Investor Relations and IPO Performance
Salim Chahine, Gonul Colak, Iftekhar Hasan, Mohamad Mazboudi
Review of Accounting Studies,
Nr. 2,
2020
Abstract
We analyze the value of investor relations (IR) strategies to IPO firms. We find that firms that are less visible and have inexperienced management tend to hire IR consultants prior to the issue date. IR consultants help create positive news coverage before an IPO, as reflected in a more optimistic tone of published media. Their presence is associated with higher underpricing at the IPO date but with lower long-run returns. IR-backed IPOs also exhibit disproportionately higher insider-related agency problems, as IR-induced higher underpricing tends to occur primarily in IPOs where underwriter and venture capitalist agency conflicts are more severe. These findings suggest that the IR programs of IPO firm are mostly short-term oriented and facilitate the ulterior motives of some insiders (underwriters and venture capitalists) targeting higher first-day returns.
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Why are some Chinese Firms Failing in the US Capital Markets? A Machine Learning Approach
Gonul Colak, Mengchuan Fu, Iftekhar Hasan
Pacific-Basin Finance Journal,
June
2020
Abstract
We study the market performance of Chinese companies listed in the U.S. stock exchanges using machine learning methods. Predicting the market performance of U.S. listed Chinese firms is a challenging task due to the scarcity of data and the large set of unknown predictors involved in the process. We examine the market performance from three different angles: the underpricing (or short-term market phenomena), the post-issuance stock underperformance (or long-term market phenomena), and the regulatory delistings (IPO failure risk). Using machine learning techniques that can better handle various data problems, we improve on the predictive power of traditional estimations, such as OLS and logit. Our predictive model highlights some novel findings: failed Chinese companies have chosen unreliable U.S. intermediaries when going public, and they tend to suffer from more severe owners-related agency problems.
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Financial Incentives and Loan Officer Behavior: Multitasking and Allocation of Effort under an Incomplete Contract
Patrick Behr, Alejandro H. Drexler, Reint E. Gropp, Andre Guettler
Journal of Financial and Quantitative Analysis,
Nr. 4,
2020
Abstract
We investigate the implications of providing loan officers with a nonlinear compensation structure that rewards loan volume and penalizes poor performance. Using a unique data set provided by a large international commercial bank, we examine the main activities that loan officers perform: loan prospecting, screening, and monitoring. We find that when loan officers are at risk of losing their bonuses, they increase prospecting and monitoring. We further show that loan officers adjust their behavior more toward the end of the month when bonus payments are approaching. These effects are more pronounced for loan officers with longer tenures at the bank.
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Banks’ Equity Performance and the Term Structure of Interest Rates
Elyas Elyasiani, Iftekhar Hasan, Elena Kalotychou, Panos K. Pouliasis, Sotiris Staikouras
Financial Markets, Institutions and Instruments,
Nr. 2,
2020
Abstract
Using an extensive global sample, this paper investigates the impact of the term structure of interest rates on bank equity returns. Decomposing the yield curve to its three constituents (level, slope and curvature), the paper evaluates the time-varying sensitivity of the bank’s equity returns to these constituents by using a diagonal dynamic conditional correlation multivariate GARCH framework. Evidence reveals that the empirical proxies for the three factors explain the variations in equity returns above and beyond the market-wide effect. More specifically, shocks to the long-term (level) and short-term (slope) factors have a statistically significant impact on equity returns, while those on the medium-term (curvature) factor are less clear-cut. Bank size plays an important role in the sense that exposures are higher for SIFIs and large banks compared to medium and small banks. Moreover, banks exhibit greater sensitivities to all risk factors during the crisis and postcrisis periods compared to the pre-crisis period; though these sensitivities do not differ for market-oriented and bank-oriented financial systems.
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How to Talk Down Your Stock Performance
Andreas Barth, Sasan Mansouri, Fabian Wöbbeking, Severin Zörgiebel
SSRN Discussion Papers,
2020
Abstract
We process the natural language of verbal firm disclosures in order to study the use of context specific language or jargon and its impact on financial performance. We observe that, within the Q&A of earnings conference calls, managers use less jargon in responses to tougher questions, and after a quarter of bad economic success. Moreover, markets interpret the lack of precise information as a bad signal: we find lower cumulative abnormal returns and a higher implied volatility following earnings calls where managers use less jargon. These results support the argument that context specific language or jargon helps to efficiently and precisely transfer information.
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Does Working at a Start-Up Pay Off?
Daniel Fackler, Lisa Hölscher, Claus Schnabel, Antje Weyh
Abstract
Using representative linked employer-employee data for Germany, this paper analyzes short- and long-run differences in labor market performance of workers joining startups instead of incumbent firms. Applying entropy balancing and following individuals over ten years, we find huge and long-lasting drawbacks from entering a start-up in terms of wages, yearly income, and (un)employment. These disadvantages hold for all groups of workers and types of start-ups analyzed. Although our analysis of different subsequent career paths highlights important heterogeneities, it does not reveal any strategy through which workers joining start-ups can catch up with the income of similar workers entering incumbent firms.
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Managerial Biases and Debt Contract Design: The Case of Syndicated Loans
Tim R. Adam, Valentin Burg, Tobias Scheinert, Daniel Streitz
Management Science,
Nr. 1,
2020
Abstract
We examine whether managerial overconfidence impacts the use of performance-pricing provisions in loan contracts (performance-sensitive debt [PSD]). Managers with biased views may issue PSD because they consider this form of debt to be mispriced. Our evidence shows that overconfident managers are more likely to issue rate-increasing PSD than regular debt. They choose PSD with steeper performance-pricing schedules than those chosen by rational managers. We reject the possibility that overconfident managers have (persistent) positive private information and use PSD for signaling. Finally, firms seem to benefit less from using PSD ex post if they are managed by overconfident rather than rational managers.
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Does Machine Learning Help us Predict Banking Crises?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Journal of Financial Stability,
December
2019
Abstract
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance metric, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly efficiently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
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Should Forecasters Use Real‐time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence
Katja Heinisch, Rolf Scheufele
German Economic Review,
Nr. 4,
2019
Abstract
In this paper, we investigate whether differences exist among forecasts using real‐time or latest‐available data to predict gross domestic product (GDP). We employ mixed‐frequency models and real‐time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real‐time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.
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Wage Delegation in the Field
Sabrina Jeworrek, Vanessa Mertins
Journal of Economics and Management Strategy,
Nr. 4,
2019
Abstract
By conducting a natural field experiment, we analyze the managerial policy of delegating the wage choice to employees. We find that this policy enhances performance significantly, which is remarkable since allocated wage premiums of the same size have no effect at all. Observed self‐imposed wage restraints and absence of negative peer effects speak in favor of wage delegation, although the chosen wage premium levels severely dampen its net value. Additional experimental and survey data provide important insights into employees' underlying motivations.
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