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Analyzing initial public offerings' short-term performance using decision trees and SVMs
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2015 | Decision Support Systems | Citations: 0
Authors: Bastı, Eyup; Kuzey, Cemil; Delen, Dursun

Abstract: In this study, we investigated underpricing of Turkish companies in the initial ... Expand

Abstract: In this study, we investigated underpricing of Turkish companies in the initial public offerings (IPOs) issued and traded on Borsa Istanbul between 2005 and 2013. The underpricing of stocks in IPOs, or essentially leaving money on the table, is considered as an important, challenging and worthy research topic in literature. Within the proposed framework, the IPO performance in the short run and the factors that affect this short run performance were analyzed. Popular machine learning methods – several decision tree models and support vector machines – were developed to investigate the major factors affecting the short-term performance of initial IPOs. A k-fold cross validation methodology was used to assess and contrast the performance of the predictive models. An information fusion-based sensitivity analysis was performed to combine the values of individual variable importance results into a common representation. The results showed that there was underpricing in the initial public offerings of Turkish companies, although it was not as high as the underpricing determined in developed markets. The market sentiment, the annual sales amounts, the total assets turnover rates, IPO stocks sales methods, the underwriting methods, the offer prices, debt ratio, and number of shares sold were among the most influential factors affecting the short term performance of initial public offerings of Turkish companies. Collapse

Semantic filters: f-measure developing country

Topics: organizational commitment missing data website logistics management price management

Methods: decision tree classification machine learning support vector machine computational algorithm sensitivity analysis
The impact of multinationality on firm value: A comparative analysis of machine learning techniques
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2014 | Decision Support Systems | Citations: 7
Authors: Kuzey, Cemil; Uyar, Ali; Delen, Dursun

Abstract: In this study, the impact of multinationality (as measured by foreign sales rati ... Expand

Abstract: In this study, the impact of multinationality (as measured by foreign sales ratio) and fourteen other financial indicators on firm value (characterized by market capitalization and market-to-book ratio) for the period of 1997–2011 was investigated using two popular machine learning techniques: decision trees and artificial neural networks. We divided the time period of 1997–2011 into two periods; 1997–2004 and 2005–2011 to investigate the robustness of results pre- and post-IFRS implementation. To determine the relative importance of factors as the predictors of firm value, first, a number of classification models are developed; then, the information fusion based sensitivity analysis is applied to these classification models to identify the ranked order of the independent variables. Among the independent variables, multinationality was found to determine firm value only moderately. In addition to multinationality, other financial characteristics such as firm size (as measured by natural logarithm of assets), leverage, liquidity, and profitability were consistently found to be affecting firm value. Collapse

Semantic filters: f-measure developing country

Topics: organizational value missing data database system accounting

Methods: decision tree classification machine learning artificial neural network sensitivity analysis experiment

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Analyzing initial public offerings' short-term performance using decision trees and SVMs

The impact of multinationality on firm value: A comparative analysis of machine learning techniques