# Universal features of price formation in financial markets: perspectives from Deep Learning

arXiv: https://arxiv.org/abs/1803.06917 · HTML: https://mlsanity.com/paper/1803.06917/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2018.

## Built on by (most cited first, 30 of 60)

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- [Deep Limit Order Book Forecasting](https://mlsanity.com/paper/2403.09267/) (2024)
- [How Robust are Limit Order Book Representations under Data Perturbation?](https://mlsanity.com/paper/2110.04752/) (2021)
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- [TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure](https://mlsanity.com/paper/2602.23784/) (2026)
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- [Denoised Labels for Financial Time-Series Data via Self-Supervised Learning](https://mlsanity.com/paper/2112.10139/) (2021)
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- [TLOB: A Novel Transformer Model with Dual Attention for Price Trend Prediction with Limit Order Book Data](https://mlsanity.com/paper/2502.15757/) (2025)
- [Computational Performance of Deep Reinforcement Learning to find Nash Equilibria](https://mlsanity.com/paper/2104.12895/) (2021)
- [Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets](https://mlsanity.com/paper/2301.08688/) (2023)
- [Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting](https://mlsanity.com/paper/2304.09840/) (2023)
- [Dynamic graph neural networks for enhanced volatility prediction in financial markets](https://mlsanity.com/paper/2410.16858/) (2024)
- [ByteGen: A Tokenizer-Free Generative Model for Orderbook Events in Byte Space](https://mlsanity.com/paper/2508.02247/) (2025)
- [LOBERT: Generative AI Foundation Model for Limit Order Book Messages](https://mlsanity.com/paper/2511.12563/) (2025)
- [The Limit Order Book Recreation Model (LOBRM): An Extended Analysis](https://mlsanity.com/paper/2107.00534/) (2021)
- [Intra-Day Price Simulation with Generative Adversarial Modelling of the Order Flow](https://mlsanity.com/paper/2109.13905/) (2021)

## Abstract (arXiv)

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 is shown to exhibit 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 is shown to improve forecasting performance, showing evidence of path-dependence in price dynamics.