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 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.