In portfolio risk minimization, the inverse covariance matrix prescribes the hedge trades in which a stock is hedged by all the other stocks in the portfolio. In practice with finite samples, however, multicollinearity makes the hedge trades too unstable and unreliable. By shrinking trade sizes and reducing the number of stocks in each hedge trade, we propose a “sparse” estimator of the inverse covariance matrix. Comparing favorably with other methods (equal weighting, shrunk covariance matrix, industry factor model, nonnegativity constraints), a portfolio formed on the proposed estimator achieves significant out-of-sample risk reduction and improves certainty equivalent returns after transaction costs.

Target acquisitiveness stands out as one of the primary drivers of all the key aspects of the market for corporate takeovers: acquisition announcement returns, probability of deal success, propensity to acquire and be acquired. Acquisitive targets, though a small proportion of the sample, are responsible for half of the overall negative acquisition announcement returns. Our large body of empirical evidence consistently supports the view that the motivation behind acquisitions of acquisitive targets is defensive: acquirers “eat in order not to be eaten”.

We use nearly 500 shifts in statutory corporate and personal income tax rates as natural experiments to assess the effect of corporate and personal taxes on capital structure. We find both corporate and personal income taxes to be significant determinants of capital structure. Based on ex post observed summary statistics, across Organisation for Economic Co-Operation and Development (OECD) countries, taxes appear to be as important as other traditional variables in explaining capital structure choices. The results are stronger among corporate tax payers, dividend payers, and companies that are more likely to have an individual as the marginal investor.

We examine the role of firms' government connections, defined by government intervention in CEO appointment and the status of state ownership, in determining the severity of financial constraints faced by Chinese firms. We demonstrate that government connections are associated with substantially less severe financial constraints (i.e., less reliance on internal cash flows to fund investment), and that the sensitivity of investment to internal cash flows is higher for firms that report greater obstacles to obtaining external funds. We also find that those large non-state firms with weak government connections, likely the engine for innovation in the coming years in China, are especially financially constrained, due perhaps to the formidable hold that their state rivals have on financial resources after the ‘grabbing-the-big-and-letting-go-the-small’ privatization program in China. Our empirical results suggest that government connections play an important role in explaining Chinese firms' financing conditions, and provide further evidence on the nature of the misallocation of credit by China's dominant state-owned banks.

We examine whether a specialized asset of family firms, founders' political connections, influences the second generation involvement in family firms in China. We further investigate the impact of such second generation involvement on firm performance. The findings suggest that founders' political connections are a critical factor in the decision of second generation involvement, with politically connected founders more likely to appoint a second generation as a family firm chairman, CEO, or director. The results are consistent with the transfer cost hypothesis of Bennedsen et al. (2015). Moreover, we document that second generation involvement enhances firm performance, with the curtailment of tunneling as an important channel of performance improvement. Our results are robust to different empirical models and controlling for possible endogeneity issues. In addition, second-generation effects are more pronounced in firms with less outside monitoring.

With superior information about their customers’ prospects, suppliers extend trade credit to capture future profitable business. We show that this information advantage generates significant return predictability. After controlling for major firm characteristics, firms that rely more on trade credit relative to debt financing have higher subsequent stock returns. The return predictability by trade credit is stronger among firms with lower borrowing capacity or profitability, and is more significant for firms with a higher degree of information asymmetry. Our findings suggest that trade credit extension reveals suppliers’ information that diffuses gradually across the investing public.

We study the procompetitive gains from international trade in a quantitative model with endogenously variable markups. We find that trade can significantly reduce markup distortions if two conditions are satisfied: (i) there is extensive misallocation, and (ii) opening to trade exposes hitherto dominant producers to greater competitive pressure. We measure the extent to which these two conditions are satisfied in Taiwanese producer-level data. Versions of our model consistent with the Taiwanese data predict that opening up to trade strongly increases competition and reduces markup distortions by up to one-half, thus significantly reducing productivity losses due to misallocation.

The variance risk premium, defined as the difference between the actual and risk-neutral expectations of the forward aggregate market variation, helps predict future market returns. Relying on a new essentially model-free estimation procedure, we show that much of this predictability may be attributed to time variation in the part of the variance risk premium associated with the special compensation demanded by investors for bearing jump tail risk, consistent with the idea that market fears play an important role in understanding the return predictability.

Volatility clustering is a well-known stylized feature of financial asset returns. This paper investigates asymmetric pattern in volatility clustering by employing a univariate copula approach of Chen and Fan (2006). Using daily realized kernel volatilities constructed from high frequency data from stock and foreign exchange markets, we find evidence that volatility clustering is highly nonlinear and strongly asymmetric in that clusters of high volatility occur more often than clusters of low volatility. To the best of our knowledge, this paper is the first one to address and uncover this phenomenon. In particular, the asymmetry in volatility clustering is found to be more pronounced in the stock markets than in the foreign exchange markets. Further, the volatility clusters are shown to remain persistent for over a month and asymmetric across different time periods. Our findings have important implications for risk management. A simulation study indicates that models which accommodate asymmetric volatility clustering can significantly improve the out-of-sample forecasts of Value-at-Risk.

We study how the presence of short sellers affects the incentives of the insiders to trade on negative information. We show it induces insiders to sell more (shares from their existing stakes) and trade faster to preempt the potential competition from short sellers. An experiment and instrumental variable analysis confirm this causal relationship. The effects are stronger for “opportunistic” (i.e., more informed) insider trades and when short sellers׳ attention is high. Return predictability of insider sales only occurs in stocks with high short-selling potential, suggesting that short sellers indirectly enhance the speed of information dissemination by accelerating trading by insiders.