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on Market Microstructure |
| By: | Vives, Xavier; Yang, Liyan |
| Abstract: | We propose a model in which investors cannot costlessly process information from asset prices. At the trading stage, investors are boundedly rational and their interpretation of prices injects noise into the price, generating a source of endogenous noise trading. Compared to the standard rational expectations equilibrium, our setup features price momentum and yields higher return volatility and excessive trading volume. In an overall equilibrium, investors optimally choose sophistication levels by balancing the benefit of beating the market against the cost of acquiring sophistication. Investors tend to over-acquire sophistication. There can exist strategic complementarity in sophistication acquisition, leading to multiple equilibria. |
| Keywords: | asset prices; disagreement; Investor sophistication; multiplicity; noise trading; trading volume; welfare |
| Date: | 2017–10 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:12360&r=mst |
| By: | Shanshan Wang; Sebastian Neus\"u{\ss}; Thomas Guhr |
| Abstract: | We measure the price impacts across a correlated financial market by the responses to single and multiple trades. Focusing on the primary responses, we use an event time scale. We quantify the asymmetries of the distributions and of the market structures of cross-impacts, and find that the impacts across the market are asymmetric and non-random. Using spectral statistics and Shannon entropy, we visualize the asymmetric information in market impacts. Also, we introduce an entropy of impacts to estimate the randomness between stocks. We show that the useful information is encoded in the impacts corresponding to the small entropy. The stocks with large number of trades are more likely to impact others, while the less traded stocks have higher probability to be impacted by others. |
| Date: | 2017–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1710.07959&r=mst |
| By: | Park, Beum-Jo; Kim, Myung-Joong |
| Abstract: | This paper suggests a dynamic measure of intentional herding, causing the excess volatility or even systemic risk in financial markets, which is based on a new concept of cumulative returns in the same direction as well as the collective behavior of all investors towards the market consensus. Differing from existing measures, the measure allows us to directly detect time-varying and market-wide intentional herding using the model of Dynamic Conditional Correlation (DCC) (Engle, 2002) between the financial market and its components that is partially free of spurious herding due to the inclusion of the variables of the number of economic news announcements as a proxy of market information. Strong evidence in favor of the dynamic measure over the other measures is based on empirical application in the U.S. markets (DJIA and S&P100), supporting the tendency to exhibit time-varying intentional herding. Much more important is a finding that the impact of intentional herding on market volatility tends to be stronger during the periods of turbulent markets like the degradation of U.S. sovereign credit rating by S&P, and be more significant in S&P 100 than DJIA. |
| Keywords: | Intentional herd behavior, Dynamic conditional correlation, News announcements, Dynamic measure, Herding tests, Volatility, Quantile regression |
| JEL: | C10 G0 G02 |
| Date: | 2017–10–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:82025&r=mst |