Universal features of price formation in financial markets: perspectives from deep learning

Abstract

Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of electronic market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary price formation mechanism relating the dynamics of supply and demand for a stock, as revealed through the order book, to subsequent variations in its market price. We assess the model by testing its out-of-sample predictions for the direction of price moves given the history of price and order flow, across a wide range of stocks and time periods. The universal price formation model exhibits a remarkably stable out-of-sample prediction accuracy across time, for a wide range of stocks from different sectors. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific.The universal model -trained on data from all stocks -outperforms, in terms of out-of-sample prediction accuracy, asset-specific linear and nonlinear models trained on time series of any given stock, showing that the universal nature of price formation weighs in favour of pooling together financial data from various stocks, rather than designing asset-or sector-specific models as commonly done. Standard data normalizations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations improves forecasting performance, showing evidence of path-dependence in price dynamics.

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

“…In [216], the authors used the limit order book time series data and LSTM method for trend prediction. The authors of [217] proposed a novel method that used limit order book flow and history information for the determination of the stock movements using LSTM. The results of the proposed method were remarkably stationary.…”

DeepLOB: Deep Convolutional Neural Networks for Limit Order Books

“…It is far from trivial to select informative features by hand. Recent research [11,10,14] has shown that deep learning models can deliver good predictive performance using only raw LOBs data with the process of feature extraction being automated by convolutional layers. However, such deep learning techniques are (typically) non-probabilistic and thus provide only heuristic uncertainty measures associated with output variables.…”

Enhancing Time Series Momentum Strategies Using Deep Neural Networks

“…While numerous papers have investigated the use of machine learning for financial time series prediction, they typically focus on casting the underlying prediction problem as a standard regression or classification task [23,24,25,12,26,19,27]with regression models forecasting expected returns, and classification models predicting the direction of future price movements. This approach, however, could lead to suboptimal performance in the context time-series momentum for several reasons.…”