本論文研究目標為：1. 描述台灣股票市場中訂單簿（order book）的若干特徵。2. 分析訂單流 (order flow) 與訂單簿間交互作用的均衡關係。 3. 探討流動性消耗者與流動性提供者如何進出市場而維持市場機能。 本研究資料來自台灣證券交易所。台灣股市的市場結構迥異於世界其他大部分的市場，採取自動化、間斷時間 (auto-electronic, periodic call) 的撮和方式：單子全部集合在交易所的電腦系統中，依照價格優先、時間優先的原則，每隔45至60秒批次執行撮和。Handa及Schwartz (1996年) 指出，這種市場結構和其他連續撮和的市場有著根本上的不同，尤其是訂單流的匯總方式與市場結清價的形成過程，但目前較少有研究提及。 在過去的文獻中，1995年Biais、Hillion及Spatt以巴黎股市中CAC 40指數的成分股為樣本，首開訂單流與訂單簿間交互作用的研究。他們直接觀察並描繪訂單在各價位的分佈情形，發現當買賣價差 (bid-ask spread) 比較大或訂單簿比較薄（亦即市場流動性較差）時，接下來會有比較多的限價單(limit orders)進場提供流動性；相反地，當spread比較小的時候，接下來會有比較多的市價單(market orders)進場消耗流動性。雖然他們有注意到買賣單、限價單、市價單對價格推升或壓低的作用，但對於引發這些變化的因素卻沒有進一步的闡釋。 1998年，Handa、Schwartz及Tiwari清楚地指出，短暫價格波動 (short-term volatility) 在促進市場達到流動性均衡方面扮演關鍵的角色。由於有基於流動性動機而進場的投資人，此時市價單與限價單成交所造成的短暫價格波動正好補償限價單交易者所面臨的資訊不對稱風險，吸引限價單進場並提供流動性；而有立即性(immediacy)需求的投資人就會下市價單而消耗流動性。1999年，Foucault把Handa等人的推論發展為賽局模型，強化下單決策與價格形成的理論，並建議以訂單流的組成成分進行實證。 這些理論在2000年Ahn、Bae和Chan發表的研究中獲得實證的支持。該文以市場深度差作為市價單限價單組成成分的代理變數，首先驗證短暫價格波動的確是使市場達流動性均衡的重要因素：當價格向上波動，將吸引限價單流入市場提供流動性；而流動性的增加將減緩價格的波動。並進一步分析價格形成過程，發現若價格波動由賣方引發，則限價賣單為流動性提供者；若價格波動來自買方，則限價買單為流動性提供者。 本研究不同於前述研究之處，其一在於台灣股票市場結構的不同。因為所有的單子，不論是新進入或殘留的、不論是買還是賣，全部都集合在電腦系統中等待撮和，因此限價單不見得是流動性提供者，市價單也不見得是流動性消耗者。其二在於直接觀察訂單分佈情形，比Biais等人更深入研究訂單變化、比Ahn等人更清楚地分析變化的過程。 本論文將市場中的單子區分為新委託單(new orders)、殘留單(stale orders)及成交單(executed orders)三大類，取得每個撮和時點前、後買賣雙方在各價位的分佈和變化情形。結果發現，大部分的新委託單並沒有立即成交（約40%沒有立即成交）；成交單中殘留單與新委託單成交的比例在任何時間區間都遠高於新委託單互相成交的比例。也就是說，殘留單對市場流動性的均衡扮演關鍵的角色。

Numerous studies have documented the existence of nonlinearity within various financial time series. But how important of a finding is this? This dissertation examines this issue from a number of perspectives. First, is the nonlinearity that has been found a statistical anomaly that is isolated to a few of the more widely known financial time series or is nonlinearity a statistical regularity inherent in such series? Second, even if nonlinearity is pervasive, does this finding have any practical relevance for finance practitioners or academics? Using the relatively financially isolated but nonetheless well-traded Taiwan Stock Exchange as a case study, it is found that virtually all of the stocks trading on this exchange exhibit nonlinearity. The pervasiveness of nonlinearity within this market, combined with earlier results from other markets, suggests that nonlinearity is an inherent aspect of financial time series. Furthermore, closer examination of the time-paths of various measures of this nonlinearity via both windowed testing and recursive testing and parameter estimation reveals an additional complication, the possibility of nonstationarity. The serial dependency structures, especially for the nonlinear dependencies, do not appear to be constant, but instead appear to exhibit a number of brief episodes of extremely strong dependencies, followed by longer stretches of relatively quiet behavior. On average, though, these nonlinearities appear with sufficient strength to be significant for the full sample. Continuing on to examine the relevance of such nonlinearities for empirical work in finance, a variety of conditionally heteroskedastic models were fit to the returns for a subsample Taiwanese stocks, the Taiwanese stock index, and stock indices for other stock markets, including New York, London, Tokyo, Hong Kong, and Singapore. In a majority of cases, such models appear to be successful at filtering out the extant nonlinearity from these series of returns; however, a variety of indicators suggest that these models are not statistically well-specified for these returns, calling into question the inferences obtained from these models. Furthermore, a comparison of the various conditionally heteroskedastic models with each other and with a dynamic linear regression model reveals that, for many of the data series, the inferences obtained from these models regarding the day-of-the-week effect and the extant autocorrelation within the data varied from model to model. This finding suggests the importance of adequately accounting for nonlinear serial dependencies (and of ensuring data stationarity) when studying financial time series, even when other empirical aspects of the data are the focus of attention. / Ph. D.

In this thesis, we propose a model which applies the genetic programming (GP) to train the profitable and stable trading strategy in the training period, and then the strategy is applied to trade stocks in the testing period. The variables for GP in our models include 6 basic information and 25 technical indicators. We perform our models on Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) from 2000/9/14 to 2010/5/21, approximately ten years. We conduct five experiments. In these experiments, we find that the trading strategies generated by GP with two arithmetic trees have more stable returns. In addition, if we obtain the trading strategies in three historical periods which are the most similar to the current training period, we earn higher return in the testing periods. In each experiment, 24 cases are considered, with training periods of 90, 180, 270, 365, 455, 545, 635 and 730 days, and testing periods of 90, 180 and 365 days, respectively. The testing period is rolling updated until the end of the experiment period. The best cumulative return 165.30\% occurs when 730-day training period pairs with 365-day testing period, which is much higher than the return of the buy-and-hold strategy 1.19\%.

In the thesis, we apply the gene expression programming (GEP) to training profitable trading strategies. We propose a model which utilizes several historical periods that are highly related to the current template period, and the best trading strategies of the historical periods generate the trading signals. To keep stability of our model, we proposed the trading decision mechanism based on simple majority vote in our model. The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) is selected as our investment target and the trading period starts from 2000/9/14 to 2012/1/17, approximately twelve years. In our experiments, the lengths of our training period are 60, 90, 120, 180, and 270 trading days, respectively. We observe that the model with higher voting threshold usually can make profitable trading decisions. The best cumulative return 236.25\% and the best annualized cumulative return 10.63\% occur when the 180-day training models pairs with available threshold 0.21 and voting threshold 0.88, which are higher than the cumulative return 0.96\% and annualized cumulative return 0.08\% of the buy-and-hold strategy.

Taiwan Security Market is a typical order-driven market. The electronic trading system of Taiwan Security Market launched in 1998 significantly reduces the trade matching time (the current matching time is around 20 seconds) and promptly provides updated online trading information to traders. In this study, we establish an online transaction simulation system which can be applied to predict trade prices and study market efficiency. Models are established for the times and volumes of the newly added bid/ask orders on the match list. Exponentially weighted moving average (EWMA) method is adopted to update the model parameters. Match prices are predicted dynamically based on the EWMA updated models. Further, high frequency bid/ask order data are used to find the supply and demand curves as well as the equilibrium prices. Differences between the transaction prices and the equilibrium prices are used to investigate the efficiency of Taiwan Security Market. Finally, EWMA and cusum control charts are used to monitor the market efficiency. In empirical study, we analyze the intra-daily (April, 2005) high frequency match data of Uni-president Enterprises Corporation and Formosa Plastics Corporation.

國際證券管理組織（The International Organization of Securities Commissions, IOSCO）認為自律機構（Self-Regulatory Organization, SRO）是管理證券市場之必要條件之一。我國證券交易法第四、五章對證券商同業公會與證券交易所皆設有專章特別規範，亦可看出自律機構在證券市場的重要性。惟目前臺灣證券交易所、財團法人中華民國證券櫃檯買賣中心及中華民國證券商業同業公會等協助管理證券市場之機構，在法律規範面及執行面皆存有許多待改進之處。本文以證券自律機構對證券商及其人員之管理規範為探討重點，試著從比較立法例說明美國、英國及日本等國家證券市場自律監理制度演變過程及法制規範，並分析探討前述臺灣證券交易所等機構所存在之問題，期能藉由制度比較，整理歸納提出我國證券自律機構在法制架構及實務運作上可供改進之具體建議。

台灣發行量加權股價指數在編製時並未調整現金股利的影響，不僅會低估實際的投資報酬率，以其作為標竿指標，在評估共同基金績效時，亦會產生標竿錯誤的問題。因此，本文將現金股利的再投資報酬納入，重新編製加權股價指數。實證結果發現，若自民國75年起調整現金股利之影響，則在民國89年10月31日時，股價指數由5544.18點調整為6419.83點，約增加1.16倍。以新指數重新衡量基金績效的結果，發現績效排名並無大幅度的改變，而且基金績效是否擊敗大盤的情形，受新指標的影響亦不大，此乃因近年來上市公司配息少，而且基金績效非常極端。但基於理論上的正確性，在計算投資報酬率及評估共同基金績效時，仍應以納入現金股利之加權股價指數為基礎，以降低因標竿指標錯誤所造成研究結果的偏誤，否則未來我國股票配息的情況及基金報酬率的特性若改變之後，以過去的方式評估績效將可能造成極大之偏差。 / The Taiwan Stock Exchange Market Weighted Index (TAIEX) is not adjusted for cash dividend. Since the TAIEX is commonly used for calculating the investment return of the Taiwan’s market and as the benchmark index for mutual fund performance evaluation, the investment return in Taiwan is underestimated and there is benchmark error in the evaluation of mutual fund performance. This paper adjusts the TAIEX by incorporating the effect of the reinvestment of cash dividend in the TAIEX. The beginning date of our adjustment is January 4, 1986. Since then until the end of October 2000, the adjusted TAIEX grew to 1.16 times of the unadjusted index. However, The mutual fund performance evaluated based on the adjusted index is insignificantly different from that based on the un-adjusted index. This is because mutual funds have extreme performance. Due to the small cash dividend paid out by the listed firms on the Taiwan Stock Exchange, the adjustment effect is not enough to overturn the evaluation of

本研究考慮重要且對台股大盤指數走勢有連動影響的因素，主要納入對台股有領頭作用的美國三大股市，那斯達克(NASDAQ)指數、道瓊工業(Dow Jones)指數、標準普爾500(S&P500)指數;其他對台股緊密連動效果的國際股票市場，香港恆生指數、上海證券綜合指數、深圳證券綜合指數、日經225指數;以及納入左右國際經濟表現的國際原油價格走勢，美國西德州原油、中東杜拜原油和歐洲北海布蘭特原油;在宏觀經濟因素方面則考量失業率、消費者物價指數、匯率、無風險利率、美國製造業重要指標的存貨/銷貨比率、影響貨幣數量甚鉅的M1B;在技術分析方面則納入多種重要的指標，心理線 (PSY) 指標、相對強弱(RSI) 指標、威廉(WMS%R) 指標、未成熟隨機(RSV) 指標、K-D隨機指標、移動平均線(MA)、乖離率(BIAS)、包寧傑%b和包寧傑帶狀寬度(BandWidth%);所有考量因素共計35項，因為納入重要因子比較多，所以完備性較高。 本研究先採用的贏者全拿(Winner-Take-All) 競爭學習策略的自組織映射網路(Self-Organizing Feature Maps, SOM)，藉由將相似資料歸屬到已身的神經元萃取出關聯分類且以計算距離來衡量神經元的離散特徵，對於探索大量且高維度的非線性複雜特徵俱有優良的因素相依性投射效果，將有利於提高預測模式精準度。在線性擬合部分則結合倒傳遞(Back-Propagation, BP)、Elman反饋式和徑向基底函數類網路(Radial-Basis-Function Network, RBF)模式為指數預測輸出，並對台股加權指數隔日收盤指數進行預測和評量。而在傳統的Elman反饋式網路只在隱藏層存在反饋機制，本研究則在輸入層和隱藏層皆建立反饋機制，將儲存在輸入層和隱藏層的過去時間資訊回饋給網路未來參考。在徑向基底函數網路方面，一般選取中心聚類點採用隨機選取方式，若能有效降低中心點個數，可降低網路複雜度，本研究導入垂直最小平方法以求取誤差最小的方式強化非監督式學習選取中心點的能力，以達到網路快速收斂，提昇網路學習品質。 研究資料為台股指數交易收盤價，日期自2001/1/2，至2011/10/31共2676筆資料。訓練資料自2001/1/2至2009/12/31，共2223筆;實證測試資料自2010/1/4至2011/10/31，計453個日數。主要評估指標採用平均相對誤差(AMRE)和平均絕對誤差 (AAE)。在考慮因子較多的狀況下，實證結果顯示，在先透過SOM進行因子聚類分析之後，預測因子被分成四個組別，分別再透過BP、Elman recurrent和RBF方法進行線性擬合，平均表現方面，以RBF模式下的四個群組因子表現最佳，其中RBF模式之下的群組4，其AMRE可達到0.63%，最差的AMRE則是群組1，約為1.05%;而Elman recurrent模式下的四組群組因子之ARME則介於1.01%和1.47%之間;其中預測效果表現最差則是BP模式的預測結果。顯示RBF具有絕佳的股價預測能力。最後，在未來研究建議可以運用本文獻所探討之其他數種類神經網路模式進行股價預測。 / In this study, we considering the impact factors for TWSE index tendency, mainly aimed at the three major American stock markets, NASDAQ index, Dow Jones index, S&P 500, which leading the Taiwan stock market trend; the other international stock markets, such as the Hong Kong Hang-Seng Index, Shanghai Stock Exchange Composite Index, Shenzhen Stock Exchange Composite Index, NIKKEI 225 index, which have close relationship with Taiwan stock market; we also adopt the international oil price trend, such as the West Texas Intermediate Crude Oil in American, the Dubai crude oil in Middle Eastern, North Sea Brent crude oil in European, which affects international economic performance widely; On the side of macroeconomic factors, we considering the Unemployed rate, Consumer Price Index, exchange rate, riskless rate, the Inventory to Sales ratio which it is important index of American manufacturing industry, and the M1b factor which did greatly affect to currency amounts; In the part of Technical Analysis index, we adopt several important indices, such as the Psychology Line Index (PSY), Relative Strength Index (RSI), the Wechsler Memory Scale—Revised Index (WMS%R), Row Stochastic Value Index (RSV), K-D Stochastics Index, Moving Average Line (MA), BIAS, Bollinger %b (%b), Bollinger Band Width (Band Width%);All factors total of 35 which we have considered the important factor is numerous, so the integrity is high. In this study, at first we adopt the Self-Organizing Feature Maps Network which based on the Winner-Take-All competition learning strategy, Similar information by the attribution to the body of the neuron has been extracted related categories and to calculate the distance to measure the discrete characteristics of neurons, it has excellent projection effect by exploring large and complex high-dimensional non-linear characteristics for all the dependency factors , would help to improve the accuracy of prediction models, would be able to help to improve the accuracy of prediction models. The part of the curve fitting combine with the back-propagation (Back-Propagation, BP), Elman recurrent model and radial basis function network (Radial-Basis-Function Network, RBF) model for the index prediction outputs, forecast and assessment the next close price of Taiwan stocks weighted index. In the traditional Elman recurrent network exists only one feedback mechanism in the hidden layer, in this study in the input and hidden layer feedback mechanisms are established, the previous information will be stored in the input and hidden layer and will be back to the network for future reference. In the radial basis function network, the general method is to selecting cluster center points by random selection, if we have the effectively way to reduce the number of the center points, which can reduces network complexity, in this study introduce the Orthogonal Least Squares method in order to obtain the smallest way to strengthen unsupervised learning center points selecting ability, in order to achieve convergence of the network fast, and improve network learning quality. Research data for the Trading close price of Taiwan Stock Index, the date since January 2, 2001 until September 30, 2011, total data number of 2656. since January 2, 2001 to December 31, 2009 a total number of 2223 trading close price as training data; empirical testing data, from January 4, 2010 to September 30, 2011, a total number of 433. The primary evaluation criteria adopt the Average Mean Relative Error (AMRE) and the Average Absolute Error (AAE). In the condition for consider more factors, the empirical results show that, by first through SOM for factor clustering analysis, the prediction factors were divided into four categories and then through BP, Elman recurrent and RBF methods for curve fitting, at the average performance , the four group factors of the RBF models get the best performance, the group 4 of the RBF model, the AMRE can reach 0.63%, the worst AMRE is group 1, about 1.05%; and the four groups of Elman recurrent model of ARME is between 1.01% and 1.47%; the worst prediction model is BP method. RBF has shown excellent predictive ability for stocks index. Finally, the proposal can be used in future studies of the literatures that we have explore several other methods of neural network model for stock trend forecasting.