本研究分為主要兩個部分，第一部分根據 Chang, Chang and Wang (2014) 提出的日內動態訊息交易機率指標(DPIN), 衡量在台灣證券交易所掛牌上市的股票的交易訊息。 此外，我們運用台灣證券交易所特有的投資人身分類別資料，將訊息交易者細分為個人、基金、外資和其他法人等不同類型的訊息交易者。 據此，我們討論在不同的產業、交易量和不同市場環境(搓合秒數縮短)下，各個類型投資者在股票市場中私有訊息的特性。
第二部分從台灣50的成分股中挑出10支股票，討論以日內動態訊息交易指標做為高頻程式交易參考依據的可能性。透過日內動態訊息交易指標和未預期報酬判斷訊息交易者在市場上的交易行為並跟隨之，在利用歷史資料進行回測且不考慮交易成本的情況下，多數的股票都有相當好的累積報酬。但在考慮交易成本的情況下，過多的交易次數衍生的大量成本使得如此簡單的概念在現實中並不能獲利。為了建立在現實中可行的交易策略，我們加入三個準則並延長持有的期間以減少交易次數、提高預測價格變動方向的能力還有捕捉較大價格變動趨勢。最終在資料回測的情況下考慮三個準則的交易策略在多數股票中有著超越市場報酬的成果。

The paper consists of two parts. In the first part of this research, we evaluate the level of informed trading of TWSE (Taiwan Stock Exchange) listed stocks by using dynamic intraday measure of the probability of informed trading (DPIN measure) published in Chang, Chang and Wang (2014). In addition, with the unique dataset of traders’ types from TWSE, we distinguish the DPIN with four groups, namely, individual, funds, foreign investor and other corporation, to discuss the level of private information in different types of traders, in stock of different industries, under different levels of trading volumes, or under different market regulations (the shrink of time spread between each deal).
In the second part of the paper, we choose 10 stocks from TAI 50 to discuss the feasibility of using DPIN as criteria to practice high-frequency trading. We identify and follow the informed traders by DPIN and the unexpected return. Ignoring trading costs, our results show that most stocks have positive accumulative return. That said, our primitive trading strategies are unprofitable if trading costs are taken into account. We then add three more criteria and extend the holding period of each transaction to decrease the frequency of trading, to improve the ability to predict the changing direction of stock price, and to well-capture the local price trends. This modified trading strategies generate substantially higher profits relative to the buy-and-hold market portfolio, even when transaction costs are taken in account.

姜林祐杰 (2013) 程式交易:方法與實務應用. 台灣 : 新陸書局

盧陽正(2007) 不同類型投資人委託資訊與成交揭示資訊之透明度與優勢資訊內涵-以台灣證券交易所掛牌公司逐筆資料解析. 台灣：中華民國證券商業同業公會.

Aktas, N., E. de Bodt, F. Declerck, and H. Van Oppens. 2007. The PIN Anomaly around M&A Announcements. Journal of Financial Markets 10: 169–191.

Aldridge, I., 2013. High-frequency trading : a practical guide to algorithmic strategies and trading systems 2nd edition. New Jersey : Wiley.

Avramov, D., Chordia, T., Goyal, A., 2006. The impact of trades on daily volatility. Rev. Financ. Stud. 19, 1241–1277.

Campbell, J., Grossman, S., Wang, J., 1993. Trading volume and serial correlation in stock returns. Q. J. Econ. 31, 281–318.

Chang, S., Chang, L., Wang, A., 2014. A dynamic intraday measure of the probability of informed trading and firm-specific return variation J. Empir. Financ. 29, 80-94

Duarte, J., Young, L., 2009. Why is PIN priced? J. Financ. Econ. 91, 119–138.

Easley, D., Hvidkjaer, S., O'Hara, M., 2002. Is information risk a determinant of asset returns? J. Financ. 57, 2185–2221.

Easley, D., Kiefer, N., O'Hara, M., 1997a. One day in the life of a very common stock. Rev. Financ. Stud. 10, 805–835.

Easley, D., Kiefer, N., O'Hara, M., 1997b. The information content of the trading process. J. Empir. Financ. 4, 159–186.

Easley, D., Kiefer, N., O'Hara, M., Paperman, J., 1996. Liquidity, information and infrequently traded stocks. J. Financ. 51, 1405–1436.

Easley, D., O'Hara, M., 1987. Price, trade size, and information in securities markets. J. Financ. Econ. 19, 69–90.

Huang, R., Stoll, H., 1997. The components of the bid-ask spread: A general approach. Rev. Financ. Stud. 10, 995–1034.

Kyle, A., 1985. Continuous auctions and insider trading. Econometrica 53, 1315–1335.

Lee, C., Ready, M., 1991. Inferring trade direction from intraday data. J. Financ. 46, 733–746.

Lu, Y.C., Wong W., 2009. Probability of Information-Based Trading as a Pricing Factor in the Taiwan Stock Market. International Research Journal of Finance and Economics

Tay, A., Ting, C., Tse, Y., Warachka, M.,2009. Using High-Frequency Transaction Data to Estimate the Probability of Informed Trading. J. Financial Econometrics 7. 288-311