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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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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An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?
Johannes Beutel, Sophia List, Gregor von Schweinitz
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 measure, 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 effciently, 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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Career Experience, Political Effects, and Voting Behavior in the Riksbank’s Monetary Policy Committee
Stefan Eichler, Tom Lähner
Economics Letters,
June
2017
Abstract
We find that career experience shapes the voting behavior of the Riksbank’s Monetary Policy Committee (MPC) members. Members with a career in the Riksbank and the government prefer higher rates. During a legislation with a center-right (center-left) party administration, MPC members with a career background in the government favor higher (lower) interest rates. Highlights: • The determinants of voting behavior in the Swedish Riksbank are considered. • Voting is analyzed with random effects ordered logit models for 1999–2013. • Interplay of career experience and political factors shapes voting behavior. • Government or Riksbank background leads to higher interest rate votes. • Partisan voting behavior is detected for members with government background.
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Determinants of Knowledge Exchange Between Foreign and Domestic Enterprises in European Post-transition Economies
Andrea Gauselmann
Journal Economia e Politica Industriale (Journal of Industrial and Business Economics),
No. 4,
2014
Abstract
The aim of this paper is to contribute to the literature on internationalised research and development by investigating determinants of knowledge and technology transfer between foreign subsidiaries and the local economy in European post-transition economies. This inquiry leads to a better understanding of determinants that influence this knowledge and technology exchange. Applying a logit model, we find that, in particular, the foreign subsidiary’s corporate governance structure, its embeddedness in the multinational enterprise’s internal knowledge base, its own technological capacity, the growth of the regional knowledge stock and the regional sectoral diversification are all positively associated with the transfer of knowledge. Subsidiaries’ investment motives and the relative weight of the sector of investment in the region’s economy appear to be of less importance. The analysis focuses on European post-transition economies, using the example of five selected Central Eastern European countries and East Germany. We exploit a unique dataset, the IWH FDI Micro database, which contains information on one thousand two hundred forty-five foreign subsidiaries in this region.
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MNEs and Regional R&D Co-operation: Evidence from Post-Transition Economies
Andrea Gauselmann
Grincoh Working Papers,
2013
Abstract
The aim of this paper is to contribute to the literature by investigating the determinants of R&D co-operation between MNEs’ foreign subsidiaries and enterprises in the region of location, thereby leading to a better understanding which firm- and region-specific factors influence this co-operation behavior. Applying a logit model, this paper investigates which firm- and region-specific determinants influence technological cooperation between foreign subsidiaries and suppliers, customers, and research institutions in the region of location. Results suggest that especially the foreign subsidiary’s mandate in terms of R&D, its internal technological embeddedness, its technological capability but also the regional knowledge stock are positively associated with these co-operations. The analysis focuses on post-transition economies, using the example of five selected CEE countries and East Germany. We exploit a unique dataset - the IWH FDI Micro database - which holds information on 1,245 foreign subsidiaries in this region.
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Natural-resource or Market-seeking FDI in Russia? An Empirical Study of Locational Factors Affecting the Regional Distribution of FDI Entries
K. Gonchar, Philipp Marek
HSE Working Papers, Series: Economics, WP BRP 26/EC/2013,
2013
Abstract
This paper analyzes the spatial distribution of foreign direct investment (FDI) across regions in Russia. Our analysis employs data on Russian firms with a foreign investor during the 2000-2009 period and links regional statistics in the conditional logit model. The main findings are threefold. First, we conclude that market-related factors and the availability of natural resources are important factors in attracting FDI. Second, existing agglomeration economies encourage foreign investors. Third, the findings imply that service-oriented FDI co-locates with extraction industries in resource-endowed regions.
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Natural-resource or Market-seeking FDI in Russia? An Empirical Study of Locational Factors Affecting the Regional Distribution of FDI Entries
K. Gonchar, Philipp Marek
IWH Discussion Papers,
No. 3,
2013
Abstract
This paper conducts an empirical study of the factors that affect the spatial distribution of foreign direct investment (FDI) across regions in Russia; in particular, this paper is concerned with those regions that are endowed with natural resources and market-related benefits. Our analysis employs data on Russian firms with a foreign investor during the 2000-2009 period and linked regional statistics in the conditional logit model. The main findings are threefold. First, we conclude that one theory alone is not able to explain the geographical pattern of foreign investments in Russia. A combination of determinants is at work; market-related factors and the availability of natural resources are important factors in attracting FDI. The relative importance of natural resources seems to grow over time, despite shocks associated with events such as the Yukos trial. Second, existing agglomeration economies encourage foreign investors by means of forces generated simultaneously by sector-specific and inter-sectoral externalities. Third, the findings imply that service-oriented FDI co-locates with extraction industries in resource-endowed regions. The results are robust when Moscow is excluded and for subsamples including only Greenfield investments or both Greenfield investments and mergers and acquisitions (M&A).
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Regional Determinants of MNE’s Location Choice in Post-transition Economies
Andrea Gauselmann, Philipp Marek
Empirica,
No. 4,
2012
Abstract
This article focuses on the impact of agglomeration and labour market factors on the location choice of MNEs in post-transition economies. We compare data from 33 regions in East Germany, the Czech Republic and Poland using a mixed logit model on a sample of 4,343 subsidiaries for the time period between 2000 and 2010. The results show that agglomeration advantages, such as sectoral specialization as well as a region’s economic and technological performance prove to be some of the most important pull factors for FDI in post-transition regions.
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