Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis. In this paper, we use Machine Learning (ML) algorithms for the prediction of future price movements using limit order book data. Two different sets of features are combined and evaluated: handcrafted features based on the raw order book data and features extracted by ML algorithms, resulting in feature vectors with highly variant dimensionalities. Three classifiers are evaluated using combinations of these sets of features on two different evaluation setups and three prediction scenarios. Even though the large scale and high frequency nature of the limit order book poses several challenges, the scope of the conducted experiments and the significance of the experimental results indicate that Machine Learning highly befits this task carving the path towards future research in this field.
The dynamics of the high frequency limit order book comprise a challenging field of study which has been investigated in past literature. An extensive survey on stochastic models and statistical techniques for modeling high frequency limit order book data can be found in @cite_31 , highlighting the inadequacies of statistical models as well as the need for more complex models, such as Machine Learning ones. Statistical models often make unrealistic assumptions about the distribution of the data, such as assuming a Poisson distribution of limit order events. Machine Learning techniques make no assumptions on the distribution of the data. Furthermore, the distribution of limit order events changes rapidly, not just from one day to the next but also within the same day. It is thus very challenging for statistical models which typically assume stationary signals to be used effectively used for modeling limit order book data. The drawbacks of statistical models and advantages of Machine Learning approaches have also been examined in @cite_2 . An extensive analysis of high frequency financial data can be found at @cite_3 , and the dynamics of limit order books are explained in detail in @cite_31 @cite_32 .
@cite_31 @cite_32 @cite_3 @cite_2
1981258337 2168802397 2131036538 1505330957
The availability of high-frequency data on transactions, quotes, and order flow in electronic order-driven markets has revolutionized data processing and statistical modeling techniques in finance and brought up new theoretical and computational challenges. Market dynamics at the transaction level cannot be characterized solely in terms the dynamics of a single price, and one must also take into account the interaction between buy and sell orders of different types by modeling the order flow at the bid price, ask price, and possibly other levels of the limit order book. We investigate several statistical properties of the order book of three liquid stocks of the Paris Bourse. The results are to a large degree independent of the stock studied. The most interesting features concern (i) the statistics of incoming limit order prices, which follows a power-law around the current price with a diverging mean; and (ii) the humped shape of the average order book, which can be quantitatively reproduced using a zero intelligence' numerical model, and qualitatively predicted using a simple approximation Materials of composition LavXwMyMnzOx, with x selected from Mg, Sc, Al, Zn, Cd, In and the rare earths that have an ionic radius smaller than that of La, with M selected from Ca, Sr, Ba and Pb, and with v, w, y, z and x in the ranges 0.45-0.85, 0.01-0.20, 0.20-0.45, 0.7-1.3 and 2.5-3.5, respectively, can have substantially improved magnetoresistance (MR) ratios, as compared to the corresponding X-free comparison material. In particular, the novel materials in polycrystalline (or non-epitaxial thin film) form can have relatively large MR ratios. For instance, polycrystalline La0.60Y0.07Ca0.33MnOx had a peak MR ratio in excess of 10,000 (in absolute value) in a field of 6 T. We propose a machine learning framework to capture the dynamics of high-frequency limit order books in financial equity markets and automate real-time prediction of metrics such as mid-price movement and price spread crossing. By characterizing each entry in a limit order book with a vector of attributes such as price and volume at different levels, the proposed framework builds a learning model for each metric with the help of multi-class support vector machines. Experiments with real data establish that features selected by the proposed framework are effective for short-term price movement forecasts.