earticle

영어

In this study, the well-known pairs trading strategy, one of typical market neutral strategies, is modified to be able to utilize high frequency equity data, and it is applied to the constituent shares of the KOSPI (Korea Composite Stock Price Index) 100 index. This study is distinguished from the most of previous work on the traditional pairs trading strategy in that we introduce the use of high frequency data in strategy modeling instead of daily closing prices, which allows us to analyze the performance of the strategy in high frequency domain. More specifically, we extract the trading signal, which is based on the spread between stocks of pair, by estimating time adaptive regression coefficients using the Kalman filter. As for underlying universe for the strategy, we confine ourselves to consider the most liquid 100 stocks in KOSPI as a basket for our experiment. Major findings include that arbitrage profitability is in fact present without being subject to market condition even when conservative transaction costs are taken into account. In particular, our strategy outperforms better in bear market condition while it varies depending on industry group. The results also demonstrate that the performance of the strategy is dependent upon timing of the market entry; the performance of trades entered around at opening and closing of the daily market is appeared to be relatively superior to that of trades executed in the rest of daily trading hours. Furthermore, we introduce an enhanced version of the strategy, which selects high-ranking pairs to trade for the next time period based on a set of in-sample statistics. It is verified that the enhanced strategy has better profitability and reliability compared to our basic strategy.