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We attempt to identify in this paper the role of trading noise as a transactions cost to market participant in the sense of Stoll (2000), especially in the presence of trading concentration. Applying the measures of Hu (2006) and Kang and Yeo (2008), we analyze the noise proportion in intraday stock returns and its interaction with investor herding and search cost. Although this noise is high on individual orders and low on institutional orders, its behavior at market open is entirely different from the rest of the day. Noises for small cap stocks, unlike volatilities, are lower than those for large cap stocks. We also found that noise relates positively to trading volume, but inversely to holdings and turnover ratio of institutional investors. Responses from institutional and individuals are quite the opposite. The noise proportion generated by individual order rises with institutional turnover and search cost encountered, while that of institutional order behaves just oppositely. At market open, behaviors of noise from institutional and individual orders just switch mutually, and then switch back afterwards. Also, noise from high-cap stocks is actually more responsive than that from low-cap ones across investors. So trading noise is a specific transactions cost, prominent to only certain investors, at certain time and for certain stocks in the market, rather than a general market friction as argued in Stoll (2000). This transactions cost is inversely related to search costs encountered in trading, which depends on investor, trading hour of day and market capitalization of stocks.

We analyze in this study cause of herding in a stock market. Information cascades have often been considered as a primary choice. However, we propose alternative explanations in this study. Employing intraday order book data, we suggest including an alternative theory based on search cost of investors, in addition to the information cascade argument. Specifically, previous works used data with daily or lower frequency based on a herding measure of Lakonishok, Shleifer, and Vishny (1992). We adopt instead a measure by Patterson and Sharma (2006) and argue that the search model of Vayanos and Wang (2007) characterizes herding phenomenon better, especially at market open. Our analysis is also consistent with the information competition equilibrium of Back, Cao and Willard (2000) and dynamic friction model of Hu (2006). We find that stronger order flow herding takes place when buyers and sellers are 6 times higher as well as when buyer-seller ratio is 50% higher, and is driven by lower transactions cost and shorter order-filling time. Herding tends to occur in trading of high-cap, high turnover stocks, which contradicts prediction of the information cascade hypothesis. Search cost effect is stronger at market open, while information cascade effect is stronger at market close. Therefore our study suggests that herding should be related both to intrinsic search cost structure of investors as well as information related factors.