2020/03/31 by Ye-Sheen Lim, Lim, Ye-Sheen, Denise Gorse +1
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Market Dynamics and Volatility #Statistical Finance (q-fin.ST) #Stock Market Forecasting Methods #Trading and Market Microstructure (q-fin.TR) #cs.LG #q-fin.ST #q-fin.TR #stat.ML
paper · pdf · doi:10.48550/arxiv.2004.01499
10 pages, The 19th International Conference on Artificial Intelligence and Soft Computing
arxiv created 2020/03/31 · openalex publication_date 2020/03/31 · arxiv updated 2020/04/06 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
In this paper we propose a deep recurrent model based on the order flow for the stationary modelling of the high-frequency directional prices movements. The order flow is the microsecond stream of orders arriving at the exchange, driving the formation of prices seen on the price chart of a stock or currency. To test the stationarity of our proposed model we train our model on data before the 2017 Bitcoin bubble period and test our model during and after the bubble. We show that without any retraining, the proposed model is temporally stable even as Bitcoin trading shifts into an extremely volatile "bubble trouble" period. The significance of the result is shown by benchmarking against existing state-of-the-art models in the literature for modelling price formation using deep learning.