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On Modeling IPO Failure Risk
Gonul Colak, Mengchuan Fu, Iftekhar Hasan
Economic Modelling,
April
2022
Abstract
This paper offers a novel framework, combining firm operational risk, IPO pricing risk, and market risk, to model IPO failure risk. By analyzing nearly a thousand variables, we observe that prior IPO failure risk models have suffered from a major missing-variable problem. Evidence reveals several key new firm-level determinants, e.g., the volatility operating performance, the size of its accounts payable, pretax income to common equity, total short-term debt, and a few macroeconomic variables such as treasury bill rate, and book-to-market of the DJIA index. These findings have major economic implications. The total value loss from not predicting the imminent failure of an IPO is significantly lower with this proposed model compared to other established models. The IPO investors could have saved around $18billion over the period between 1994 and 2016 by using this model.
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Do Digital Information Technologies Help Unemployed Job Seekers Find a Job? Evidence from the Broadband Internet Expansion in Germany
Nicole Gürtzgen, André Diegmann, Laura Pohlan, Gerard J. van den Berg
European Economic Review,
February
2021
Abstract
This paper studies effects of the introduction of a new digital mass medium on reemployment of unemployed job seekers. We combine data on broadband internet availability at the local level with German individual register data. We address endogeneity by exploiting technological peculiarities that affected the roll-out of broadband internet. Results show that broadband internet improves reemployment rates after the first months in unemployment for males. Complementary analyses with survey data suggest that internet access mainly changes male job seekers’ search behavior by increasing online search and the number of job applications.
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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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The Internet Effects on Sex Crime Offenses – Evidence from the German Broadband Internet Expansion
André Diegmann
Journal of Economic Behavior and Organization,
September
2019
Abstract
This paper studies the effects of the introduction of a new mass medium on sex crime in Germany. I use unique data on criminal offenses and broadband internet measured at the municipal level to shed light on this issue. In order to address endogeneity in broadband internet availability, I exploit technical peculiarities at the regional level that determine the roll-out of high-speed internet. Results provide evidence of a substitution effect of internet exposure on sex crime. The substitution effect is neither driven by differences in reporting behavior, nor by matching processes at the victim and offender side. This suggests that the consumption of extreme media plays an important role in explaining the documented high-speed internet effect.
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Do Digital Information Technologies Help Unemployed Job Seekers Find a Job? Evidence from the Broadband Internet Expansion in Germany
Nicole Gürtzgen, André Diegmann, Laura Pohlan, Gerard J. van den Berg
Abstract
This paper studies effects of the introduction of a new digital mass medium on reemployment of unemployed job seekers. We combine data on high-speed (broadband) internet availability at the local level with German individual register data. We address endogeneity by exploiting technological peculiarities that affected the roll-out of high-speed internet. The results show that high-speed internet improves reemployment rates after the first months in unemployment. This is confirmed by complementary analyses with individual survey data suggesting that internet access increases online job search and the number of job interviews after a few months in unemployment.
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What Type of Finance Matters for Growth? Bayesian Model Averaging Evidence
Iftekhar Hasan, Roman Horvath, Jan Mares
World Bank Economic Review,
Nr. 2,
2018
Abstract
We examine the effect of finance on long-term economic growth using Bayesian model averaging to address model uncertainty in cross-country growth regressions. The literature largely focuses on financial indicators that assess the financial depth of banks and stock markets. We examine these indicators jointly with newly developed indicators that assess the stability and efficiency of financial markets. Once we subject the finance-growth regressions to model uncertainty, our results suggest that commonly used indicators of financial development are not robustly related to long-term growth. However, the findings from our global sample indicate that one newly developed indicator—the efficiency of financial intermediaries—is robustly related to long-term growth.
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On Mitra's Sufficient Condition for Topological Chaos: Seventeen Years Later
Liuchun Deng, M. Ali Khan
Economics Letters,
March
2018
Abstract
This letter reports an easy extension of Mitra’s “easily verifiable” sufficient condition for topological chaos in unimodal maps, and offers its application to reduced-form representations of two economic models that have figured prominently in the recent literature in economic dynamics: the check- and the M-map pertaining to the 2-sector Robinson–Solow–Srinivasan (RSS) and Matsuyama models respectively. A consideration of the iterates of these maps establishes the complementarity of the useful 2001 condition with the 1982 (LMPY) theorem of Li–Misiurewicz–Pianigiani–Yorke when supplemented by a geometric construction elaborated in Khan–Piazza (2011).
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Predicting Earnings and Cash Flows: The Information Content of Losses and Tax Loss Carryforwards
Sandra Dreher, Sebastian Eichfelder, Felix Noth
IWH Discussion Papers,
Nr. 30,
2017
Abstract
We analyse the relevance of losses, accounting information on tax loss carryforwards, and deferred taxes for the prediction of earnings and cash flows up to four years ahead. We use a unique hand-collected panel of German listed firms encompassing detailed information on tax loss carryforwards and deferred taxes from the tax footnote. Our out-of-sample predictions show that considering accounting information on tax loss carryforwards and deferred taxes does not enhance the accuracy of performance forecasts and can even worsen performance predictions. We find that common forecasting approaches that treat positive and negative performances equally or that use a dummy variable for negative performance can lead to biased performance forecasts, and we provide a simple empirical specification to account for that issue.
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