本文選題：技術(shù)分析 + 市場有效性�。� 參考：《復(fù)旦大學(xué)》2014年博士論文

【摘要】：技術(shù)分析作為一種經(jīng)典的投資分析工具,在現(xiàn)實中經(jīng)久不衰,蒙受了大量投資者的青睞。但是由于理論依據(jù)的缺乏和方法上的主觀性,加之與弱式有效市場假說的矛盾,技術(shù)分析在金融學(xué)術(shù)界中始終備受爭議。理論與現(xiàn)實形成了鮮明的對比,這一反常現(xiàn)象至今尚未有充分合理的解釋。本文采用了理論建模和實證檢驗相結(jié)合的研究方法,探尋了技術(shù)分析被廣泛使用的深層次原因。通過建立三個簡單的資產(chǎn)定價模型,本文從多角度解釋了技術(shù)分析能夠產(chǎn)生經(jīng)濟收益的可能原因,其中：信息發(fā)現(xiàn)模型假設(shè)市場中存在信息不對稱以及信息傳播上的摩擦,技術(shù)分析可幫助推測市場中與基本面價值相關(guān)的非公開信息；趨勢追隨模型假設(shè)市場中存在大批非理性的噪音交易者,技術(shù)交易規(guī)則捕捉到了他們的趨勢交易行為,并確有可能獲利；羊群效應(yīng)模型假設(shè)大量技術(shù)交易者使用同質(zhì)的交易規(guī)則,他們自身的羊群買賣行為推動了價格走勢向著他們所期望的方向“自我實現(xiàn)”�；�2000-2012年中國A股市場的實證結(jié)果表明,雙重移動均線和交易區(qū)間突破兩類規(guī)則可產(chǎn)生最為顯著的經(jīng)濟收益,年化超額回報率分別達(dá)到了5.18%和2.85%。而相對價格指數(shù)、亞歷山大過濾、價格形態(tài)以及K線圖等規(guī)則,在考慮了交易成本后,并不能產(chǎn)生穩(wěn)健的超額回報率。本文采用了多種較為前沿的計量技術(shù)和先進(jìn)的實證方法來解決數(shù)據(jù)過度挖掘和風(fēng)險調(diào)整的問題。基于898家上市公司股票的面板數(shù)據(jù)回歸分析結(jié)果進(jìn)一步表明,信息不透明程度較高、技術(shù)交易者數(shù)量較少、投資者情緒較弱的公司股票,所錄得的技術(shù)分析超額回報率較高,支持了信息發(fā)現(xiàn)模型的預(yù)測,但是與趨勢追隨模型和羊群效應(yīng)模型的預(yù)測相抵觸。這說明技術(shù)分析并非是盲目的噪音交易行為,而是市場暫時失效時,投資者獲取信息的一種必要手段。
[Abstract]:As a classical investment analysis tool, technical analysis has been favored by a large number of investors for a long time in reality. However, due to the lack of theoretical basis, the subjectivity of methods and the contradiction with the weak efficient market hypothesis, technical analysis has always been controversial in the financial academia. There is a sharp contrast between theory and reality, which has not yet been fully explained. In this paper, theoretical modeling and empirical testing are used to explore the deep reasons why technical analysis is widely used. Through the establishment of three simple asset pricing models, this paper explains the possible reasons for the economic benefits of technical analysis from various angles. Among them, the information discovery model assumes that there is information asymmetry and information transmission friction in the market. Technical analysis can help to speculate on non-public information related to fundamental value in the market; trend following models assume that there are a large number of irrational noise traders in the market, and technical trading rules capture their trend trading behavior. The herding effect model assumes that a large number of technical traders use homogeneous trading rules, and their own herding behavior promotes the price trend to be "self-actualized" in the direction they expect. Based on the empirical results of China A-share market from 2000 to 2012, the results show that the double moving average and the trading interval break through the two kinds of rules can produce the most significant economic returns, the annualized excess returns are 5.18% and 2.85%, respectively. Rules such as relative price indices, Alexander filtering, price patterns and K charts do not produce robust excess returns when transaction costs are taken into account. This paper adopts several advanced metrological techniques and advanced empirical methods to solve the problem of data over-mining and risk adjustment. The results of panel data regression analysis based on 898 listed companies further show that the companies with higher degree of information opacity, less number of technical traders and weaker investor sentiment have higher abnormal returns on technical analysis. It supports the prediction of information discovery model, but contradicts the prediction of trend following model and herding effect model. This shows that technical analysis is not a blind noise trading behavior, but a necessary means for investors to obtain information when the market temporarily fails.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：F832.51

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本文編號：1854723