneity in information between agents, complexity drives a wedge between the private and social value of data and lowers price informativeness. Estimation errors generate short-term price reversals similar to liquidity demand.

Market microstructure invariance (MMI) stipulates that trading costs of financial assets are driven by the volume and volatility of bets, that are, transactions intended to produce idiosyncratic gains based on investors’ beliefs. With futures transactions data, we estimate bet volume as the trading volume of brokerage firms that trade on behalf of their clients and bet volatility as the trade-related component of futures volatility. We find that the futures bid-ask spread lines up with bet volume and bet volatility as predicted by MMI, and that intermediation by high frequency traders does not interfere with the MMI relation.

We investigate the effect of ETF ownership on stock market anomalies and mar- ket efficiency. We find that low ETF ownership stocks exhibit higher returns, greater Sharpe ratios, and highly significant alphas compared to high ETF ownership stocks. We show that high ETF ownership stocks demonstrate more pronounced information flows than low ETF ownership stocks, reducing their mispricing as they are more in- formationally efficient. We find similar results when we match the two groups based on size, volume, book-to-market, and momentum. Our results are robust to different matching methods and to a wide array of controls in Fama-MacBeth regressions. Using Russell index reconstitution, we find causal evidence that ETF ownership attenuates anomaly returns.