Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/115755
DC FieldValueLanguage
dc.contributor.advisor陳樹衡zh_TW
dc.contributor.advisorChen, Shu Hengen_US
dc.contributor.author沈佩璇zh_TW
dc.contributor.authorShen, Pei Hsuanen_US
dc.creator沈佩璇zh_TW
dc.creatorShen, Pei Hsuanen_US
dc.date2018en_US
dc.date.accessioned2018-02-02T03:35:12Z-
dc.date.available2018-02-02T03:35:12Z-
dc.date.issued2018-02-02T03:35:12Z-
dc.identifierG0104258021en_US
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/115755-
dc.description碩士zh_TW
dc.description國立政治大學zh_TW
dc.description經濟學系zh_TW
dc.description104258021zh_TW
dc.description.abstractzh_TW
dc.description.abstractBehavioral finance is the study of the influence of psychology on the behaviors of financial practitioners and the subsequent effect on the markets. Although behavioral finance theory has been popular for many years, empirical studies only become possible recently, thanks to the advancement of technology and the availability of data and tools. This research adopts an empirical approach to investigate how investors’ attention and interview sentiments influence Taiwan stock market. In particular, we identify the psychological factors that have an impact on Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX).\n In addition to TAIEX data, including TAIEX prices and trading volume, two other data sources have been used in this study: (1) Investor Confidence Index interview data provided by J.P. Morgan Asset Management, representing investors’ interview sentiments and (2) search volume data from Google Trends, symbolizing investors’ attention. We first analyzed weekly data from January 5, 2014 to November 6, 2016, and then ran regression on the data, under the Newey-West correction of standard errors method, to identify the effects of investors’ attention and interview sentiments on TAIEX. \n We have found many interesting results. First, we discovered the investors in the Taiwan stock market normally use company names, not ticker symbols, to conduct Google search for information related to investment decisions. Second, investors’ attention based on the Google Search Volume Index (SVI) searched by company names is significantly and positively correlated with the average returns of TAIEX, which agrees with the attention hypothesis of Barber and Odean (2007). Third, we verified the hypothesis of Barber and Odean (2007) that the positive trend of SVI is an indication of investors’ intention of purchasing a stock. Fourth, investors’ interview sentiment of Taiwan Stock Price Index is negatively correlated with the average returns of TAIEX, which supports the overconfident hypothesis proposed by De Bondt and Thaler (1995). By contrast, their interview sentiment of Taiwan Economic Situation Index is positively correlated with the average returns of TAIEX. Finally, trading volume is positively related to the average returns of TAIEX, which aligns with that reported in Chuang, Ouyang, and Lo (2010).en_US
dc.description.tableofcontents1. Abstract i\n2. Introduction 1\n 1. Research Motivation 1\n 2. Contributions 2\n 3. Organizations 4\n3. Background and Related Works 5\n 1. Investors’ Psychology and Behaviours 5\n 2. Related Works 7\n 3. Summary 11\n4. Research Methods 13\n 1. Taiwan Economic Journal Data 13\n 2. J. P. Morgan Asset Management Confidence Indexes 13\n 3. Google Trends Data 16\n 4. Newey-West Correction of Standard Errors 18\n 5. Summary 20\n5. Research Results and Analysis 21\n 1. Variables with a Significant Impact on Average Stock Returns 22\n 2. Ticker Symbols vs. Company Names: SVIs for Investors Attention 25\n 3. Analysis of Investors’ Behaviours 30\n 4. Verification of Investors Buying Attention 33\n 5. Short-Term Average Returns Prediction. 37\n 6. Summary 39\n6. Conclusions and Outlooks 40\n 1. Review of Research Findings 40\n 2. Applications of the Study 42\n 3. Limitations of the Study 42\n 4. Recommendations for Future Research 43\n7. Appendix 45\n 1. Newey-West Correction of Standard Errors Method Explained 45\n 2. Code 49\n8. References 51zh_TW
dc.format.extent730048 bytes-
dc.format.mimetypeapplication/pdf-
dc.source.urihttp://thesis.lib.nccu.edu.tw/record/#G0104258021en_US
dc.subject認知金融zh_TW
dc.subject搜尋量指數zh_TW
dc.subject股票市場zh_TW
dc.subject投資行為zh_TW
dc.subjectCognitive financeen_US
dc.subjectGoogle search volume indexen_US
dc.subjectStock marketen_US
dc.subjectBehavior of investorsen_US
dc.titleGoogle搜尋趨勢與認知金融:從臺灣股票市場學到的新知zh_TW
dc.titleGoogle Trends and Cognitive Finance : Lessons Gained from the Taiwan Stock Marketen_US
dc.typethesisen_US
dc.relation.referenceAouadi, A., Arouri, M., & Teulon, F. (2013). Investor attention and stock market activity: Evidence from France. Economic Modelling, 35, 674-681. \nAndrade, S. C., Chang, C., & Seasholes, M. S. (2008). Trading imbalances, predictable reversals, and cross-stock price pressure. Journal of Financial Economics, 88(2), 406-423.\nAndrews, D. W. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica: Journal of the Econometric Society, 817-858.\nBaker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The Journal of Finance, 61(4), 1645-1680.\nBank, M., Larch, M., & Peter, G. (2011). Google search volume and its influence on liquidity and returns of German stocks. Financial markets and portfolio management, 25(3), 239-264.\nBarber, B. M., & Odean, T. (2007). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. The Review of Financial Studies, 21(2), 785-818.\nBarber, B. M., Odean, T., & Zhu, N. (2008). Do retail trades move markets?. The Review of Financial Studies, 22(1), 151-186.\nBarber, B. M., Odean, T., & Zhu, N. (2009). Systematic Noise. Journal of Financial Markets, volume 12, issue 4, pages 547-569, Elsevier\nChuang, W. J., Ouyang, L. Y., & Lo, W. C. (2010). The impact of investor sentiment on excess returns a Taiwan stock market case. International Journal of Information and Management Sciences, 21(1), 13-28. \nChung, S. L., & Yeh, C. Y. (2009). Investor Sentiment, Regimes and Stock Returns.\n(February 13, 2009). Available at SSRN: https://ssrn.com/abstract=1342588.\nDa, Z., Engelberg, J., & Gao, P. (2009). In Search of Attention. AFA 2010 Atlanta\nMeetings Paper. Available at SSRN: http://ssrn.com/abstract=1364209\nDa, Z., Engelberg, J., & Gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461-1499.\nDaniel, K., Hirshleifer, D., and Subrahmanyam, A.(1998) Investor Psychology and Security Market Under- and Over-Reactions, Journal of Finance, 53(6):1839-1885.\nDe Bondt, W. F. & Thaler, R. H. (1995). Financial decision-making in markets and firms: A behavioral perspective. Handbooks in operations research and management science, 9, 385-410.\nFan, M. H., Liao, E. C., & Chen, M. Y. (2014, December). A TAIEX forecasting model based on changes of keyword search volume on Google Trends. In IEEE International Symposium on Independent Computing (ISIC). IEEE.\nGallant, A. R. (2009). Nonlinear statistical models (Vol. 310). John Wiley & Sons.\nKarpoff, J. M. (1987). The relation between price changes and trading volume: A survey. Journal of Financial and quantitative Analysis, 22(1), 109-126. \nKUAN, C. M. (2008). LECTURE ON Robust Tests with and without Consistent Estimation of Asymptotic Covariance Matrix. Institute of Economics Academia Sinica. Retrieved from http://homepage.ntu.edu.tw/~ckuan/pdf/Lec-HAC_0802.pdf\nLee, C., and Swaminathan, B. (2000), Price momentum and trading volume, Journal of Finance, Vol. 55, pp.2017-2069.\nLitterman, R. B. (1983). A random walk, Markov model for the distribution of time series. Journal of Business & Economic Statistics, 1(2), 169-173.\nMiller, E. M. (1977). Risk, uncertainty, and divergence of opinion. The Journal of finance, 32(4), 1151-1168.\nMcCall, J. J. (1970). Economics of information and job search. The Quarterly Journal of Economics, 113-126.\nRusso, J. E. (1974). More information is better: A reevaluation of Jacoby, Speller and Kohn. Journal of Consumer Research, 1(3), 68-72.zh_TW
item.openairecristypehttp://purl.org/coar/resource_type/c_46ec-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairetypethesis-
item.cerifentitytypePublications-
Appears in Collections:學位論文
Files in This Item:
File SizeFormat
802101.pdf712.94 kBAdobe PDF2View/Open
Show simple item record

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.