政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/38790

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/38790

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政大典藏 > College of Social Science > Department of Economics > Theses > Item 140.119/38790

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/38790

Title:	異質與分群訊息在金融市場的交易行為及績效分析 Analysis on heterogeneous and subgroup information in financial markets
Authors:	楊祐宗 Yang, Yu- Tsung
Contributors:	莊委桐楊祐宗 Yang, Yu- Tsung
Keywords:	群體異質訊息群聚行為績效分析 subgroups heterogeneous information herds performance
Date:	2009
Issue Date:	2010-04-09 17:53:02 (UTC+8)
Abstract:	金融市場中存在著許多預測機構, 他們各自召集信眾並且不時地釋放訊息給其會員好讓他們能在交易中獲利。每筆訊息皆代表著各機構對此資產價值的預測, 會員則依此訊息至市場上尋找機會交易。他們交易前會先理性地觀察市場過往的波動。如果市場走勢所預測的訊息與自己的訊息一致, 那交易者交易時大概不會有所顧慮。然而當市場趨勢與自己的訊息不一致時, 交易者勢必會陷入兩難。仔細地衡量斟酌兩股力量的輕重後, 進而選擇他覺得對的決定。如果交易者放棄自己的訊息而追隨前人交易的腳步, 那我們可定義這是一種群聚的行為。如果某一機構的會員人數龐大, 則他們勢必會影響市場價格的波動。不知情的交易者在看到價格趨勢如此時, 可能會放棄自己的訊息轉而追隨過往交易者的選擇。然而此種交易伴隨著風險, 因為不知道正確的訊息為何, 當價格已經達到機構所預測的目標時, 知情的會員便開始反向操作, 而不知情的交易者可能會持續地採取此一交易策略。於是當資產真正價值揭露時, 不知情交易者便可能因此被套牢。跟隨大眾的決策相對保險, 但是當追隨的人沒有額外的訊息無法查覺情勢的變化時, 便可能面臨損失的風險。我們建構了一個存在兩種類型交易者的市場, 一方是沒有參加機構的一般交易者, 另一方是同時參加某一機構的會員交易者。透過私有訊息與公開歷史交易預測的權衡, 交易者必須想辦法在這一次機會的交易中獲利。而我們想找出是否對任何交易者而言, 參加預測機構是有利可圖的。想當然爾, 市場中會員交易者的多寡對於機構預測目標價位的達成頗為重要, 因為影響力的大小間接決定了市場雙方的利潤。當然每位交易者對訊息的信心也有所不同, 這些因素都會影響雙方的利潤。而本篇論文即是嘗試找出在哪種條件之下, 參與機構交易者的交易績效會比沒有參與機構交易者的績效為佳。 Traders with their own heterogeneous hidden information are coming to the market to trade in order to maximize their expected profits. They will observe the trends of prices and compare it to their private signals and then make the right decisions. The trends might not consistent with the private signals. If the traders choose to abandon his own signals and follow the actions made by predecessors, we called the action “Herds.” In this paper, we set a mechanism to harmonize with these two powers. Also we put the traders into two subgroups, and one of the groups will send another signal to its members. For simplicity, we use a sequential trading model to see the trade patterns. Since we use the closing price to measure traders’ profits, traders in the market need to presume what the closing price will be. Then we calculate the profits of each group and find out their performance. We want to see under what kind of conditions, the performance of one group will be better than that of another group. If we can find the conditions of better performance, it is worth for the traders to join that group.
Reference:	[1] Abhijit V. Banerjee (1992), “A Simple Model of Herd Behavior,” The Quarterly Journal of Economics, Vol. 107, No 3, pp. 797-817. [2] Adam Copeland (2004), “Learning Dynamics with Private and Public Signals,” working paper. [3] Andreas Park and Hamid Sabourian (2008), “Herding and Contrarian Behavior in Financial Markets,” working paper. [4] Bogachan Celen and Shachar Kariv (2003), “Distinguishing Informational Cascades from Herd Behavior in the Laboratory,” working paper. [5] Brian W. Rogers (2003), “The Timing of Social Learning,” working paper. [6] Daron Acemoglu, Munther A. Dahleh, Ilan Lobel, and Asuman Ozdaglar (2008), “Bayesian Learning in Social Networks,” working paper. [7] David S. Scharfstein and Jeremy C. Stein (1990), “Herd Behavior and Investment,” The American Economic Review, Vol. 80, No. 3, pp. 465-479. [8] Helios Herrera and Johannes Horner (2008), “Social Learning with a Hidden Action,” working paper. [9] Jonathan E. Alevy, Michael S. Haigh, and John A. List (2003), “Information Cascades with Financial Market Professionals: An Experimental Study,” NCR-134. [10] Lawrence R. Glosten and Paul R. Milgrom (1985), “Bid, Ask and Transaction Prices in a Specialist Market with Heterogenously Informed Traders,” Journal of Financial Economics, 14, pp. 71-100. [11] Lisa R. Anderson and Charles A. Holt (1997), “Information Cascades in the Laboratory,” The American Economic Review, Vol. 87, No. 5, pp. 847-862. [12] Luis Angel Medrano and Xavier Vives (2001), “Strategic Behavior and Price Discovery,” The RAND Journal of Economics, Vol. 32, No. 2, pp. 221-248. [13] Manuel Amador and Pierre-Olivier Weill (2006), “Learning from Private and Public Observation of Other’s Actions,” MPRA Paper, No. 109, posted 07. [14] Marco Cipriani (2002), “Rational Herds, Speed of Learning and Contagion in Financial Markets,” working paper. [15] Marco Cipriani and Antonio Guarino (2005), “Herd Behavior in a Laboratory Financial Market,” The American Economic Review, Vol. 95, No. 5, pp. 1427-1443. [16] Marco Cipriani and Antonio Guarino (2007), “Estimating a Structural Model of Herd Behavior in Financial Markets,” working paper. [17] Marco Cipriani and Antonio Guarino (2008), “Herd Behavior in Financial Markets: An Experiment with Financial Market Professionals,” IMF working paper, WP/08/141. [18] Robert H. Jennings, Laura T. Starks, and John C. Fellingham (1981), “An Equilibrium Model of Asset Trading with Sequential Information Arrival,” The Journal of Finance, Vol. 36, No. 1, pp. 143-161. [19] Sushil Bikhchandani, David Hirshleifer, and Ivo Welch (1992), “A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades,” The Journal of Political Economy, Vol. 100, No. 5, pp. 992-1026. [20] Sushil Bikhchandani and Sunil Sharma (2000), “Herd Behavior in Financial Markets: A Review,” IMF working paper, WP/00/48.
Description:	碩士國立政治大學經濟研究所 96258013 98
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0096258013
Data Type:	thesis
Appears in Collections:	[Department of Economics] Theses

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