This thesis is based on three essays in corporate credit risk and economic performance analysis. Corporate bankruptcy prediction using past financial information is well established in the literature. Early studies of corporate bankruptcy prediction mainly applied statistical techniques such as discriminant analysis, logit and probit. Although, some of these models such as logit is still widely popular amongst the academics and practitioners due to its simplicity, the shortcomings of such models for bankruptcy prediction have been noticed and criticised in the literature. One of the main shortcomings is that these models as linear classification approach can not explain the possible non-linear relationship between some accounting ratios and the probability of default (PD). This issue has been addressed in the literature by introducing non-linear statistical techniques such as support vector machines (SVM). The first essay of this thesis, presented in Chapter 2, investigates the performance of SVM in corporate bankruptcy prediction and compares its performance with that of logit. This essay analyses bankruptcy risk for firms in the Asian and Pacific region using a list of financial ratios which covers different aspects of a firm's performance. The financial and credit event information for this analysis is provided by the Risk Management Institute of National University of Singapore (RMI NUS). With respect to forecasting accuracy, the findings of this analysis reveal that on average the SVM displays a higher forecasting accuracy and a more robust performance than the logit. Among several financial ratios suggested as predictors of default, leverage ratios and firm size display a higher discriminating power compared to others. Additionally, an analysis of the relationship between PD and financial ratios is provided. The accounting based models in bankruptcy analysis are mostly based on a set of measures which represents current financial position of the firms. However, these models have no indication of the status of the technology competency of a firm amongst its peers, which could be a more significant factor in the survival of a firm. Therefore, a new measure about level of firm's technological knowledge is required for a more precise assessment of firms future financial performance. Considering the rise in the technological competition and patenting activities since the 1990s and also the important role of accurate credit rating modeling in the financial stability, second essay of this thesis examined in Chapter 3 focuses on the relationship between patent applications, as an output of a firm's technological development, and financial survival. Applying a survival analysis model, this relationship is empirically tested on a longitudinal firm-level data set for the listed firms in the US which matches the patent application data from European Patent Offi ce (EPO) against a set of financial variables provided by RMI NUS. The results of this analysis reveal that patent applications are strong identifiers of low default risk companies. In a further analysis, third essay of this thesis presented in Chapter 4 focuses on the impact of patent applications on firm's economic performance. In contrast to the studies which study the overall patent portfolio indicators as proxy for innovation, on a few aspects of firm performance this essay provides a comprehensive analysis of the effect of individual patent applications on several aspects of firm performance including including profitability, leverage, liquidity, size, credit rating quality and stock return. Using the matched data set of patent application data and economic variables for the US listed firms explained earlier, this essay examines whether changing from non-patenting to patenting status when a firm files its first and subsequent applications is associated with significant changes in its firm's performance and stability. The empirical findings of this essay indicates a higher capitalisation, increased liquidity, a lower leverage and an improve in credit quality for the patenting firms.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:683629 |
| Date | January 2016 |
| Creators | Aliakbari, Saeideh |
| Contributors | Moro, R. |
| Publisher | Brunel University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://bura.brunel.ac.uk/handle/2438/12416 |
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