臺灣博碩士論文加值系統

眾多研究認為法人的交易行為攸關股票的價格走勢與投資報酬。因此，本研究探討台灣機構投資者的交易行為(包含自營商、外資券商及公股行庫)對成交量前100大的上市櫃公司之股票週報酬率是否有解釋能力，樣本期間為2000年7月至2012年5月。本文的貢獻在於利用機構投資者的交易網路來建構其交易網路密度，並加入Fama and French (1993)的三因子在本研究迴歸模型當中。實證結果發現交易網路密度對股票週報酬率具有解釋能力，其中自營商和外資券商的交易網路密度對股票週報酬呈現正相關，而公股行庫則呈現負相關。尤其是當市場不好時政府基金會與市場反向操作，顯示政府基金的護盤效果。

Many researches have concluded that the trading behaviors of institutional investors can have impact on stock price trend and its investment performance. Hence, this study investigates the trading behaviors of all institutional investors in Taiwan (including broker-dealer, foreign institutional investors, and government-owned fund) and their explanatory power to the weekly stock returns in the top 100 trading volume companies of the market from July 2000 to May 2012. The contribution of this study is to propose the network density based on institution investors’ trading network and construct the regression model combining with the Fama-French three factors. The empirical results find the network density can explain the weekly stock returns in which broker-dealer and foreign institutional investors are positively correlated and government-owned fund are negatively correlated. In particular, the government-owned fund trades against with the market when the market is in the bear market which suggests that the government-owned fund indeed stabilizes the stock market.

中文摘要 I
Abstract II
目錄 III
表目錄 IV
圖目錄 V
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的與架構 2
第二章 文獻探討 3
第三章 資料 5
第一節 資料變數簡介 5
第二節 資料簡介 9
第四章 研究方法 12
第一節 交易比例 12
第二節 上市櫃公司成交量最大的前100家公司 12
第三節 交易網路密度 12
第四節 三因子迴歸模型 14
第五節 網路密度迴歸模型及基本假設 15
第五章 實證結果與分析 21
第一節 成交量最大前100家公司 21
第二節 交易網路密度迴歸模型 24
第六章 結論與建議 51
第一節 結論 51
第二節 未來研究與建議 52
參考文獻 53

[1]Chinazzi, M., Fagiolo, G., Reyes, J. A. and Schiavo, S. (2013), “Post-mortem examination of the international financial network”, Journal of Economic Dynamics and Control, 37(8), 1692-1713.
[2]Chordia, T. and Swaminathan, B. (2000), “Trading volume and cross‐autocorrelations in stock returns”, the Journal of Finance, 55(2), 913-935.
[3]Chuang, Hongwei. (2016), “Brokers’ financial network and stock return”, the North American Journal of Economics and Finance, 36, 172-183.
[4]Close, N. (1975), “Price reaction to large transactions in the Canadian equity markets”, Financial Analysts Journal, 31(6), 50-57.
[5]Gompers, P. A. and Metrick, A. (1998), “Institutional investors and equity prices (No. w6723)”, National Bureau of Economic Research.
[6]Fama, E. F. and French, K. R. (1992), “The cross-section of expected stock returns”, the Journal of Finance, 47(2), 427-465.
[7]Fama, E. F. and French, K. R. (1993), “Common risk factors in the returns on stocks and bonds”, Journal of Financial Economics, 33(1), 3-56.
[8]Pareek, A. (2012), “Information networks: Implications for mutual fund trading behavior and stock returns”, in AFA 2010 Atlanta Meetings Paper.