This chapter somewhat departs from our initial motivation of studying limit order books per se, and addresses the very practical question of the predictability of financial markets based on the information content of limit order books.
Forecasting the market has always been one of the “hottest” topics among market practitioners, and the temptation to identify hopefully profitable signals has never been as high as today. Numerous academic studies aim at identifying some predictive features in the time series of past returns, although many seem to obtain negative results. For instance, it is a well-known stylized fact that there is no evidence of linear correlation between successive returns, see e.g., (Chakraborti et al. 2011a) (Lillo and Farmer, 2004). Such studies seemingly demonstrate the lack of predictive character of the series of past returns, as far as the sign of the next price move is concerned. In that sense, the property generally referred to as the Efficient Market Hypothesis does not seem to be challenged.
Rather intriguingly, several books – some popular amongst finance practitioners – introduce and explain predictive strategies that seem to always make money (see e.g., Murphy, 1999; Vidyamurthy, 2004). But, when backtesting those strategies on realistic samples, the results are often quite disappointing, and the strategies no longer profitable. It is likely that the plague of over-fitting, inherent to many prediction methods, plays a key role in the seemingly good performances published in those books.
However, there exist several ways to actually make better predictions than just using the series of past returns. For instance, Abergel and Politi (2013) exhibit some synthetic baskets that are not traded and therefore, not necessarily arbitrage-free. Based on these baskets, they provide evidence of short-term predictability. More specific to the context of order-driven markets, the use of limit order book data has yielded interesting prediction results (Zheng et al. 2012; Anane et al. 2015; Anane and Abergel, 2015; Cont et al. 2014).
The study presented in this chapter is performed both from an academic and a professional perspective. It is based on an extensive use of market data, inclusive of limit order book data, and aims at identifying signals that can be used as forecasting tools, and studying their performances. Several prediction methods are introduced and systematically benchmarked.