技術(shù)分析介紹教程課件

1、技術(shù)分析介紹技術(shù)分析介紹亞當(dāng)亞當(dāng)索拉博索拉博國際技術(shù)分析聯(lián)盟主席，國際技術(shù)分析聯(lián)盟主席，CQS技術(shù)分析總監(jiān)技術(shù)分析總監(jiān)中文翻譯由中文翻譯由河源外匯論壇河源外匯論壇友情提供友情提供北京北京2011年年9月月6日日2內(nèi)容內(nèi)容什么是技術(shù)分析技術(shù)分析的歷史技術(shù)分析 對 基本面經(jīng)濟(jì)/金融分析技術(shù)分析研究的對象及其假設(shè)技術(shù)分析的類型（圖表、振蕩指標(biāo)、型態(tài)與分形）趨勢、反轉(zhuǎn)、周期運(yùn)用技術(shù)分析進(jìn)行交易與投資：關(guān)于概率以及出市入市的價位對技術(shù)分析之批判附錄推薦閱讀資料及學(xué)術(shù)參考3什么是技術(shù)分析？什么是技術(shù)分析？“技術(shù)分析是指分析證券歷史價格從而預(yù)測未來價格走勢的過程?！笔袌鲱A(yù)測只建立在價格、時間、成交量、未平倉

2、合約信息的基礎(chǔ)之上而忽略如盈利、現(xiàn)金流、資產(chǎn)負(fù)債表等基本面數(shù)據(jù)4技術(shù)分析的歷史技術(shù)分析的歷史技術(shù)分析的起源可追溯到幾百年前已知的最古老的技術(shù)分析方法由本間宗久（Munehisa Homma）所發(fā)明，并在18世紀(jì)早期應(yīng)用于大米市場技術(shù)分析在美國的發(fā)展可追溯到查爾士道（Charles H. Dow）從1900年開始在華爾街日報發(fā)表的技術(shù)分析論文5技術(shù)分析的歷史技術(shù)分析的歷史從此以后，更多的技術(shù)分析工具得到了發(fā)展，而且越來越強(qiáng)調(diào)計算機(jī)輔助技術(shù)。6技術(shù)分析技術(shù)分析 VS 基本面經(jīng)濟(jì)基本面經(jīng)濟(jì)/金融分析金融分析基本面分析利用宏觀和微觀的經(jīng)濟(jì)數(shù)據(jù)來預(yù)測國家、企業(yè)、個人的宏觀和微觀結(jié)果。 他們使用基本面數(shù)據(jù)

3、來構(gòu)造未來現(xiàn)金流和投資回報的模型。技術(shù)分析則使用價格、時間、交易活動數(shù)據(jù)，來預(yù)測未來價格范圍和活動的概率。 我們使用技術(shù)數(shù)據(jù)來建立交易和投資策略。7技術(shù)分析的研究對象技術(shù)分析的研究對象價格=任何交易活躍的證券、貨幣或大宗商品的價格歷史時間=年度、月度、每周、每日、日間數(shù)據(jù)交易數(shù)據(jù)=成交量以及未平倉合約8技術(shù)分析的假設(shè)技術(shù)分析的假設(shè)市場價格反映了任何時刻的所有已知信息。市場價格會對額外的信息做出反應(yīng)。市場參與者不斷變化的情緒和活動會留下一連串變化著的價格和市場活動的蹤跡，用技術(shù)分析為之建造模型，可預(yù)測未來的價格范圍和變動。9技術(shù)分析的類型技術(shù)分析的類型（圖表、振蕩指標(biāo)、型態(tài)與分形）（圖表、振蕩指

4、標(biāo)、型態(tài)與分形）重要價位（依重要程度排列）1）現(xiàn)價價=當(dāng)前的交易價2）收市價收市價=某一日（或其他時段）交易活動結(jié)束前最后一筆交易的成交價格3）開市價開市價=某時段開始的價格4）最高價最高價=該時段內(nèi)最高的成交價格；最低價最低價=該時段內(nèi)最低的成交價格10收市價、開市價、最高價、最低價收市價、開市價、最高價、最低價 收市價收市價=一日（或其他時段）交易活動結(jié)束前最后一筆交易的成交價格 開市價開市價=某交易時段開始的價格 最高價最高價=該時段內(nèi)最高的成交價格 最低價最低價=該時段內(nèi)最低的成交價格最高價最高價 = 2.5收市價收市價= 1.8開市價開市價 = 2.2最低價最低價= 1.5在這個交易

5、時段里，市場開市價為在這個交易時段里，市場開市價為2.2，最，最高價為高價為2.5，最低價為，最低價為1.5，最后收于，最后收于1.8。技術(shù)分析師在為下個交易時段制定交易策技術(shù)分析師在為下個交易時段制定交易策略時會考慮這些價格水平。略時會考慮這些價格水平。11圖表的類型圖表的類型線形圖柱狀圖K 線圖OX圖市場概覽 (又譯四度空間)12線形圖線形圖線形圖只使用收盤價的歷史記錄這類型的圖包含了有限但關(guān)鍵的信息就價格歷史而言，長期數(shù)據(jù)在半對數(shù)刻度中有時候會更有用。1992年來上海年來上海A股指數(shù)股指數(shù)算術(shù)標(biāo)度算術(shù)標(biāo)度1992年來上海年來上海A股指數(shù)股指數(shù)半對數(shù)標(biāo)度半對數(shù)標(biāo)度13支撐支撐支撐 = 預(yù)期

6、購買活動集中的價格水平 支撐水平可在一系列技術(shù)指標(biāo)基礎(chǔ)上識別出來 在先前價格最低位或者“突破”的水平上常常能看到支撐現(xiàn)象上海上海A股指數(shù)股指數(shù)-2008年年8月月-2011年年6月月橫向支撐橫向支撐趨勢支撐趨勢支撐14阻力 = 預(yù)期出售活動集中的價格水平 支撐水平可在一系列技術(shù)指標(biāo)基礎(chǔ)上識別出來 在先前價格最高位或者“突破”的水平上常常能看到阻力現(xiàn)象阻力阻力上海上海A股指數(shù)股指數(shù)-2008年年8月月-2011年年6月月橫向阻力橫向阻力趨勢阻力趨勢阻力15柱狀圖柱狀圖柱狀圖包含開市價、最高價、最低價、收市價的信息。柱狀圖展示了每個交易時段的價格范圍和清晰度。最高價最高價 = 2.5收市價收市價

7、= 1.8開市價開市價 = 2.2最低價最低價 = 1.52006年以來年以來S&P500指數(shù)指數(shù)算術(shù)標(biāo)度算術(shù)標(biāo)度16K線線圖圖陰陽燭圖包含開市價、最高價、最低價、收市價的信息。用不同的顏色標(biāo)識上升或下跌的交易日，從而強(qiáng)調(diào)每個市場時段的不同心理。連續(xù)幾個陰陽燭的組合能有效反映變化連續(xù)幾個陰陽燭的組合能有效反映變化著的市場心理著的市場心理17陰陽燭圖陰陽燭圖2011年年2月以來歐元兌美元月以來歐元兌美元錘頭錘頭牛市形態(tài)牛市形態(tài)頂部三星頂部三星熊市形態(tài)熊市形態(tài)前進(jìn)白色三兵前進(jìn)白色三兵牛市形態(tài)牛市形態(tài)18OX圖圖點(diǎn)數(shù)圖忽略時間因素，只記錄交易價格水平的實(shí)質(zhì)變化。點(diǎn)數(shù)圖為技術(shù)分析師提供了忽略時

8、間因素而分析市場行為的另一種途徑。2006年年6月以來黃金走勢月以來黃金走勢點(diǎn)數(shù)圖凸顯支撐和阻力水平點(diǎn)數(shù)圖凸顯支撐和阻力水平19市場市場概覽概覽市場輪廓圖以時間分配的形式記錄成交價格，一個交易時段里不同分段的每個成交價格用不同的字母來表示。20市場市場概覽概覽市場概覽圖對于在證券交易所交易的證券尤為有效，每個成交價格的成交量都可以得到利用。21成交量分析成交量分析成交量是某一特定交易時段或價格水平上成交的證券價值。2008年年6月以來的月以來的Google價格未達(dá)到預(yù)期高位，人們紛紛拋價格未達(dá)到預(yù)期高位，人們紛紛拋售，導(dǎo)致成交量出現(xiàn)峰值售，導(dǎo)致成交量出現(xiàn)峰值價格在價格在$350左右時，買入者涌

9、入市場，導(dǎo)致成交量增左右時，買入者涌入市場，導(dǎo)致成交量增大大22未平倉量分析未平倉量分析未平倉量是指某特定市場在某交易時段結(jié)束時，多方所持有或空方所拋空的契約口數(shù)。2011年年2月以來的可可期貨月以來的可可期貨下跌趨勢加劇，未平倉量增加，從而強(qiáng)下跌趨勢加劇，未平倉量增加，從而強(qiáng)化熊市預(yù)期化熊市預(yù)期市場參與者獲利，未平倉量減少市場參與者獲利，未平倉量減少23趨勢分析趨勢分析市場上存在趨勢。順應(yīng)趨勢而持有的倉位比逆勢而為更有可能獲得盈利。技術(shù)分析師試圖使用幾何和數(shù)學(xué)技術(shù)來識別市場的趨勢及其范圍。2002年年-2009年年S&P 500指數(shù)指數(shù)2008年年8月持續(xù)月持續(xù)已久的上漲趨勢已久的上

10、漲趨勢出現(xiàn)轉(zhuǎn)折，標(biāo)志出現(xiàn)轉(zhuǎn)折，標(biāo)志著大跌的開始著大跌的開始出現(xiàn)上升趨勢前出現(xiàn)上升趨勢前有好幾個買入機(jī)有好幾個買入機(jī)會會24趨勢分析趨勢分析技術(shù)分析師可使用移動平均數(shù)來使數(shù)據(jù)平滑化，從而識別趨勢和趨勢變動。2002年年-2009年年S&P 500指數(shù)指數(shù)2008年年8月長期月長期上升趨勢戛然而上升趨勢戛然而止，慢速快速移止，慢速快速移動平均線的交叉動平均線的交叉預(yù)示著跌勢到來預(yù)示著跌勢到來從從200天移動平均價格天移動平均價格可看出上升趨勢，在上可看出上升趨勢，在上升趨勢出現(xiàn)前有很多買升趨勢出現(xiàn)前有很多買入機(jī)會入機(jī)會25反轉(zhuǎn)分析反轉(zhuǎn)分析每個趨勢結(jié)束時會發(fā)生反轉(zhuǎn)，市場先前的方向動搖不定，趨

11、勢的變化日益明顯。技術(shù)分析師試圖識別常見的反轉(zhuǎn)形態(tài)以及反轉(zhuǎn)市場行為的信號，從而察覺市場方向改變的預(yù)警。牛勢錘子形態(tài)和雙重谷底標(biāo)志著牛勢錘子形態(tài)和雙重谷底標(biāo)志著2000年黃年黃金市場熊勢的反轉(zhuǎn)，當(dāng)時價格為金市場熊勢的反轉(zhuǎn)，當(dāng)時價格為255美元美元2000年年7月至月至2001年年6月的黃金月的黃金26形態(tài)分析形態(tài)分析隨著時間的推移，市場往往會呈現(xiàn)常見的行為形態(tài)。技術(shù)分析師試圖識別常見的價格形態(tài)，從而制定利用這些價格形態(tài)的交易策略。無法突破先前更無法突破先前更高價格高價格熊勢熊勢形態(tài)形態(tài)旗形是旗形是“持續(xù)形態(tài)持續(xù)形態(tài)”，預(yù)示著市場重拾先前趨預(yù)示著市場重拾先前趨勢前會有一段勢前會有一段“整固整固”形

12、態(tài)。形態(tài)。牛勢牛勢“倒置頭肩形態(tài)倒置頭肩形態(tài)”預(yù)示先前熊市的結(jié)束預(yù)示先前熊市的結(jié)束成功成功“突破突破”標(biāo)志著上標(biāo)志著上升趨勢的持續(xù)升趨勢的持續(xù)27分形分析分形分析隨著時間的推移，趨勢通常會呈現(xiàn)出常見的價格形態(tài)和分形行為。技術(shù)分析師試圖識別價格分形，利用某特定趨勢或回調(diào)的演變來制定一些交易系統(tǒng)。28分形分析分形分析1996年至2009年標(biāo)普500的艾略特波浪圖29周期分析周期分析所有市場、多重時間段都會呈現(xiàn)出周期性。技術(shù)分析師試圖識別市場周期，從而制定一些交易系統(tǒng)來利用每個周期以及該周期與其他時間周期之間可能存在的諧波。30振蕩指標(biāo)振蕩指標(biāo)振蕩指標(biāo)把歷史上的價格信息放進(jìn)數(shù)學(xué)模型中，使數(shù)據(jù)平滑化，

13、同時突出動能和市場特征的改變。相對強(qiáng)弱指數(shù)從相對強(qiáng)弱指數(shù)從70%以上跌到以上跌到70%以下以下=熊市信號熊市信號相對強(qiáng)弱指數(shù)從相對強(qiáng)弱指數(shù)從30%以下上升至以下上升至30%以上以上=牛市信號牛市信號從從2010年年7月至月至2011年年6月日元兌美元月日元兌美元31振蕩指標(biāo)振蕩指標(biāo)有很多不同的振蕩指標(biāo)研究（不下一百種）每一種研究都試圖突出價格和市場行為的不同方面有些更適合趨勢市場，有些更適合橫向市場有些振蕩指標(biāo)甚至尋求比較趨勢市場和橫向市場，比如動向指數(shù)（DMI）2010年年7月至月至2011年年7月黃金的動向指數(shù)圖表月黃金的動向指數(shù)圖表動向指數(shù)預(yù)示動向指數(shù)預(yù)示著不斷加強(qiáng)的著不斷加強(qiáng)的上升趨勢

14、上升趨勢動向指數(shù)一開始預(yù)示動向指數(shù)一開始預(yù)示減弱的上升趨勢，而減弱的上升趨勢，而后轉(zhuǎn)為較弱的下降趨后轉(zhuǎn)為較弱的下降趨勢勢動向指數(shù)預(yù)示動向指數(shù)預(yù)示著不斷加強(qiáng)的著不斷加強(qiáng)的上升趨勢上升趨勢不斷減弱的不斷減弱的上升趨勢上升趨勢32利用技術(shù)分析做交易利用技術(shù)分析做交易技術(shù)分析師使用一系列的技巧，從而達(dá)到如下目標(biāo)：判斷當(dāng)前市場行為的特性判斷當(dāng)前市場行為的特性趨勢，趨勢，調(diào)整調(diào)整，抑或反轉(zhuǎn)，抑或反轉(zhuǎn)識別適合持有新的多頭或空頭倉位的關(guān)鍵市場識別適合持有新的多頭或空頭倉位的關(guān)鍵市場價位價位；既呈現(xiàn)出良好的成功概率，又有有效平衡盈利潛力與止損的風(fēng)險回報比率對所有交易倉位進(jìn)行風(fēng)險管理風(fēng)險管理，以后市價格變動為借鑒

15、，根據(jù)變化中的市場條件調(diào)整持續(xù)的技術(shù)策略33利用技術(shù)分析做交易利用技術(shù)分析做交易技術(shù)分析師交易原則技術(shù)分析師交易原則 #1 趨勢是你的朋友趨勢是你的朋友 2010年年2月在月在1050美元美元的價位上購買黃金的價位上購買黃金判斷當(dāng)前市場行為的特性判斷當(dāng)前市場行為的特性市場上升趨勢明顯，有賣空跡象。識別重要的市場水平識別重要的市場水平購進(jìn)多頭倉位的時機(jī)應(yīng)該是：上升趨勢低谷附近1040美元，以及向上傾斜的移動平均線1024美元，因?yàn)殡S后市價將恢復(fù)到原來的上升趨勢，從而帶來大筆盈利。風(fēng)險管理風(fēng)險管理 下止損單的位置應(yīng)該比趨勢線和移動平均線稍低1010美元，若升勢結(jié)束會有小額損失。34利用技術(shù)分析做交

16、易利用技術(shù)分析做交易技術(shù)分析師交易原則技術(shù)分析師交易原則 #1 趨勢是你的朋友趨勢是你的朋友 2010年年2月在月在1050美元的價位上美元的價位上購買黃金購買黃金潛在盈利潛在盈利 接下來的高價分別是1127美元、1161美元、1225美元 若在1050美元價位上購入，則潛在盈利分別是77美元、111美元、175美元潛在損失潛在損失 若在1010美元價位上實(shí)現(xiàn)止損，則損失40美元風(fēng)險回報比率風(fēng)險回報比率 77/40 （近 2:1）1127 111/40 （近 2.8 : 1） 1161 175/40 （近 4.4 : 1）1225不同概率帶來的結(jié)果不同概率帶來的結(jié)果 如果就第一個價格目標(biāo)而言，

17、只有50%實(shí)現(xiàn)了上述交易，那么每次交易平均盈利仍為37美元通過設(shè)置特定的入市和離市的價位，技術(shù)分析師就能利用這些概率并從中獲益。通過設(shè)置特定的入市和離市的價位，技術(shù)分析師就能利用這些概率并從中獲益。35利用技術(shù)分析做交易利用技術(shù)分析做交易技術(shù)分析師交易原則 #1 趨勢是你的朋友 2010年2月在1050美元的價位上購買黃金2010年年2月月 $1050后來也出現(xiàn)大量類似的風(fēng)險后來也出現(xiàn)大量類似的風(fēng)險/回報技術(shù)性購入回報技術(shù)性購入信號，頗具吸引力信號，頗具吸引力36技術(shù)分析的限制技術(shù)分析的限制依賴準(zhǔn)確及時的市場數(shù)據(jù)在交易活躍的市場最為有效有些技巧較為主觀 視覺技巧和數(shù)學(xué)技巧都會應(yīng)用到技術(shù)分析不是

18、“水晶球” 技術(shù)分析師無法預(yù)知未來。他們只是嘗試在過去的價格和市場歷史基礎(chǔ)上，估計未來發(fā)展的概率，從而判別入市離市的明智時機(jī)。謝謝謝謝河源外匯論壇友情提供附錄附錄1：推薦書目：推薦書目Murphy, John J. - Technical Analysis of the Financial Markets - New York Institute of Finance/Prentice Hall 1999du Plessis, Jeremy - The Definitive Guide to Point and Figure - Harriman House Ltd.Plummer, Tony

19、 - Forecasting Financial Markets - Kogan Page 1998Prechter, Robert - R N Elliotts Masterworks, the Definitive Collection - New Classic Library, a division of Elliott Wave International, Gainesville GA, USA 1994 Lambert, Clive - Candlestick Charts - Harriman House Ltd.Linton, David - Cloud Charts, Tr

20、ading Success with the Ichimoku Technique - Updata, PlcElliott, Nicole - Ichimoku Charts: An Introduction to Ichimoku Kinko Clouds - Harriman House Ltd.Sperandeo, Victor - Trader Vic: Methods of a Wall Street Master - John Wiley & Sons Inc 1993Schwager, Jack D. - Market Wizards - New York Instit

21、ute of Finance/Simon & Schuster 1989Schwager, Jack D. - The New Market Wizards - Harper-Collins 1992附錄附錄2：歷史上的學(xué)術(shù)論文：歷史上的學(xué)術(shù)論文1925 SCHULTZ, Henry, Forecasting Security Prices, Journal of the American Statistical Association, Volume 20, Issue 150 (Jun., 1925), 244-249. 1932 KING, Willford I., Foreca

22、sting Methods Successfully Used Since 1928, Journal of the American Statistical Association, Volume 27, Issue 179 (Sep., 1932), 315-319. 1933 COWLES, Alfred 3rd, Can Stock Market Forecasters Forecast?, Econometrica, Volume 1, Issue 3 (Jul., 1933), 309-324. Cowles (1933) found that stock market forec

23、asters cannot forecast.1934 KING, Willford I., Technical Methods of Forecasting Stock Prices, Journal of the American Statistical Association, Volume 29, Issue 187 (Sep., 1934), 323-325. 1952 GORDON, R. A., Wesley Mitchell and the Study of Business Cycles, Journal of Business of the University of Ch

24、icago, Volume 25, Issue 2 (Apr., 1952), 101-107. 1955 ROOS, Charles F., Survey of Economic Forecasting Techniques: A Survey Article, Econometrica, Volume 23, Issue 4 (Oct., 1955), 363-395. 1959 ROBERTS, Harry V., Stock-Market Patterns and Financial Analysis: Methodological Suggestions, Journal of Fi

25、nance, Volume 14, Issue 1 (Mar., 1959), 1-10. Roberts (1959) suggests that, There is every reason to believe, however, that this methos of looking at the tape will facilitate all that takes place afterward.“1963 WEINTRAUB, Robert E., On Speculative Prices and Random Walks A Denial Journal of Finance

26、, Volume 18, Issue 1 (Mar., 1963), 59-66. 1963 ZARNOWITZ, Victor, On the Dating of Business Cycles, Journal of Business, Volume 36, Issue 2 (Apr., 1963), 179-199. 1964 ALEXANDER, Sidney S., Price Movements in Speculative Markets: Trends or Random Walks, 1964. Alexander (1964) concludes with, The fin

27、dings surveyed in this paper can be summarized by the statement that in speculative markets price changes appear to follow a random walk over time, but a move, once initiated, tends to persist. In particular, if the stock markets has moved up x per cent it is likely to move up more than x per cent f

28、urther before it moves down by x per cent.“1965 ATKINSON, Sue N., Financial Flows in Recent Business Cycles, Journal of Finance, Volume 20, Issue 1 (Mar., 1965), 14-35. 1965 FAMA, Eugene F., The Behavior of Stock-Market Prices, Journal of Business, Volume 38, Issue 1 (Jan., 1965), 34-105. Fama (1965

29、) concludes that, chart reading, though perhaps an interesting pastime, is of no real value to the stock market investor.“1966 FAMA, Eugene F. and Marshall E. BLUME, Filter Rules and Stock-Market Trading, Journal of Business, Volume 39, Issue 1, Part2: Supplement on Security Pricing (Jan., 1966), 22

30、6-241. 1967 LEVY, Robert A., Relative Strength as a Criterion for Investment Selection, Journal of Finance, Volume 22, Issue 4 (Dec., 1967), 595-610. Levy (1967) concludes with, Although it appears that superior profits can be achieved by investing in securities which historically have been relative

31、ly strong in price movement, the random walk hypothesis is not thereby refuted. To the extent that the superior profits are attributable to the incurrence of extraordinary risk.“1968 HOWREY, E. Philip, A Spectrum Analysis of the Long-Swing Hypothesis, International Economic Review, Volume 9, Issue 2

32、 (Jun., 1968), 228-252. 1968JAMES, F. E., Jr., Monthly Moving Averages-An Effective Investment Tool?, Journal of Financial and Quantitative Analysis, Volume 3, Issue 3, Special Issue: Random Walk Hypothesis (Sep., 1968), 315-326. 1969 MELNIK, Arie and Alan KRAUS, Short-Run Interest Rate Cycles in th

33、e U.S.: 1954-1967, Journal of Financial Quantitative Analysis, Volume 4, Issue 3 (Sep., 1969), 291-299. 附錄附錄3：近期的學(xué)術(shù)論文：近期的學(xué)術(shù)論文ADLAND, R. and S. STRANDENES, 2006. Market efficiency in the bulk freight market revisited. Maritime Policy & Management.AFOLABI, M.O. and O. OLUDE, 2007. Predicting Stock

34、 Prices Using a Hybrid Kohonen Self Organizing Map (SOM). Proceedings of the Fortieth Annual Hawaii International ATMEH, M.A. and I.M. DOBBS, 2006. Technical analysis and the stochastic properties of the Jordanian stock market index return. Studies in Economics and Finance.BABA, N., 2006. Utilizatio

35、n of NNs for Improving the Traditional Technical Analysis in the Financial Markets. LECTURE NOTES IN COMPUTER SCIENCE. BAKER, A., 2006. American power and the dollar: The constraints of technical authority and declaratory policy in the . New Political Economy. BASK, M. and J. FIDRMUC, 2006. Fundamen

36、tals and Technical Trading: Behavior of Exchange Rates in the CEECs. Bank of Finland discussion paper. BEHNCKE, H., 2006. Spectral analysis of fourth order differential operators II. Math. Nachr. BELFORD, P., 2006. Candlestick stock analysis with genetic algorithms. Proceedings of the 8th annual con

37、ference.CASSENS, J. and A. KOFOD-PETERSEN, 2006. Using Activity Theory to Model Context Awareness: a Qualitative Case Study. Proceedings of the 19th International Florida ArtificialCHANG, Y.H., M. METGHALCHI and C.C. CHAN, 2006. Technical trading strategies and cross-national information linkage: th

38、e case of Taiwan stock market. Applied Financial Economics.CHEN, A. and P. BICKEL, 2006. Efficient independent component analysis. Annals of Statistics.CHEUNG, Y.W., 2006. An Empirical Model of Daily Highs and Lows. CHIARELLA, C., X.Z. HE and C. HOMMES, 2006. A dynamic analysis of moving average rul

39、es star, open. Journal of Economic Dynamics and Control.COIERA, E., 2006. Putting the technical back into socio-technical systems research. Int J Med Inform.CREAMER, G. and Y. FREUND, 2006. Automated Trading with Boosting and Expert Weighting. DALE, R.T. and W.E. TYNER, 2006. ECONOMIC AND TECHNICAL

40、ANALYSIS OF ETHANOL DRY MILLING: MODEL DESCRIPTION. Staff Paper (06-04), Dept. of Ag. Econ., Purdue University.DE, D., et al., 2006. Genetic algorithms to optimise the time to make stock market investment. Proceedings of the 8th annual conference on Genetic and DE, P. and M. GRIMALDI, 2006. Exchange

41、 rate puzzles: A tale of switching attractors star, open. European Economic Review.DUEKER, M. and C.J. NEELY, 2007. Can Markov switching models predict excess foreign exchange returns?. Journal of banking and finance.DUTTA, G., et al., 2006. Artificial Neural Network Models for Forecasting Stock Pri

42、ce Index in the Bombay Stock Exchange. Journal of Emerging Market Finance. GARCIA-ALMANZA, A.L. and E.P.K. TSANG, 2006. The Repository Method for Chance Discovery in Financial Forecasting. GARCIA-ALMANZA, A.L. and E.P.K. TSANG, 2006. Forecasting stock prices using Genetic Programming and Chance Disc

43、overy. 12th International Conference On Computing In Economics And.GILBERT, T., 2006. Mixed methods and mixed methodologies: The practical, the technical and the political. Journal of Research in Nursing. INCE, H., 2007. Short term forecasting with support vector machines and application to stock pr

44、ice prediction. International Journal of General Systems.INCE, H. and T.B. TRAFALIS, 2007. Kernel principal component analysis and support vector machines for stock price prediction. IIE Transactions.KOZHAN, R. and M. SALMON, 2006. On the Predictability of Tick by Tick Exchange Rates Using the Struc

45、ture of the Order Book. LIEN, K., 2006. Day Trading the Currency Market: Technical and Fundamental Strategies to Profit from Market Swings. LIU, J.N.K. and R.W.M. KWONG, 2007. Automatic extraction and identification of chart patterns towards financial forecast. Applied Soft Computing. MANZAN, S. and

46、 F. WESTERHOFF, 2006. Heterogeneous Expectations, Exchange Rate Dynamics and Predictability. J Econ Behav Organ (in press). MENKHOFF, L. and M.P. TAYLOR, 2007. The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis. Journal of Economic Literature, forthcoming.MORRIS, G.L., 2006.

47、 Candlestick Charting Explained. . 附錄附錄3：近期的學(xué)術(shù)論文：近期的學(xué)術(shù)論文NEELY, C.J., et al., 2006. The Adaptive Markets Hypothesis Evidence from the Foreign Exchange Market. . PAN, H., D. SORNETTE and K. KORTANEK, 2006. Intelligent finance?an emerging direction. Quantitative Finance. PAPADAMOU, S. and G. STEPHANIDE

48、S, 2007. Improving Technical Trading Systems by Using a New MATLAB based Genetic Algorithm Procedure. Mathematical and Computer Modelling. PETRONI, N.L., et al., 2006. FATKit: A Framework for the Extraction and Analysis of Digital Forensic Data from Volatile System . Digital Investigation. PREDEN, J

49、. and J. HELANDER, 2006. Auto-adaptation Driven by Observed Context Histories. 2 ndInternational Workshop on Exploiting Context Histories .QI, M. and Y. WU, 2006. Technical Trading-Rule Profitability, Data Snooping, and Reality Check: Evidence from the Foreign . Journal of Money, Credit, and Banking

50、. REITZ, S., 2006. On the predictive content of technical analysis. The North American Journal of Economics and Finance. REITZ, S. and M.P. TAYLOR, 2006. The coordination channel of foreign exchange intervention: a nonlinear microstructural analysis. REITZ, S., F. WESTERHOFF and C. WIELAND, 2006. Ta

51、rget Zone Interventions and Coordination of Expectations. Journal of Optimization Theory and Applications. SACK, W., et al., 2006. A Methodological Framework for Socio-Cognitive Analyses of Collaborative Design of Open Source . Computer Supported Cooperative Work (CSCW). SAREWITZ, D., 2006. Liberati

52、ng science from politics. American Scientist.SAVIN, G., P. WELLER and J. ZVINGELIS, 2007. The Predictive Power of Head-and-Shoulders Price Patterns in the US Stock Market. Journal of Financial Econometrics. SCHOREELS, C. and J.M. GARIBALDI, 2006. Comparative study of central decision makers versus g

53、roups of evolved agents trading in equity . Proceedings of the 12th International Conference on SCHULMEISTER, S., 2006. The interaction between technical currency trading and exchange rate fluctuations star, open. Finance Research Letters. SPEER, S.A., 2007. On Recruiting Conversation Analysis for C

54、ritical Realist Purposes. Theory & Psychology. STEIN, J.L., 2007. United States Current Account Deficits: A Stochastic Optimal Control Analysis. Journal of banking and finance. STROZZI, F. and J.M.Z. COMENGES, 2006. Towards a non-linear trading strategy for financial time series. Chaos, Solitons

55、 and Fractals. TANAKA-YAMAWAKI, M. and S. TOKUOKA, 2007. Adaptive use of technical indicators for the prediction of intra-day stock prices. Physica A: Statistical Mechanics and its Applications. THUNIS, P., et al., 2007. Analysis of model responses to emission-reduction scenarios within the CityDelt

56、a project. Atmospheric Environment.TILAKARATNE, C.D., et al., 2006. Quantification of intermarket influence on the Australian All Ordinary Index based on optimization . Proceedings of the 13th Biennial Computational Techniques ZIVOT, E., J. WANG and I. NETLIBRARY, 2006. Modeling Financial Time Serie

57、s with S-plus. . 42Contact InformationCQS (Switzerland) S.A.3 Quai du Mont-BlancCase Postale 13931211 Geneva 1SwitzerlandTel: +41 22 596 1800Fax: +41 22 596 1801CQS (UK) LLPCQS Investment Management LimitedCQS Asset Management Limited33 Grosvenor PlaceLondon SW1X 7HYEnglandTel: +44 0 20 7201 6900Fax

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