The prediction of bank acquisition targets with discriminant and logit analyses: Methodological issues and empirical evidence

Pasiouras, Fotios; Tanna, Sailesh

doi:10.1016/j.ribaf.2009.01.004

Cited by 22 publications

(28 citation statements)

References 43 publications

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“…In line with previous studies, we partition our data by selecting 80% of our sample as the training set and the remaining 20% as the testing set (Geng et al, 2015;Doumpos et al, 2017;Routledge et al, 2017). In addition, we select our testing set from a future period rather than in random (Pasiouras et al, 2008;Pasiouras and Tanna, 2010). We do so, in order to test our model against a future period.…”

Section: Discussionmentioning

confidence: 99%

“…The logistic regression model (LOGIT) is probably the most popular predictive model in finance (Hasbrouck, 1985;Palepu, 1986;Ambrose and Megginson, 1992;Barnes, 1998;Powell, 2001;Espahbodi and Espahbodi, 2003;Pasiouras and Tanna, 2010;Veganzones and Severin, 2018;Mai et al, 2019;Ly and Nguyen, 2020). LOGIT estimates a non-linear sigmoid function between the binary output and the control variables.…”

Section: Logistic Regressionmentioning

confidence: 99%

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Textual Information and IPO Underpricing: A Machine Learning Approach

et al. 2020

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This study examines the predictive power of textual information from S-1 filings in explaining IPO underpricing. Our empirical approach differs from previous research, as we utilize several machine learning algorithms to predict whether an IPO will be underpriced, or not. We analyze a large sample of 2,481 U.S. IPOs from 1997 to 2016, and we find that textual information can effectively complement traditional financial variables in terms of prediction accuracy. In fact, models that use both textual data and financial variables as inputs have superior performance compared to models using a single type of input. We attribute our findings to the fact that textual information can reduce the ex-ante valuation uncertainty of IPO firms, thus leading to more accurate estimates.

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Section: Discussionmentioning

confidence: 99%

Section: Logistic Regressionmentioning

confidence: 99%

Textual Information and IPO Underpricing: A Machine Learning Approach

et al. 2020

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“…To enhance evaluation accuracy, various prediction techniques have been formulated and introduced, which can be categorized into four groups: expert system approaches (Altman 1968;Somerville and Taffler 1995), traditional econometric models (Doumpos et al 2001;Pasiouras and Tanna 2010;Yim and Mitchell 2005), artificial intelligence (AI) techniques (Blanco et al 2013;Yu and Yao 2013;Han et al 2013), and their hybrids (Yim and Mitchell 2005;Lee et al 2002;Chen and Huang 2003;Lee and Chen 2005;Hsieh 2005). Moreover, the explanatory variables (or evaluation indexes) play an important role in credit rating prediction and vary across different target agents.…”

Section: Introductionmentioning

confidence: 99%

Social credit: a comprehensive literature review

Tang

et al. 2015

Financial Innovation

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To avoid credit fraud, social credit within an economic system has become an increasingly important criterion for the evaluation of economic agent activity and guaranteeing the development of a market economy with minimal supervision costs. This paper provides a comprehensive review of the social credit literature from the perspectives of theoretical foundation, scoring methods, and regulatory mechanisms. The study considers the credit of various economic agents within the social credit system such as countries (or governments), corporations, and individuals and their credit variations in online markets (i.e., network credit). A historical review of the theoretical (or model) development of economic agents is presented together with significant works and future research directions. Some interesting conclusions are summarized from the literature review. (1) Credit theory studies can be categorized into traditional and emerging schools both focusing on the economic explanation of social credit in conjunction with creation and evolution mechanisms. (2) The most popular credit scoring methods include expert systems, econometric models, artificial intelligence (AI) techniques, and their hybrid forms. Evaluation indexes should vary across different target agents. (3) The most pressing task for regulatory mechanisms that supervise social credit to avoid credit fraud is the establishment of shared credit databases with consistent data standards.

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“…Thus, the authors try to identify variables that act as "early warning signals" for crises. Other studies apply classical statistical techniques such as discriminant, logit or probit analysis [4,5]. However, although the obtained results have been satisfactory, all these techniques present the drawback that they make some assumptions about the model or the data distribution that are not usually satisfied.…”

Section: Introductionmentioning

confidence: 99%

Generalized Logistic Regression Models Using Neural Network Basis Functions Applied to the Detection of Banking Crises

Gutiérrez

Salcedo‐Sanz

Vargas

et al. 2010

Trends in Applied Intelligent Systems

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Abstract. The financial system plays a crucial role in economic development. Financial crises are recurrent phenomena in modern financial systems. The literature offers several definitions of financial instability, but it is well known that a financial crisis with a banking crisis is the most common example of financial instability. In this paper we introduce a novel model for detection and prediction of crises, based on the hybridization of a standard logistic regression with Product Unit (PU) neural networks and Radial Basis Function (RBF) networks. These hybrid approaches are described in the paper, and applied to the detection and prediction of banking crises by using a large database of countries in the period 1981 to 1999. The proposed techniques are shown to perform better than other existing statistical and artificial intelligence methods for this problem.

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The prediction of bank acquisition targets with discriminant and logit analyses: Methodological issues and empirical evidence

Cited by 22 publications

References 43 publications

Textual Information and IPO Underpricing: A Machine Learning Approach

Textual Information and IPO Underpricing: A Machine Learning Approach

Social credit: a comprehensive literature review

Generalized Logistic Regression Models Using Neural Network Basis Functions Applied to the Detection of Banking Crises

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