Using the adoption of Zombie Property Laws (ZL) across several US states, we show that requiring lenders to maintain properties in the foreclosure process affects mortgage lending decisions and standards. Difference-in-differences estimations using a state border design show that ZL incentivizes lenders to screen mortgage applications more carefully: they deny more applications and impose higher interest rates on originated loans, especially risky loans. In turn, these loans exhibit higher ex post performance. ZL also affects lender behavior after borrowers become distressed, causing them to strategically keep delinquent mortgages alive. Our findings inform the debate on policy responses to foreclosure crises.

Drawing on detailed career and biographical data of Chinese politicians, this study builds a dynamic social network for all political elites in China and examines the selection process of provincial-level politicians. Using regression and tree-based machine learning techniques and leveraging individuals’ global centrality within political networks, we unveil the relative importance of economic performance, political networks, and career trajectory in determining the selection of provincial leaders. Our findings highlight the critical role of network embeddedness.

This paper examines how initial public offerings (IPOs) affect firms' internal organization. We find that IPO firms become more hierarchical and standardized organizations, characterized by additional layers, more managers, smaller control spans, and larger administrative functions. These changes occur mostly in preparation for the IPO and can be only partially explained by growth. IPO firms with greater human capital risk experience larger hierarchical changes. Hierarchical changes help firms standardize employee roles and formalize internal processes. Our results suggest that firms reorganize to reduce their dependence on key individuals' human capital when transitioning to public markets.

This study examines the institutional demand for mispriced stocks with incongruent expectations implied by the book-to-market (BM) ratio and financial strength. Institutional trading (or institutional demand) is calculated by both changes in institutional ownership (percentage of shares held) and the number of institutional investors from the previous to the current quarter. Market mispricing and expectation errors in value/glamour stocks can be identified by analysing firms’ recent financial strength (measured by FSCORE). Firms are sorted into value stocks (top 30%), middle stocks (between 30% and 70%) and glamour stocks (bottom 30%) by distribution of BM ratios at the end of the previous fiscal year. Firms in the sample are then double sorted by FSCORE and BM: in each BM portfolio, firms are further classified into high-, mid- and low-FSCORE groups. Consistent with the argument of expectation errors in value/glamour stocks (Piotroski and So, 2012), institutional investors buy value stocks with strong fundamentals (underpriced) and sell glamour stocks with weak fundamentals (overpriced). Independent institutions are more likely to take advantage of the mispricing in value/glamour firms than passive institutions. Institutional trading on expectation errors could reduce the abnormal returns to mispriced stocks. Institutional trading patterns on mispriced value/glamour stocks are also documented in global markets. Our research provides new evidence that the institutional investors do exploit the BM anomalies if the mispricing can be identified by both the BM and the recent financial strength. Our study differs from Caglayan, Celiker and Sonaer (2018) as we emphasise that financial institutions, in addition to relying on only the BM values, process information from financial statements to infer firms’ financial strength. This study is also the first to document that institutional demand on mispricing could attenuate the BM anomaly.