臺灣博碩士論文加值系統

The financial crisis that escalated over a decade ago had severe devastating effects that lasted for years. It left behind in its trail both economic and social side effects far from being favorable. While the phenomenon inclined many researchers to devise prediction models which act as security for anticipating future financial turmoil, there has been little attention brought upon corporate operating performance, an important business factor that has been widely deemed to be one of the main causes of financial crisis.
Considering the research gap, this study introduces a synthesized structure wherein qualitative and quantitative approaches are fused together to build a hybrid operating performance forecasting model. It is generally considered that warning signs for financial distress are recognizable, provided that, investors are equipped with a proficient and reliable forecasting system. Through the proposed model, managers and investors alike will be able to anticipate the problem and make changes where necessary.
The focal point of the quantitative information is on publicly listed companies in the semiconductor industry, using financial ratios as predictors of operating performance. However, since merely utilizing financial ratios is distinguished as insufficient and results in an unconvincing prediction model, this study supplements numerical analysis with textual analysis. Quantitative data is derived from computational linguistics in the form of annual report readability.
The research covers the years spanning 2016 – 2018 and focuses on the semiconductor industry as it comprises some of Taiwan’s largest companies and is considered to be a major contributor to the economy. Results are then logged into support vector machines (SVMs), a type of AI-based technique, grounded on the statistical learning theory and structural risk minimization (SRM) principle. The SVMs method has demonstrated its superior generalization ability in several forecasting tasks in the past few years.
The presented model supplements numerical study with textual analysis in order to generate a more robust and reliable performance forecasting method that serves as a better gauge for future financial crises.

CONTENTS
ABSTRACT iii
ACKNOWLEDGEMENT v
CONTENTS vi
LIST OF TABLES viii
LIST OF FIGURES ix
CHAPTER ONE INTRODUCTION 1
1.1 Research Background and Motivation 1
1.2 Research Overview 1
1.3 Research Objectives 3
1.4 Structure of the Study 4
1.5 Research Outline 5
CHAPTER TWO LITERATURE REVIEW 6
2.1 Prediction Models in Business 6
2.2 Financial Ratio Analysis 8
2.3 Corporate Disclosure Readability 10
CHAPTER THREE METHODOLOGY 18
3.1 Research Design 18
3.2 Variable Selection: Financial Ratios 20
3.4 Support Vector Machines (SVMs) 26
CHAPTER FOUR EMPIRICAL ANALYSIS 34
4.1 Descriptive Statistics 34
4.2 Robustness Practices 36
4.3 Forecasting Model Results 39
CHAPTER FIVE CONCLUSIONS & SUGGESTIONS 48
5.1 Conclusions 48
5.2 Research Contributions 49
5.3 Research Limitations 49
5.4 Suggestions for Future Work 49
REFERENCES 51
APPENDICES 61
Appendix 1: Statistics-Based Classifiers Used in Previous Literature 61
Appendix 1b: Artificial Intelligence-Based Classifiers Used in Previous Literature 62
Appendix 2: Unavailable/ Deleted Data Samples 64

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