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題名：台灣證券市場交易方向之推導與資訊含量
作者：詹場 引用關係
作者(外文)：Chan, Chang
校院名稱：國立臺灣大學
系所名稱：財務金融學研究所
指導教授：胡星陽
學位類別：博士
出版日期：2000
主題關鍵詞：交易方向；日內資料；台灣證券市場；資訊含量；市場個體結構；揭示法則；買價；賣價；trade direction；intra-day data；Taiwan Stock Market；information content；microstructure；disclosure rules；bid；ask
原始連結：連回原系統網址new window
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交易方向在市場個體結構(Market Microstructure)理論中扮演著關鍵角色，而文獻上所運用的交易方向法則多以報價導向(Quote Driven)市場之交易機制為依歸，基於分盤競價(Call Auction)市場與報價導向市場的交易機制存在明顯的差異，因此本研究的主要目的是尋求更適於分盤競價市場交易方向的決定法則。
本論文用前一盤成交價作為買進委託與賣出委託的分類基礎，以當盤的買賣淨委託量定義分盤競價市場的交易方向，並據此定義推導出決定分盤競價市場交易方向的決定通則。為了增加本文所推導的交易方向決定法則的實用性以及適用於台灣證券市場，因此進一步衍伸推導無委託量資料以及揭示買價及揭示賣價資料不完整下的交易方向判定法則。
為了驗證本文所推導的交易方向法則，是否比價格升降法則(Tick Test Rule)與Lee and Ready(1991)法則更適合於分盤競價市場交易方向之決定，我們以採用分盤競價的台灣證券集中市場為實證對象。樣本期間是86年6月1日至86年8月31日，包括74個交易日，共含387支股票，計有4,482,990盤的交易資料。以VAR模型與一般迴歸模型進行實證。結果顯示依據本文所推導的交易方向法則所決定的交易方向變數與帶正負號的交易量(Signed Volume)，對報酬與價格變動檔數的預測力最強，證實本文所推導的交易方向則比價格升降法則與Lee and Ready法則等兩種常用的交易方向決定法則更適用於分盤競價市場。
此外，本文亦發現若直接運用台灣證交所之日內交易資料，將有22.5%的成交價是多餘的；45%的成交量資料是錯誤的，從而會導致：低估報酬絕對值20.8％，低估平均每盤成交量21.6％，低估波動性11%，低估流動性565%，高估效率性14.9%，高估交易頻率21.9%，低估交易量的一階自我相關91.5%。防止上述錯誤的作法是將交易時間相同的交易資料合併。
Trade direction plays an important role in the microstructure literature. There are several rules used in the literature to infer the trade direction by identifying which side initiates a trade. But these rules are tailored to a order driven market, and cannot be applied to a call auction market. In a call auction market, buy and sell orders accumulate over a period and, when orders are cleared, everyone pays or gets the same price regardless of their quotes.
In a call auction market, we propose to define the trade direction based on the sign of the net order arrived between two auctions, that is the relative size of the buy and sell orders. Based on our definition, we develop the general rules to infer the trade direction for call auction market. In addition, We also derive the rules to infer the trading direction when the order size or the best price is unobservable。We apply our rules, Tick test rules and the rules of Lee and Ready (1991) to the transaction data of the Taiwan Stock Exchange (TSE) and find that information content of the trade direction defined by our rules is more than that of the other two rules.
We also find that The TSE often separates one transaction into two records. Without taking into account this practice, researchers will get biased results. To provide evidences of the argument, We use the intraday data of TSE during the period from June to August in 1997. Out of 5,780,995 records, 1,298,005(22.5%) occurred at the same time with the record reported right before them. Therefore, both records actually come from the same transaction. So, if one does not merge these records, then 45% of volume data and 22.5% of price data are incorrect.
To use these unconsolidated data will cause several errors: The absolute rate of return is understated by 20.8%. The standard deviation of rate of return is understated by 11%. The liquidity measure is understated by 565%. The first order auto-correlation coefficient for the rate of return is overstated by 14.9%. The average transaction volume per call is understated by 21.6%. The trading frequency is overstated by 21.9%. The first order auto-correlation coefficient for the volume is understated by 91.5%. To correct these errors, one should merge two transaction records that occurred at the same time for the same security. The true volume of a call is the sum of the volume of the two records; the true price of a call is either one of the prices of the two records.
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