External Social Networks and Earnings Management
Ming Fang, Bill Francis, Iftekhar Hasan, Qiang Wu
British Accounting Review,
No. 2,
2022
Abstract
Using a sample of U.S. listed firms for the 2000–2017 period, we examine how external social networks of top executives and directors affect earnings management in their firms. We find that well-connected firms are more aggressive in managing earnings through both accruals and real activities and that the results are robust after controlling for internal executive social ties. Using a difference-in-differences approach, we find that earnings management decreases after a socially connected executive or director dies. Additional analysis shows that connections forged by past professional working experiences have a greater impact on earnings management than connections forged by education and other social activities. Moreover, CFO social networks have a greater influence on earnings management than CEO social networks. Finally, we explore the underlying mechanisms, finding that 1) firms that are socially connected to each other show more similarities in their earnings management than firms that do not share a connection, and 2) more connected firms are less likely to incur accounting restatements. Collectively, our findings indicate that the external social networks of top executives and directors are important determinants of both their accrual- and real activity-based earnings management.
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29.07.2021 • 20/2021
Communication instead of conflict – why are female CEOs so interesting for hedge funds
The value of female-led firms is enhanced more by the intervention of activist investors than that of firms with male CEOs. This is the result of a recent paper by Iftekhar Hasan (Fordham University and IWH) and Qiang Wu (Rensselaer Polytechnic Institute, RPI) at the Halle Institute for Economic Research (IWH). "The results show that female CEOs particularly benefit from the intervention of hedge fund activists due to their strong communication and interpersonal skills," explains Iftekhar Hasan. This is because, on average, the intervention of an activist hedge fund increases the value of the firm ex post. To achieve this, activist hedge funds such as Carl Icahn, Trian Fundmanagement or Elliott prefer to rely on communication and cooperation with the management.
Reint E. Gropp
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Who Benefits from Mandatory CSR? Evidence from the Indian Companies Act 2013
Jitendra Aswani, N. K. Chidambaran, Iftekhar Hasan
Emerging Markets Review,
March
2021
Abstract
We examine the value impact of mandatory Corporate Social Responsibility (CSR) spending required by the Indian Companies Act of 2013 for large and profitable Indian firms. We find that the external mandate is value decreasing, even after controlling for prior voluntary CSR activity by firms affected by the mandate. We also find that there is systematic crosssectional variation across firms. Firms that are profitable and firms in the Fast Moving Consumer Goods sector that voluntarily engaged in CSR, benefit from CSR. Industrial firms and firms with high capital expenditures are negatively impacted by the mandate. We conclude that a one-size-fits-all approach to CSR is sub-optimal and value decreasing.
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The Appropriateness of the Macroeconomic Imbalance Procedure for Central and Eastern European Countries
Geraldine Dany-Knedlik, Martina Kämpfe, Tobias Knedlik
Empirica,
No. 1,
2021
Abstract
The European Commission’s Scoreboard of Macroeconomic Imbalances is a rare case of a publicly released early warning system. It was published first time in 2012 by the European Commission as a reaction to public debt crises in Europe. So far, the Macroeconomic Imbalance Procedure takes a one-size-fits-all approach with regard to the identification of thresholds. The experience of Central and Eastern European Countries during the global financial crisis and in the resulting public debt crises has been largely different from that of other European countries. This paper looks at the appropriateness of scoreboard of the Macroeconomic Imbalances Procedure of the European Commission for this group of catching-up countries. It is shown that while some of the indicators of the scoreboard are helpful to predict crises in the region, thresholds are in most cases set too narrow since it largely disregarded the specifics of catching-up economies, in particular higher and more volatile growth rates of various macroeconomic variables.
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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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flexpaneldid: A Stata Toolbox for Causal Analysis with Varying Treatment Time and Duration
Eva Dettmann, Alexander Giebler, Antje Weyh
IWH Discussion Papers,
No. 3,
2020
Abstract
The paper presents a modification of the matching and difference-in-differences approach of Heckman et al. (1998) for the staggered treatment adoption design and a Stata tool that implements the approach. This flexible conditional difference-in-differences approach is particularly useful for causal analysis of treatments with varying start dates and varying treatment durations. Introducing more flexibility enables the user to consider individual treatment periods for the treated observations and thus circumventing problems arising in canonical difference-in-differences approaches. The open-source flexpaneldid toolbox for Stata implements the developed approach and allows comprehensive robustness checks and quality tests. The core of the paper gives comprehensive examples to explain the use of the commands and its options on the basis of a publicly accessible data set.
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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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19.09.2019 • 19/2019
Long-term effects of privatisation in eastern Germany: award-winning US economist begins large-scale research project at the IWH
It is one of the most prestigious awards in the German scientific community: the Max Planck-Humboldt Research Award 2019 endowed with €1.5 million goes to Ufuk Akcigit, Professor of Economics at the University of Chicago. At the Halle Institute for Economic Research (IWH), Akcigit aims to use innovative methods to investigate why the economy in eastern Germany is still lagging behind that in western Germany – and what role the privatisation process 30 years ago played in this.
Reint E. Gropp
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Bankruptcy Spillovers
Shai B. Bernstein, Emanuele Colonnelli, Xavier Giroud, Benjamin Iverson
Journal of Financial Economics,
No. 3,
2019
Abstract
How do different bankruptcy approaches affect the local economy? Using US Census microdata, we explore the spillover effects of reorganization and liquidation on geographically proximate firms. We exploit the random assignment of bankruptcy judges as a source of exogenous variation in the probability of liquidation. We find that employment declines substantially in the immediate neighborhood of the liquidated establishments, relative to reorganized establishments. The spillover effects are highly localized and concentrate in nontradable and service sectors, consistent with a reduction in local consumer traffic and a decline in knowledge spillovers between firms. The evidence highlights the externalities that bankruptcy design can impose on nonbankrupt firms.
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