The pairs trading is a strategy often adopted to identify arbitrage opportunity based on historical equilibrium in spread between the share prices. Basically, an investor evaluates the current position of the spread based on its historical fluctuations and seizes the moment when the current spread deviates from its historical mean level by a pre-determined threshold. In this study, the well-known basic pairs trading strategy, one of typical market neutral strategies, is modified so as to utilize high frequency data, and it is also applied to the constituent shares of the KOSPI (The Korea Composite Stock Price Index) 100 index. We also introduce the use of the high frequency equity data in strategy modeling, although the industry practice for market neutral hedge funds is to use daily sampling frequency of equity data in designing a trading model. In this study, intraday stock prices data sampled at a 30-minute interval is used for the strategy, and the performance is analyzed in high frequency domain. The data set covers the horizon from the 1st of October 2008 to the 31st of July 2010, which includes bullish, bearish, and flat market periods within the horizon. We highlight how perfor- mance varies depending on market condition, industry group, and timing of the market entry. This study is distinguished from the most previous works 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 comprising a pair, by estimating time adaptive regression coefficient using the Kalman filter scheme. This study is the first practice in the realm of the high frequency market neutral trading strategy that extracts trading signal by estimating time adaptive regression coefficient using the Kalman filter scheme. Moreover, our loss-cut strategy clears position if holding duration exceeds the pre-determined maximum trading duration. As for the underlying universe for the strategy, we confine ourselves by considering only the most liquid top 100 stocks in terms of larger trading amounts and higher liquidity as a basket for our experiment. This is to get rid of other external variables that may add undesirable noises to the overall performance which would make it difficult to analyze pure performance of the strategy itself. We analyze the results of out-of-sample performance test from various angles. 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 the bear market condition, showing 2.55% of average rate of return per trade in bearish period, which is higher than 0.80% in the bullish period and 0.39% in the flat period. The performance of the pair trading strategy varies depending on the industry group. Those industry groups dominantly influenced by domestic demand i.e. non-cyclical in Korea such as Distribution, Household & Personal Products, and Automobiles and Components show relatively higher winning ratios and average rates of return per trade whereas the industry groups involving fast-paced technological development and variable international demand such as Technology Hardware and Software & Services give out relatively low statistics. The results also demonstrate that the performance of the strategy is dependent upon the time when trading position is set up during daily trading hours; the performance of trades entered around at opening and closing of the daily market appears to be relatively superior to that of trades executed in the rest of daily trading hours in terms of the average rate of return per trade, the winning ratio, and the information ratio. Recalling that intraday volatility pattern is generally formed in U-shape, the strategy seems to achieve higher performance in intraday time zone with higher volatility, which also corresponds to our previous finding that our strategy returns outstanding figures in volatile market trend. Besides, the strategy seems to take advantage of inefficiency derived from where stock price reflects the undisclosed market information. Furthermore, we introduce an enhanced version of the pair trading strategy and compare the performances with the basic strategy. One difference between the basic and the enhanced model is that it selects high-ranking pairs to trade for the next time period based on a set of in-sample statistics, which includes in-sample ADF (Augmented Dickey-Fuller) test t-statistics, in-sample information ratio, and in-sample mean reversion strength. In our study, moving window with the size of 2 weeks is considered as an in-sample period. It is verified that the performance of the enhanced strategy had better profitability and reliability compared with our basic strategy.