We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilizes convolutional filters to capture the spatial structure of the LOBs as well as long short-term memory modules to capture longer time dependencies. The proposed network outperforms all existing state-of-the-art algorithms on the benchmark LOB dataset [A. Ntakaris, M. Magris, J. Kanniainen, M. Gabbouj, and A. Iosifidis, “Benchmark dataset for mid-price prediction of limit order book data with machine learning methods,” J. Forecasting , vol. 37, no. 8, 852–866, 2018]. In a more realistic setting, we test our model by using one-year market quotes from the London Stock Exchange, and the model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments. Importantly, our model translates well to instruments that were not part of the training set, indicating the model's ability to extract universal features. In order to better understand these features and to go beyond a “black box” model, we perform a sensitivity analysis to understand the rationale behind the model predictions and reveal the components of LOBs that are most relevant. The ability to extract robust features that translate well to other instruments is an important property of our model, which has many other applications.
@cite_47: 
@cite_69: Stock markets are affected by many uncertainties and interrelated economic and political factors at both local and global levels. The key to successful stock market forecasting is achieving best results with minimum required input data. To determine the set of relevant factors for making accurate predictions is a complicated task and so regular stock market analysis is very essential. More specifically, the stock market’s movements are analyzed and predicted in order to retrieve knowledge that could guide investors on when to buy and sell. It will also help the investor to make money through his investment in the stock market. This paper surveys large number of resources from research papers, web-sources, company reports and other available sources.
@cite_22: 
@cite_15: We propose a survey of soft computing techniques applied to financial market.We surveyed several primary studies proposed in the literature.A framework for building an intelligent trading system was proposed.Future directions of this research field are discussed. Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision-making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field.
@cite_28: Evidence exists that emerging market stock returns are influenced by a different set of factors than those that influence the returns for stocks traded in developed countries. This study uses artificial neural networks to predict stock price movement (i.e., price returns) for firms traded on the Shanghai stock exchange. We compare the predictive power using linear models from financial forecasting literature to the predictive power of the univariate and multivariate neural network models. Our results show that neural networks outperform the linear models compared. These results are statistically significant across our sample firms, and indicate neural networks are a useful tool for stock price prediction in emerging markets, like China.
@cite_39: 
@cite_57: We ask whether stock returns in France, Germany, Japan, the UK and the US are predictable by three instruments: the dividend yield, the earnings yield and the short rate. The predictability regression is suggested by a present value model with earnings growth, payout ratios and the short rate as state variables. We use this model imposing a constant risk premium to examine the finite sample evidence on predictability. Not only do we find the short rate to be a relevant state variable theoretically, it is also the only robust short-run predictor of equity returns. The evidence in Lamont (1998) on earnings and dividend yield predictability is not robust to our increased sample period, does not survive finite sample corrections and does not extend to other countries. We find no evidence of long-horizon predictability once we account for finite sample influence. Finally, cross-country predictability appears stronger than predictability using local instruments.
@cite_43: 
@cite_46: Recent empirical evidence suggests that the variance risk premium, or the difference between risk-neutral and statistical expectations of the future return variation, predicts aggregate stock market returns, with the predictability especially strong at the 2-4 month horizons. We provide extensive Monte Carlo simulation evidence that statistical finite sample biases in the overlapping return regressions underlying these findings can not “explain” this apparent predictability. Further corroborating the existing empirical evidence, we show that the patterns in the predictability across different return horizons estimated from country specific regressions for France, Germany, Japan, Switzerland and the U.K. are remarkably similar to the pattern previously documented for the U.S. Defining a “global” variance risk premium, we uncover even stronger predictability and almost identical cross-country patterns through the use of panel regressions that effectively restrict the compensation for world-wide variance risk to be the same across countries. Our findings are broadly consistent with the implications from a stylized two-country general equilibrium model explicitly incorporating the effects of world-wide time-varying economic uncertainty.
@cite_16: The key to successful stock market forecasting is achieving best results with minimum required input data. Given stock market model uncertainty, soft computing techniques are viable candidates to capture stock market nonlinear relations returning significant forecasting results with not necessarily prior knowledge of input data statistical distributions. This paper surveys more than 100 related published articles that focus on neural and neuro-fuzzy techniques derived and applied to forecast stock markets. Classifications are made in terms of input data, forecasting methodology, performance evaluation and performance measures used. Through the surveyed papers, it is shown that soft computing techniques are widely accepted to studying and evaluating stock market behavior.
@cite_75: 
Research on the predictability of stock markets has a long history in the financial literature e.g., @cite_57 @cite_75 . Although opinions differ regarding the efficiency of markets, many widely accepted studies show that financial markets are to some extent predictable @cite_46 @cite_43 @cite_47 @cite_22 . Two major classes of work which attempt to forecast financial time-series are, broadly speaking, statistical parametric models and data-driven machine learning approaches @cite_69 . Traditional statistical methods generally assume that the time-series under study are generated from a parametric process @cite_15 . There is, however, agreement that stock returns behave in more complex ways, typically highly nonlinearly @cite_28 @cite_39 . Machine learning techniques are able to capture such arbitrary nonlinear relationships with little, or no, prior knowledge regarding the input data @cite_16 .