| dc.contributor.advisor | 周行一 | zh_TW |
| dc.contributor.advisor | Chow,Edward H. | en_US |
| dc.contributor.author (Authors) | 陳明憲 | zh_TW |
| dc.contributor.author (Authors) | Chen, Ming-Hsien | en_US |
| dc.creator (作者) | 陳明憲 | zh_TW |
| dc.creator (作者) | Chen, Ming-Hsien | en_US |
| dc.date (日期) | 2007 | en_US |
| dc.date.accessioned | 14-Sep-2009 09:04:18 (UTC+8) | - |
| dc.date.available | 14-Sep-2009 09:04:18 (UTC+8) | - |
| dc.date.issued (上傳時間) | 14-Sep-2009 09:04:18 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0913575041 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/31019 | - |
| dc.description (描述) | 博士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 財務管理研究所 | zh_TW |
| dc.description (描述) | 91357504 | zh_TW |
| dc.description (描述) | 96 | zh_TW |
| dc.description.abstract (摘要) | Who can survive longer and what factors could prolong the trading life of individual investors in the market? This is the questions we ask in the dissertation. Based on our knowledge, there is not any research about the issue of survival analysis on analyzing individual investors in stock market. The paper classifies three possibilities could affect the trading life of investors: personal characteristics, trading behavior, and market condition. In the dissertation, we use tick-by-tick transaction data from the Taiwan Stock Exchange to profile survivors versus non-survivors, to investigate how the traders’ characteristics (such as, gender), trading behaviors (such as the degree of diversification, trading amount and trading frequency) and market condition affect the trading life of investors. We borrow the proportional hazard models proposed by Cox (1972) who used in bio-statistics to analyze the survival rate. Using the Kaplan-Meier curves for male and female, we find that survival functions and hazard rates of female investors have better survival prognosis than the male investors. Different timing of entering results in distinct patterns of survival curves and hazard rates. Investors entering that market in the bull and bear market have a larger survival rate than those who enter the market in normal time during the trading life from 1 to 7 years. Moreover, as the trading life increases larger 7 year, the three curves of bull, bear and normal market conditions, respectively, appear to get closer, suggesting that if trading life is shorter than 7 years, the investors entering in the bull and bear markets seemly have lower hazard ratio than that in the normal market to leave the market. Finally, the results of Cox’s proportional hazard model show that female investors stay in the market 74 days longer than the male. Trading cycle increasing by one day will prolong the traders in the market by 4.8 days. Average volume per trade measured in ten thousands does not have economic effect on the trading duration, although its estimate is statistically significant. A one percentage increase of portfolio return will reduce about 151 days of the trading life. One more stock in the portfolio will prolong about 133 days in the trading life. The effect on the trading duration of trading performance of those who enter in the bull market is positive. | zh_TW |
| dc.description.abstract (摘要) | Who can survive longer and what factors could prolong the trading life of individual investors in the market? This is the questions we ask in the dissertation. Based on our knowledge, there is not any research about the issue of survival analysis on analyzing individual investors in stock market. The paper classifies three possibilities could affect the trading life of investors: personal characteristics, trading behavior, and market condition. In the dissertation, we use tick-by-tick transaction data from the Taiwan Stock Exchange to profile survivors versus non-survivors, to investigate how the traders’ characteristics (such as, gender), trading behaviors (such as the degree of diversification, trading amount and trading frequency) and market condition affect the trading life of investors. We borrow the proportional hazard models proposed by Cox (1972) who used in bio-statistics to analyze the survival rate. Using the Kaplan-Meier curves for male and female, we find that survival functions and hazard rates of female investors have better survival prognosis than the male investors. Different timing of entering results in distinct patterns of survival curves and hazard rates. Investors entering that market in the bull and bear market have a larger survival rate than those who enter the market in normal time during the trading life from 1 to 7 years. Moreover, as the trading life increases larger 7 year, the three curves of bull, bear and normal market conditions, respectively, appear to get closer, suggesting that if trading life is shorter than 7 years, the investors entering in the bull and bear markets seemly have lower hazard ratio than that in the normal market to leave the market. Finally, the results of Cox’s proportional hazard model show that female investors stay in the market 74 days longer than the male. Trading cycle increasing by one day will prolong the traders in the market by 4.8 days. Average volume per trade measured in ten thousands does not have economic effect on the trading duration, although its estimate is statistically significant. A one percentage increase of portfolio return will reduce about 151 days of the trading life. One more stock in the portfolio will prolong about 133 days in the trading life. The effect on the trading duration of trading performance of those who enter in the bull market is positive. | en_US |
| dc.description.tableofcontents | Dedication iii Acknowledgements iv List of Tables vii List of Figures viii Abstract ix Chapter 1. Introduction 1 Chapter 2. Literature Review 6 2.1 Literatures on Survival Analysis 7 2.2 Individuals’ Investing Activities in Securities Markets 13 Chapter 3. Methodology for Survival Analysis of Investors 21 3.1 Basic Concepts of Survival Analysis 21 3.2 Kaplan-Meier Product-limit Estimator and Life-Table Method 29 3.3 Parametric Survival Regression Models 31 3.4 Cox’s Proportional Hazards Model 32 Chapter 4. Data Description and Profile of Individual Investors 34 4.1 Data Description and Selection Criterions 34 4.2 Profile of Individual Investors in Taiwan 40 4.3 Research Hypothesis and Definitions of Variables 46 Chapter 5. Survival Analysis of Individual Investors 52 5.1 Life-table Analysis 52 5.2 Empirical Results of Cox’s Proportional Hazards Models 62 5.3 Robust Tests of Sensitive Analysis on Right Censored Data 71 Chapter 6. Conclusion and Discussion 74 6.1 Conclusion Remark 74 6.2 Discussion and Further Researches 78 References 80 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0913575041 | en_US |
| dc.subject (關鍵詞) | 投資行為 | zh_TW |
| dc.subject (關鍵詞) | 存活率 | zh_TW |
| dc.subject (關鍵詞) | 散戶 | zh_TW |
| dc.subject (關鍵詞) | 個別投資人 | zh_TW |
| dc.subject (關鍵詞) | investing behaviors | en_US |
| dc.subject (關鍵詞) | survival analysis | en_US |
| dc.subject (關鍵詞) | proporation hazard model | en_US |
| dc.subject (關鍵詞) | individual investor | en_US |
| dc.title (題名) | 台灣股票市場散戶存活率之研究 | zh_TW |
| dc.title (題名) | How and Why Individual Investors Quit? | en_US |
| dc.type (資料類型) | thesis | en |
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