本文的研究目的是探討在破產模型中納入 Google Index 後，是否能夠提升預測企業發生財務危機的準確性。本研究的研究對象是所有上市公司，樣本期間為2004年到 2017年。本文的研究結果如下。 第一，整體而言，我們發現Google Index對於預測公司破產的機率有顯著的解釋力。第二，進一步發現，不論是市場投資人字詞還是負面字詞在破產模型中都有顯著正向影響，其中市場投資人字詞的解釋能力較負面字詞佳。第三，進行單一字詞分析時，我們發現投信、外資、散戶、造假、虧損、裁員和更換CEO對於預測破產機率有顯著性。

The purpose of the research is ,when we add Google Index into the bankruptcy model, whether the accuracy raising or not. Our research samples are Taiwan-listed company. The sample period is from 2004 to 2017. Over all, the result is listing as follows. First, we find that Google Index is statistically significant in the bankruptcy model. Second, no matter the investor-word or the bad-word are positively significant in the model. Third, when we analyzed with only one key word, we discovered that Invesment Bank, dealer, analyst, foreign investor, individual investors, fraud, fake, deficit, layoff, CEO replacement are positively significant in the financial crisis model.

第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 　 2
第三節 研究架構 3
第二章 文獻回顧與探討 5
第一節 網路搜索熱度相關文獻 5
第二節 破產預測之相關文獻 6
第三章 研究設計 9
第一節 資料來源與樣本選取 9
第二節 樣本配對方法 10
第三節 控制變數之衡量 12
第四節 統計方法與研究模型 12
一. Z-score模型 12
二. Google trend 介紹 14
三. 主成分分析 15
四. 二元羅吉斯迴歸模型 (Binary Logistic Regression Model) 17
第五節 研究模型 18
第四章 實證結果 21
第一節 樣本分配與敘述統計 21
第二節 差異性檢定與相關係數 22
第三節 主成分係數矩陣 23
第四節 GOOGLE INDEX對於破產模型的影響 23
第五章 結論 29

高蘭芬. (2002). 董監事股權質押之代理問題對會計資訊與公司績效之影響. 成功大學會計學系研究所博士論文, 1-128.
French, K. R., and Roll, R. (1986). Stock return variances: The arrival of information and the reaction of traders. Journal of Financial and Economics, 17(1), 5-26.
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting, 109-131.
Veldkamp, L., and Farboodi, M. (2016). The Long-Run Evolution of the Financial Sector. In 2016 Meeting Papers , 530, . Society for Economic Dynamics.
Hendershott, T., Livdan, D., and Schürhoff, N. (2015). Are institutions informed about news?. Journal of Financial and Economics, 117(2), 249-287.
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.
Frakes, W. B., and Baeza-Yates, R. (Eds.). (1992). Information retrieval: Data structures and algorithms , 331, . Englewood Cliffs, New Jersey: prentice Hall.
Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139-1168.
Fang, L., and Peress, J. (2009). Media coverage and the cross‐section of stock returns. The Journal of Finance, 64(5), 2023-2052.
Drake, M. S., Roulstone, D. T., and Thornock, J. R. (2012). Investor information demand: Evidence from Google searches around earnings announcements. Journal of Accounting, 50(4), 1001-1040.
Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., and Chen, J. (2016). Deep speech 2: End-to-end speech recognition in English and mandarin. In International Conference on Machine Learning , 173-182.
Demers, E., and Vega, C. (2011). Linguistic tone in earnings announcements: News or noise. FRB International Finance Discussion Paper, 951.
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting, 71-111.
Collins, R. A., and Green, R. D. (1982). Statistical methods for bankruptcy forecasting. Journal of Economics and Business, 34(4), 349-354.
Odom, M. D., and Sharda, R. (1990, June). A neural network model for bankruptcy prediction. In Neural Networks, 1990., 1990 IJCNN International Joint Conference on , 163-168.
Tam, K. Y., and Kiang, M. Y. (1992). Managerial applications of neural networks: the case of bank failure predictions. Management Science, 38(7), 926-947.
Tseng, F.-M., and Hu, Y.-C. (2010). Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems with Applications, 37(3), 1846-1853.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A., and Vishny, R. W. (1997). Legal determinants of external finance. Journal of Finance, 1131-1150.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A., and Vishny, R. (2000). Investor protection and corporate governance. Journal of Financial and Economics, 58(1), 3-27.
La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R. W. Vishny. 1999. Corporate ownership around the world. Journal of Finance , 54, 471-517.
Core, J., W. Guay, and T. Rusticus,(2006),“Does Weak Corporate Governance Cause Stock Returns? Journal of Finance, 61(2), 655-687.
Rosenbaum, P. R., and Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.
Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting , 59-82.
Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business, 74(1), 101-124.
Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559-572.
J. Latin, D. Carroll, P. E. Green, ”Analyzing Multivariate Data,”2003, Duxbruy.
A. C. Rencher, ”Multivariate Statistical Inference and Applications,” (1998) , John Wily and Sons.
Meaney, M. J., Diorio, J., Francis, D., Widdowson, J., LaPlante, P., Caldji, C., and Plotsky, P. M. (1996). Early Environmental Regulation of Forebrain Glucocorticoid Receptor Gene Expression: Implications for Adrenocortical Responses to Stress , 61–72. Developmental neuroscience, 18(1-2), 61-72.