中文目錄
1.地理與信息獲取
2.刪除未報(bào)告的創(chuàng)新
3.產(chǎn)業(yè)網(wǎng)絡(luò)與企業(yè)行為
4.公司債務(wù)與創(chuàng)新和增長
5.產(chǎn)品生命周期
6.CEO和高管的偏好

1.The Geography of Information Acquisition（JFQA2022）

Using detailed data on company visits by Chinese mutual funds, we provide direct evidence of mutual fund information acquisition activities and the consequent informational advantages mutual funds establish in local firms. Mutual funds are more likely to visit local and nearby firms both in and outside of their portfolios, but the ease of travel between fund and firm locations can substantially alleviate geographic distance constraints. Company visits by mutual funds are strongly associated with both fund trading activities and fund trading performance. Our results show that geographic constraints and costly information acquisition amplify information asymmetry in financial markets.

2.Deleting Unreported Innovation（JFQA2022）

The absence of observable innovation data for a firm often leads us to exclude or classify these firms as non-innovators. We assess the reliability of six methods for dealing with unreported innovation using several different counterfactuals for firms without reported R&D or patents. These tests reveal that excluding firms without observable innovation or imputing them as zero innovators and including a dummy variable can lead to biased parameter estimates for observed innovation and other explanatory variables. Excluding firms without patents is especially problematic, leading to false-positive results in empirical tests. Our tests suggest using multiple imputation to handle unreported innovation.

3.Industry Networks and the Geography of Firm Behavior（MS2022）

Using a network approach that circumvents well-known challenges in estimating peer effects, we show that interactions with a firm’s geographic neighbors play a significant causal role in corporate investment behavior and a modest role in financial policies and firm performance. Moreover, these geography network effects are almost entirely driven by propagation effects through product market and supply chain networks. We corroborate our findings in a quasi-experimental framework that allows for spillovers in treatment effects. Our findings help rationalize industrial clusters (e.g., Silicon Valley), as they illustrate that agglomeration economies are substantial and operate predominantly within industry boundaries.

4.Can Corporate Debt Foster Innovation and Growth（RFS2022）

Recent empirical studies have shown that innovative firms heavily rely on debt financing. Debt overhang implies that debt hampers innovation by incumbents. A second effect of debt is that it stimulates innovation by entrants. Using a Schumpeterian growth model with endogenous R&D and financing choices, we demonstrate that this second effect always dominates, so that debt fosters innovation and growth at the aggregate level. Our analysis suggests that the relation between debt and investment is more complex than previously acknowledged and highlights potential limitations of empirical work that solely focuses on incumbents when measuring the effects of debt on investment.

5.Product Life Cycles in Corporate Finance（RFS2022）

We develop a novel 10-K text-based model of product life cycles and examine firm investment policies. Conditioning on the life cycle substantially improves the power of?q?to explain investment and reveals a natural ordering of investments over the life cycle. While R&D and CAPX sensitivity are high early in the cycle, acquisitions arise as products mature, and divestitures and product extension investments arise as products decline.?q-sensitivities that condition on the life cycle can vary by as much as 400% from traditional sensitivities. The life cycle framework further reveals an enriched relationship between competition, investment, and corporate profits.

6.Revealing the revealed preferences of public firm CEOs and top executives: A new database from credit card spending（SMJ2022）

We introduce a new database which provides an unprecedented window into the off-the-job lives and interests of public firm top executives as reflected by their personal income allocation. We construct this database by matching household credit card spending data with the population of executives in Execucomp. To overcome the significant computational challenges associated with matching these data, we build on the statistical record-linking literature in a way that allows us to generate reliable matches with limited information. To facilitate research exploring new questions made possible with this database, we make our matching crosswalk freely available for academic use.