Order book information is crucial for traders, but it can be complex. With the numbers of stocks listed in stock exchanges, it is impossible to track all the available information for the human mind. Therefore, the order flows could be an interesting dataset for machine learning models. The novel research of Kolm, Turiel and Westray (2021) utilizes deep-learning for high-frequency return forecasts for 115 NASDAQ stocks based on order book information at the most granular level.

The paper has several key contributions. Firstly, it does not forecast one single return but rather a whole vector of returns – a term structure consisting of mid-price return forecasts at a specified horizon. The forecasted term structure provides essential information about the most optimal execution algorithms (or a trading strategy). According to the authors, forecasts have an „accuracy peak“ at two price changes, after which the accuracy declines. Secondly, the paper compares several methods: autoregressive model with exogenous inputs, MLP, LSTM, LSTM-MLP, stacked LSTM, and CNN-LSTM. Therefore, the article could also serve as a horse race across several possible forecasting methods. Lastly, using more traditional statistical approaches, the authors have identified a better forecasting performance in more information-rich stocks. As a result, this novel research could benefit many areas such as high-frequency trading (but trading costs must be considered), optimal execution strategy, or market-making.