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    Title: 財務市場資訊不對稱下之市場現象與參與者行為之研究
    Authors: 謝易霖
    Contributors: 陳樹衡
    謝易霖
    Keywords: 資訊不對稱
    資訊散佈
    雙方喊價市場
    實驗經濟學
    強化學習模型
    Date: 2007
    Issue Date: 2009-09-18 16:06:20 (UTC+8)
    Abstract: 財務市場資訊不對稱的現象已由不少學者研究, 本文利用真人實驗方法對此一議題再檢驗, 依照擁有資訊的程度分為: 完全知訊者、不完全知訊者與外部者。結果發現, 價格收斂情形與知訊者的多寡有顯著相關, 然而卻與知訊品質的高低相關性較低。成交量與價格收斂情形呈反向關係, 雖顯著但相關性有限, 我們推測對資產定價的落差雖是交易動機的原因之一,但並非僅只有此原因。市場內財富差異性亦與價格收斂有所相關, 價格收斂越好的市場, 市場吉尼係數就越小, 顯示參與者間貧富落差越小。與過去文獻差異較多是擁有較多資訊的參與者不見得有較好的利得,因此, 擁有資訊的程度不再是決定利得的唯一因素, 策略的選擇將是影響利潤的重要因素之一。

    根據實驗結果, 發現限價單使用比例與期末利得有顯著的正相關, 且排名較為前面的參與者能較快學習到此一結果。本文將限價單使用比例的增減做為一策略選擇, 並利用三種強化學習模型解釋市場現象, 此三種模型皆從Roth 與Erev 的文獻中而來, 前二種模型中有二種參數: 新進因子與經驗因子, 新進因子表示前一期策略的動機對本期採同一策略動機的影響,經驗因子則表示前其策略所引發的利潤對本期策略動機的影響, 此一參數亦隱含了參與者強化學習之能力。第三種模型則多增加了參與者對利潤敏感的的測度。結果發現, 無論是此三種模型的何種參數, 在不同資訊結構的市場與不同類型的參與者間幾無差異。然而, 若以參與者利得的表現區分, 參與者對過去利潤的反應, 即經驗因子, 有顯著的差異, 說明了利潤高低與是否能從過去利潤結果學習到經驗(即強化學習能力) 有密切關係。上述三個現象說明, 參與者的行為參數在進入實驗室前就已決定了,因此利用市場環境與參與者身份將之分類比較的差異性不大, 但這樣的差異卻會影響之後的利得。故本文的結論與過去文獻不同的是, 在此實驗中決定參與者利得
    多寡的不再是資訊掌握程度, 而是其學習(策略) 之能力。
    Reference: 中文部分:
    謝宗林(1988)。股票市場與內線交易。經濟前瞻第九號。
    唐恕(民87)。異質訊息下內線交易與交易策略與市場績效之研究。輔仁大學金融研究所碩士論文。
    詹場.胡星陽(2001.7)。流動性衡量方法之綜合評論。國家科學委員會研究彙刊。11卷3期。205-221。
    張萬同(2003)。衍生性金融市場之管理、運作、及效率。桂冠圖書。
    周星.林清勝(2004)。實驗經濟學最新發展動動態述評。學術月刊
    楊曉嵐.金雪軍(2005)。基於實驗經濟學方法的證卷市場信息有效性研究。浙江大學學報,Vol.35,No6,p80–88。
    李家瑋(民94)。比較遺傳演算法語強化學習: 以代理人彩券市場為例。國立政治大學經濟學系碩士論文。
    Bibliography
    Anand, A. and Martell, T. (2001). ”informed” limit order trading. working paper.
    Ang, J. and Schwarz, T. (1985). Risk aversion and information structure:An experimental study of price variability in securities markets.The Journal of Finance, 40:825–844.
    Banks, J. (1985). Price-conveyed information versus observed insider behavior: A note on rational expectations convergence. The Journal of Political Economy, 94(4):807–81.
    Chan, N., LeBaron, B, L. A., and Poggio, T. (1998). Information dissemination and aggregation in asset markets with simple intelligent traders. working paper.
    Chmura, T. and Piz, T. (2007). An extended reinforcement algorithm for estimation of human behaviour in experimental congestion games. Journal of Artificial Societies and Social Simulation,10(2).
    Cliff, D. (2001). Evolution of market mechanism through a continuous space of auction-types. Techreport HPL-2001-326, Information Infrastructure Laboratory HP Laboratories Bristol.
    Duffy, J. and Unver, M. (2006). Asset price bubble and crashes with near-zero-intelligence traders. Economic theory, 27:537–563.
    Erev, I. and Roth, A. E. (1998). Prediciting how people play games: Reinforcement learning in experimental games with unique, mixed strategy euqilibri. The American Economic Review, 88(4):848–881.
    Fama, E. (1970). Efficient capital markets: a review of theory an empirical work. The Journal of Finance, 25(2):383–417.
    Feltovich, N. (2000). Reinforcement-based vs. belief-based learning models in experimental asymmetric-information games. Econometrica, 68(3):605–641.
    Figlewski, S. (1978). ”market efficiency” in a market with heterogeneous
    information. The Journal of Political Economy, 86(4):581–
    579.
    Flood, M., Koedijk, K., van Dijk, M., and van Leeuwen, I. (2002). Dividing the pie: Asymmetrically informed dealers and market transparency.working paper.
    Forsythe, R. and Lundholm, R. (1990). Information aggregation in an experimental market. Econometrica, 58(2):309–347.
    Foster, F. and Viswanathan (1994). Strategic trading with asymmetrically informed traders and long-lived information. The Journal of Financial and Quantitative Analysis, 29(24):499–518.
    Friedman, D. and Sunder, S. (1994). Experimental methods: A primer for economists. Cambridge university press.
    Gil-Bazo, J.and Moreno, D. and Tapia, M. (2005). Price dynamics, informational efficiency and wealth distribution in continuous double auction markets. working paper.
    Glosten, L. and Milgrom, P. (1985). Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. Journal of Financial Economics, 114:71–100.
    Gode, D. K. and Sunder, S. (1993). Allocative efficiency of markets with zero-intelligence traders: Market as a partial substutute for individual rationality. The Journal of Political Economy, 101(1):119-137.
    Gresik, T. A. and Satterthwaite, M. A. (1985). The rate at which a simple market becomes efficient as the number of traders increases:An asymptotic result for optimal trading mechanisms. Discussion Paper.
    Grossklags, J. and Schmidt, C. (2006). Software agents and market (in) efficiency: a human trader experiment. In Forthcoming in IEEE Trans SMC Part C., Special Issue on Game-theoretic Analysis & Simulation of Negotiation Agents.
    Kyle, A. (1985). Continuous auctions and insider trading. Econometrica, 53(6):1315–1336.
    LeRoy, S. F. and LaCivita, C. (1981). Risk aversion and the dispersion of asset price. The Journal of Business, 54(4):535–547.
    Matsui, H., Koyama, Y., and Ishiyama, K. (2005). A report of large scale gaming simulation using a u-mart system in economic education.In Proceedings of the Third International Conference on Creating, Connecting and Collaborating through Computing.
    Nakajim, Y., Ono, I., Sato, H., Mori, N., Kita, H., Matsui, H., Taniguchi, K., Deguchi, H., Terano, T., and Shiozawa, Y. (2004). Introducing virtual futures market systemu-mart. In EES 2004 :Experiments in Economic Sciences - New Approaches to Solving Real-world Problems.
    O’Brien, J. and Srivastava, S. (1991). Dynamic stock markets with multiple assets: an experiment analysis. The Journal of Finance, 46(5):1111–1138.
    Plott, C. and Sunder (1988). Rational expectation and the aggregation of diverse information in laboratory security markets. Econometrica, 56(5):1085–1118.
    Plott, C. and Sunder, S. (1982). Efficiency of experimental security markets with insider information: an application of rational expectation models. The Journal of Political Economy, 90(40):663–698.
    Roth, A. E. and Erev, I. (1995). Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term. Games and Economic Behavior, 8:164–212.
    Sarin, R. and Vahid, F. (2001). Predicting how people play games: A simple dynamic model of choice. Games and Economic Behavior,34:104–122.
    Sato, H., Matsui, H., Ono, I., Kita, H., Terano, T., Deguchi, H., and Shiozawa, Y. (2001). U-mart project: learning economic principles from the bottom by both human and software agents. In JSAI2001 workshop, pages 121–131.
    Sunder, S. (1995). Experimental asset markets: A survey  , The handbook of experimental economics. Princeton university press.
    Theissen, E. (2000). Market structure, informational efficiency and liquidity: An experimental comparison of auction and dealer markets. Journal of financial markets, 3:333–363.
    Victoire, T. and Suganthan, P. N. (2007). Differential evolution and evolutionary programming for solving non-convex economic dispatch problems. Technical report, Agency for Science, Technology and Reasearch. Agency for Science, Technology and Reasearch.
    Wang, S., Tseng, J., Tai, C., Lai, K., Chen, S., and Li, S. (2008). Network topology of an experimental futures exchange. European Physical Journal B, 26:105–111.
    Description: 碩士
    國立政治大學
    經濟研究所
    93258012
    96
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0932580121
    Data Type: thesis
    Appears in Collections:[經濟學系] 學位論文

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