Asymmetric effects of the limit order book on price dynamics

Abstract

We analyze whether the information in different parts of the limit order book affect prices differently. We distinguish between slopes of lower and higher levels of the bid and ask sides and include these four slope measures as well as midquote return and trade direction in a vector autoregressive model. Slope measures of the same side based on different levels affect both short-and long-run price dynamics quite differently, in line with the predictions based on recent theoretical models such as Foucault, Kadan, and Kandel (2005) and Rosu (2009). In a high frequency day trading exercise, we show that ignoring these asymmetries costs a trader approximately 25 basis points in daily profits, suggesting that the asymmetries are important not only statistically but also economically. Our statistical results are robust to using alternative definitions of slope measures and sample periods while our economic results are robust to trading under alternative assumptions such as trading slower speeds.

Data-driven Methods for Simulation and Forecasting of Financial Time Series

“…Cenesizoglu, Dionne, and Zhou [58], and Hasbrouck and Saar [130]) demonstrated that traders might strategically choose to place their orders in different levels of the book depending on various factors, therefore limit orders at different price levels may contain different information content with respect to predicting future returns. A further study with more focus on the impact of multi-level OFIs over different time horizons is suggested.…”

Bayesian bilinear neural network for predicting the mid‐price dynamics in limit‐order book markets

“…A review of different NN applications in finance is provided in McNelis (2005), an early discussion on econometric applications can be found in Kuan and White (1994) and within time-series analysis in Hewamalage et al (2021), Qi andZhang (2008), andTeräsvirta et al (2005). Cenesizoglu et al (2022) analyzed the relationship between LOB variables and mid-price movements showing that it is possible to obtain economical gain from these variables and the mid-price return. Further, their causality analysis supports the use of lagged LOB variables for forecasting purposes.…”

Online Hybrid Neural Network for Stock Price Prediction: A Case Study of High-Frequency Stock Trading in the Chinese Market

“…Regarding the statistical parametric models for stock price prediction, Cenesizoglu et al (2016) extracted the informative variables that characterize LOBs, so as to establish a vector auto-regressive (VAR) model to analyze how various features of the LOBs affect prices. Mondal et al (2014) evaluated the accuracy and variability of stock price forecasts using an auto-regressive integrated moving average (ARIMA) model.…”