Searching out of Trading Noise: A Study of Intraday Transactions Cost

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.Noise, transaction cost, herding, search model, order book

Diversifying Risks in Bond Portfolios: A Cross-border Approach

This study recalibrates corporate bond idiosyncratic risks in an international context. Applying a statistically powerful risk decomposition scheme, we show in this study that diversification is improved by the addition of a global risk benchmark. We build a long-run stationary yield spread decomposition scheme which provides better diversification effect. In addition to global liquidity and default risk factors, we also include country-specific default risk component, and all of them are free of measurement or availability issues. The idiosyncratic risk component is estimated as a fixed effect along with all the parameter estimates, rather than separately from an exogenous generating process. Our linear model is simple, yet it can be easily and promptly applied by practitioners

Diversifying Risks in Bond Portfolios: A Cross-border Approach

This study recalibrates corporate bond idiosyncratic risks in an international context. Applying a statistically powerful risk decomposition scheme, we show in this study that diversification is improved by the addition of a global risk benchmark. We build a long-run stationary yield spread decomposition scheme which provides better diversification effect. In addition to global liquidity and default risk factors, we also include country-specific default risk component, and all of them are free of measurement or availability issues. The idiosyncratic risk component is estimated as a fixed effect along with all the parameter estimates, rather than separately from an exogenous generating process. Our linear model is simple, yet it can be easily and promptly applied by practitioners

Searching out of Trading Noise: A Study of Intraday Transactions Cost

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

Does trading remove or bring frictions?

We explore in this paper how trading noise, when considered as a market friction, reacts to trading activity. Transactions cost is a good explanation for intraday trading behavior in the market according to our data. Particularly, we show that in general trading brings friction to market. However, trading friction at market open is the lowest during the day, as trading causes less friction then relatively. This is due to the behavioral difference among investors. When market opens, individual trading removes, while institutional trading brings, market friction. Situation in the rest of the day is just the opposite, where individual, instead of institutional, trading brings friction. The uneven behavior of trading noise across investors and time of day makes it a specific, rather than general, transactions cost, as opposed to Stoll (2000). Intraday trading activity suppresses both order width and depth, as proxies for trading intensity, therefore creates more noise or friction in the market. Width and depth contribute to trading noise in a polarized way, so that individual trading hurts friction in small cap stocks at open, but benefits it at close. Institutional trading brings extremely strong friction to large cap stocks, but less so at market close. So trading noise as a specific, rather than general, transactions cost is prominent only to certain investors, at certain time and for certain stocks in the market. Our findings lend itself to the justification of the new financial transactions tax proposed by the European Union

Behavioral investment strategy matters: a statistical arbitrage approach

In this study, we employ a statistical arbitrage approach to demonstrate that momentum investment strategy tend to work better in periods longer than six months, a result different from findings in past literature. Compared with standard parametric tests, the statistical arbitrage method produces more clearly that momentum strategies work only in longer formation and holding periods. Also they yield positive significant returns in an up market, but negative yet insignificant returns in a down market. Disposition and over-confidence effects are important factors contributing to the phenomenon. The over-confidence effect seems to dominate the disposition effect, especially in an up market. Moreover, the over-confidence investment behavior of institutional investors is the main cause for significant momentum returns observed in an up market. In a down market, the institutional investors tend to adopt a contrarian strategy while the individuals are still maintaining momentum behavior within shorter periods. The behavior difference between investor groups explains in part why momentum strategies work differently between up and down market states. Robustness tests confirm that the momentum returns do not come from firm size, overlapping execution periods, market states definition or market frictions

What Causes Herding:Information Cascade or Search Cost ?

We analyze in this study what could have caused herding in the stock market. Information cascades have often been considered as a major cause. However, we present in this study evidences inconsistent with that hypothesis. Our analysis is in support of an alternative theory based on search cost of investors. Specifically, previous works studied daily data or those with lower frequency based on a herding measure of Lakonishok, Shleifer, and Vishny (1992). We adopt instead the measure of Patterson and Sharma (2006) and argue that the search model of Vayanos and Wang (2007) characterize herding phenomenon better. Our analysis supports their hypothesis employing intraday order book data. We find that stronger order flow herding is driven by lower transactions cost. Herding tend to occur in trading of high-cap, high turnover stocks, which contradicts prediction of the information cascade hypothesis. Information cascade effect, if any, is actually stronger near market close than at open. Therefore our study suggests that herding could be related more to intrinsic search cost structure of investors rather than information related factors

Price informativeness and predictability: how liquidity can help

Information asymmetry and liquidity concentration has been widely discussed in literatures. This study shows how liquidity influences not only forecasting performances of term structure estimation, but also information transmission and price adjustment across markets. Our analysis helps understanding how extreme market movements affect one another. This study examines, and provides a rationale for incorporating, liquidity in estimating term structure. Forecasting performance can be greatly enhanced when conditioning on trading liquidity. It reduces information asymmetry in the sense of Easley and O’Hara (2004) and Burlacu, Fontaine and Jimenez-Garces (2007). We adopt a time series forecasting model following Diebold and Li (2006) to compare behavior of forecasted price errors. Our findings indicate that forecasted price errors in markets with less depth would influence those with more. Information asymmetry induces volatile trading first and then price adjustment is transmitted to another market due to insufficient market depth. Cross-market price adjustment could be as much as 21 bps on average. Compared with previous studies, our results establish a valid reason to condition on liquidity when forecasting prices

What Causes Herding:Information Cascade or Search Cost ?

We analyze in this study what could have caused herding in the stock market. Information cascades have often been considered as a major cause. However, we present in this study evidences inconsistent with that hypothesis. Our analysis is in support of an alternative theory based on search cost of investors. Specifically, previous works studied daily data or those with lower frequency based on a herding measure of Lakonishok, Shleifer, and Vishny (1992). We adopt instead the measure of Patterson and Sharma (2006) and argue that the search model of Vayanos and Wang (2007) characterize herding phenomenon better. Our analysis supports their hypothesis employing intraday order book data. We find that stronger order flow herding is driven by lower transactions cost. Herding tend to occur in trading of high-cap, high turnover stocks, which contradicts prediction of the information cascade hypothesis. Information cascade effect, if any, is actually stronger near market close than at open. Therefore our study suggests that herding could be related more to intrinsic search cost structure of investors rather than information related factors