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on Market Microstructure |
| By: | Michael J. Fleming; Giang Nguyen; Bruce Mizrach |
| Abstract: | It?s long been known that asset prices respond not only to public information, such as macroeconomic announcements, but also to private information revealed through trading. More recently, with the growth of high-frequency trading, academics have argued that limit orders?orders to buy or sell a security at a specific price or better?also contain information. In this post, we examine the information content of trades and limit orders in the U.S. Treasury securities market, following this paper, recently published in the Journal of Financial Markets and earlier as a New York Fed staff report. |
| Keywords: | Price impact; limit orders; information; Treasury market |
| JEL: | G1 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednls:87299&r=all |
| By: | Roberto Riccò; Barbara Rindi; Duane J. Seppi |
| Abstract: | This paper describes price discovery and liquidity provision in a dynamic limit order market with asymmetric information and non-Markovian learning. Investors condition on information in both the current limit order book and also, unlike in previous research, on the prior order history when deciding whether to provide or take liquidity. Our analysis shows that the information content of the prior order history can be substantial. Surprisingly, the information content of equilibrium orders can differ from order direction and aggressiveness. JEL classiffication: G10, G20, G24, D40. Keywords: Limit order markets, asymmetric information, liquidity, market microstructure. |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:igi:igierp:660&r=all |
| By: | Dat Thanh Tran; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis |
| Abstract: | Financial time-series analysis and forecasting have been extensively studied over the past decades, yet still remain as a very challenging research topic. Since financial market is inherently noisy and stochastic, a majority of financial time-series of interests are non-stationary, and often obtained from different modalities. This property presents great challenges and can significantly affect the performance of the subsequent analysis/forecasting steps. Recently, the Temporal Attention augmented Bilinear Layer (TABL) has shown great performances in tackling financial forecasting problems. In this paper, by taking into account the nature of bilinear projections in TABL networks, we propose Bilinear Normalization (BiN), a simple, yet efficient normalization layer to be incorporated into TABL networks to tackle potential problems posed by non-stationarity and multimodalities in the input series. Our experiments using a large scale Limit Order Book (LOB) consisting of more than 4 millions order events show that BiN-TABL outperforms TABL networks using other state-of-the-arts normalization schemes by a large margin. |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2003.00598&r=all |