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Title: | 配對交易於台灣股票市場之實證研究 —以金融業、紡織業、半導體業為例 An Empirical Study of Pairs Trading in the Finance、Textile and Semi-Conductor Industry |
Authors: | 蔡景任 Tsai, Ching-Jen |
Contributors: | 郭維裕 Kuo, Wei-Yu 蔡景任 Tsai, Ching-Jen |
Keywords: | 配對交易 零期交錯 最小均方和 產業配對 統計套利 Pairs trading Zero crossing Sum of squared deviations Industry pairs Statistic arbitrage |
Date: | 2018 |
Issue Date: | 2018-07-17 11:22:02 (UTC+8) |
Abstract: | 台灣加權指數在2017年5月突破萬點,並維持了整整一年的時間,創下史上最長萬點的紀錄,也讓投資人對股票市場更具信心,成交量近乎每日1,000億以上。股價越來越高的情況下,主動式投資如何選擇適當的投資標的以提升獲利;被動式投資又該選擇哪個市場的指數進行長期投資,成為了所有投資人最關切的課題。在如此活絡且多頭的股票市場下,建立在統計套利概念上的配對交易策略,是否能於過去這段時間依然保有其獲利能力,為本研究的主要研究動機。
本研究將配對交易運用於台灣的三個主要產業:金融業、紡織業與半導體業,並產業中的調整後股價作為資料樣本,將調整後股價做股價標準化,並透過產業內每檔個股的標準化股價,選出前20組擁有最小均方和(SSD)的配對。再加上Zero Crossing,挑出前50組最小均方和中的最高20組Zero Crossing配對,希望藉由Zero Crossing來提升配對的收斂程度。
本研究以2010/3/2至2016/2/26作為樣本交易期間,分成4段樣本期間,每段共1.5年。第1年為配對形成期,往後的半年為配對交易期,在交易期第1天重新將股價標準化,當交易期的標準化股價價差分別超過形成期的標準化價差的0.5倍與1倍標準差時,便作為啟動交易的訊號。
實證結果顯示,台灣的金融業與紡織業最小SSD的配對報酬優於最大Zero Crossing的報酬表現,而半導體業中的Zero Crossing則有優化配對報酬的結果。且在0.5倍標準差的交易訊號,交易更頻繁的情況下,金融業與紡織業的配對報酬優於1倍標準差,而半導體業則為一半0.5倍標準差較好,一半1倍標準差較好。而從台灣加權指數的表現來觀察配對交易,金融業與紡織業在空頭市場時較有獲利空間,而半導體業則是在多頭市場時獲利表現較好。 In May 2017, Taiwan Stock Index closed above 10,000 level and it lasted for more than a year till today. Not only did it create the longest bull market and 10,000 level record in Taiwan stock market, but it also intensified the confidence of investors. As daily trading volume keep at over 100 billion NTD level and the stock price is getting higher and higher, the questions of how to invest smartly become hot and popular again. How is the subjective way to choose our investing target to gain more profit? And by passive investing, which country and what kind of index should we do the long investing?
On top of the two methods mentioned above, based on the statistic arbitrage, pairs trading is another good strategy that has been used all over the world. During the past 6 years and the bull market time, does pairs trading still maintain its profit-making ability in Taiwan stock market is our main research motivation.
We applied pairs trading into three main industry of Taiwan, which is Finance, Textile and Semi-Conductor. We chose the adjusted stock price as our data sample and turned the adjusted price into normalized price (starts at 1). By comparing each normalized price in each industry, we picked up the top 20 pairs with lowest SSDs (Sum of Square Deviations) and the top 20 pairs with the highest number of zero crossing among the 50 pairs with the lowest SSDs. By introducing zero crossing, we hope that zero crossing can increase the convergence and profit of pairs.
From March 2, 2010 to February 26, 2016 is our sample period, and we divide sample period into four sub period. Each sub period lasts for 1.5 year, the first year is the pairs formation period, and the next six months is pairs trading period. At the very first day of trading period, we normalized the adjusted price into normalized price again and made the price of first trading day starts at 1.
Whenever the spread of the normalized price in trading period diverge by more than half and one standard deviations from the historical spread observed over the formation period, we take that as trading signal and create our trading position.
Empirical results show that the return with lowest SSDs have better performance than the return with highest number of zero crossing in Finance and Textile industry. And it turns out that with highest number of zero crossing in Semi-Conductor industry have optimized the return.
Under the different trading signal circumstances, in the Finance and Textile industry the half standard deviations signal can gain more profit than one standard deviations. To the Semi-Conductor industry, half performance of half standard deviations is better and half return of one standard deviations is better.
From the perspective of whole stock market, Finance and Textile industry get profits at the bear market time, however, the Semi-Conductor can gain more returns at bull market period. |
Reference: | Do, Binh., and R. Faff, 2010, “Does Simple Pairs Trading Still Work?,” Financial Analyst Journal, 66, 88-95.
Desai, Jay., A. Trivedi and N. Joshi, 2012, “The Case of Gold and Silver: A New Algorithm for Pairs Trading,”. Available at SSRN: http://ssrn.com/abstract=2152324
Fama, E., and K. French, 1997, “Industry Costs of Equity,” Journa of Finanical Economics, 43, 153-193.
Gatev, E., W. Goetzmann, and K. Rouwenhorst, 2006, “Pairs Trading Performance of a Relative-Value Arbitrage Rule,” The Review of Financial Studies, 19, 797-827.
Jegadeesh, N., And S. Titman, 1993, “Returns to Buying Winners and Selling Losers: Implication for Stock Market Efficiency,” Journal of Finance, 48, 65-91.
Panagiotis, S., D. Thomakos and T. Wang, 2011, “Pairs Trading on International ETFs,”. Available at SSRN: https://ssrn.com/abstract=1958546
Rudy, J., C. Dunis, G. Giorgioni and J. Laws, 2010, “Statistical Arbitrage and High-Frequency Data with an Application to Eurostoxx 50 Equities,” Working Paper, University of Liverpool.
Temlyakov, K, 2012, “Optimal Parameters to Pairs Trading,” Working Paper. Available at SSRN: http://ssrn.com/abstract=2140111
李梓萱 (2011)。配對交易是否仍有其功效—以台灣股票市場為例,逢甲大學財務金融系,台中。
藍慶芳 (2013)。運用配對交易於台灣股票期貨之實證研究,朝陽科技大學金融系,台中。 |
Description: | 碩士 國立政治大學 國際經營與貿易學系 105351017 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0105351017 |
Data Type: | thesis |
DOI: | 10.6814/THE.NCCU.IB.020.2018.F06 |
Appears in Collections: | [國際經營與貿易學系 ] 學位論文
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