Recent experimental studies illustrate the influence of price path, particularly the ‘non-straight’ price path, on several aspects of investor decision-making. The paper employs an empirical proxy for price path based on convexity and demonstrates that price convexity significantly impacts the selling decisions with transaction-level data. We find that a price path that is likely to signal a favourable (unfavourable) price movement in the future lowers (heightens) the selling propensity of traders in stocks. The findings suggest that likely expectations about future price movement, as could be inferred from the experienced price path, significantly influence the trading decisions of retail traders.
We examine the relation between an institution's stock ownership and its tendency to support corporate management through the “Say-on-Pay” (SOP) executive compensation vote. Institutional advisors are more likely to oppose management on the SOP vote for their small-scale investments, i.e., investments that comprise a small fraction of an institution's aggregate stockholdings across its funds, or, alternatively, investments that comprise a small fraction of the total equity market capitalization of a corporation. We find evidence indicating that this voting pattern reflects an institutions’ overall sentiment for the stock, and is particularly prevalent when institutions have limited attention to monitor their investments.
Propelled by the recent financial product innovations involving derivatives, securitization and mortgages, commercial banks are becoming more complex, branching out into many “nontraditional” banking operations beyond issuance of loans. This broadening of operational scope in a pursuit of revenue diversification may be beneficial if banks exhibit scope economies. The existing (two-decade-old) empirical evidence lends no support for such product-scope-driven cost economies in banking, but it is greatly outdated and, surprisingly, there has been little (if any) research on this subject despite the drastic transformations that the U.S. banking industry has undergone over the past two decades in the wake of technological advancements and regulatory changes. Commercial banks have significantly shifted towards nontraditional operations, making the portfolio of products offered by present-day banks very different from that two decades ago. In this paper, we provide new and more robust evidence about scope economies in U.S. commercial banking. We improve upon the prior literature not only by analyzing the most recent data and accounting for banks’ nontraditional off-balance sheet operations, but also in multiple methodological ways. To test for scope economies, we estimate a flexible time-varying-coefficient panel-data quantile regression model which accommodates three-way heterogeneity across banks. Our results provide strong evidence in support of significantly positive scope economies across banks of virtually all sizes. Contrary to earlier studies, we find no empirical corroboration for scope diseconomies.
How does the microstructure of an over-the-counter market respond in a time of stress? We test several hypotheses of network-based models by analysing the 2015 crash of the Swiss franc-euro FX derivatives market. To do so we employ unique data at transaction and counterparty identity level, and a new analytical framework that uses the trading network topology to segment the market into a multi-layered structure. We document limited intermediation by inner-core nodes, in particular dealers with loss making outstanding positions. Clients in greater need of trading were less likely to trade, pointing to a supply driven liquidity shortage. However, more central and better connected clients were able to access the market sooner and at better prices than more peripheral clients, lending support to theory predictions that network centrality matters for sourcing liquidity and execution quality.
We study how capital account liberalization affects technological innovation. We provide robust evidence that industries more dependent on external finance have disproportionately higher innovation performance in economies with a more liberalized capital account. Among the components of capital account liberalization, although both equity market liberalization and outward FDI by domestic firms have sizable effects on innovation, they affect it differently. While equity market liberalization helps alleviate financial constraints by facilitating access to external finance, outward FDI by domestic firms promotes innovative activities by increasing internal finance from foreign operations. Further analysis indicates that the innovation-enhancing effects of capital account liberalization are limited mainly to countries with relatively well-developed financial systems and strong institutional quality, even in periods of financial crisis.
Using confidential loan officer survey data on lending standards and internal risk ratings on loans, we document an effect of large-scale asset purchase programs (LSAPs) on lending standards and risk-taking. We exploit cross-sectional variation in banks’ holdings of mortgage-backed securities to show that the first and third round of quantitative easing (QE1 and QE3) significantly lowered lending standards and increased loan risk characteristics. The magnitude of the effects is about the same in QE1 and QE3, and is comparable to the effect of a one percentage point decrease in the Fed funds target rate.
This paper examines the effects of bond liquidity on firms’ investments. We postulate that bond liquidity increases firms’ investment opportunities by reducing the cost of capital and improving access to financing. Using the variation in liquidity generated by several – both positive and negative – exogenous shocks, we find that firms respond to positive (negative) shocks by expanding (contracting) capital expenditures and acquisition activity. Further, by enhancing access to funding, bond liquidity facilitates acquisition financing and reduces the likelihood of investment delays. We also find a positive impact of bond liquidity on market valuations and profitability, suggesting that these investments are value-increasing.
We show that a bank's knowledge of an industry developed through its loan portfolio facilitates the bank's credit provision to other firms in that industry. This effect works beyond the bank's private information about the focal firm and is consistent with a cross information production where experience with other firms from a similar background reduces information asymmetry on the firm concerned. To tackle endogeneity, we develop an instrument for a bank's expertise in an industry based on historical, natural, and regulatory conditions. We provide further evidence using the 2007 housing market crash as a laboratory. We find that banks hit by the shock rebalance loan allocations to buffer borrowers in their expertise industries from a credit crunch. The effect of industry expertise is more pronounced for opaque firms and firms facing foreign competition pressure. Our findings suggest a spillover effect or economies of scale in banks’ information production. It helps explain the cost efficiency of financial intermediaries relative to direct lending and why, beyond relationship considerations, firms may prefer some banks over others.
This paper explores ways to improve the existing systemic risk measures by incorporating machine learning algorithms into the measurement. We aim to overcome the shortcomings of existing methods that rely on restricted modeling and are difficult to tap into various data resources. To this end, this paper unifies a dynamic quantification framework for systemic risk and links it to a two-step supervised learning problem, which allows for hierarchical structure of the systemic event and the return dependence. We leverage the generalization and predictive powers of machine learning to statistically model the tail events and the co-movements of the equity returns during the shocks to the macro-economy. Our results show that most machine learning algorithms enhance the systemic risk measure’s predictive power. Numerous comparative and sensitivity backtesting studies for United States and Hong Kong markets are conducted, from which we recommend the best machine learning algorithm for systemic risk measurement.
Credit ratings are fundamental in assessing the credit risk of a security or debtor. The failure of the Collateralized Debt Obligation (CDO) ratings during the financial crisis of 2007-2008 and the massive undervaluation of corporate risk leading up to the crisis resulted in a review of rating approaches. Yet the fundamental metric that guides the construction of credit ratings has not changed. We study the inadequacies of the old metric in simple models of investment and in structured finance portfolio optimization tasks, and we propose a new methodology based on a buffered probability of exceedance. The new approach offers a conservative risk assessment, with substantial conceptual and computational benefits. We illustrate the new approach using several examples and report the results of a structuring step-up CDO case study, with details available in an online Supplement.