Predicting Stock Price Changes Based on the Limit Order Book: A Survey

Abstract

This survey starts with a general overview of the strategies for stock price change predictions based on market data and in particular Limit Order Book (LOB) data. The main discussion is devoted to the systematic analysis, comparison, and critical evaluation of the state-of-the-art studies in the research area of stock price movement predictions based on LOB data. LOB and Order Flow data are two of the most valuable information sources available to traders on the stock markets. Academic researchers are actively exploring the application of different quantitative methods and algorithms for this type of data to predict stock price movements. With the advancements in machine learning and subsequently in deep learning, the complexity and computational intensity of these models was growing, as well as the claimed predictive power. Some researchers claim accuracy of stock price movement prediction well in excess of 80%. These models are now commonly employed by automated market-making programs to set bids and ask for quotes. If these results were also applicable to arbitrage trading strategies, then those algorithms could make a fortune for their developers. Thus, the open question is whether these results could be used to generate buy and sell signals that could be exploited with active trading. Therefore, this survey paper is intended to answer this question by reviewing these results and scrutinising their reliability. The ultimate conclusion from this analysis is that although considerable progress was achieved in this direction, even the state-of-art models can not guarantee a consistent profit in active trading. Taking this into account several suggestions for future research in this area were formulated along the three dimensions: input data, model’s architecture, and experimental setup. In particular, from the input data perspective, it is critical that the dataset is properly processed, up-to-date, and its size is sufficient for the particular model training. From the model architecture perspective, even though deep learning models are demonstrating a stronger performance than classical models, they are also more prone to over-fitting. To avoid over-fitting it is suggested to optimize the feature space, as well as a number of layers and neurons, and apply dropout functionality. The over-fitting problem can be also addressed by optimising the experimental setup in several ways: Introducing the early stopping mechanism; Saving the best weights of the model achieved during the training; Testing the model on the out-of-sample data, which should be separated from the validation and training samples. Finally, it is suggested to always conduct the trading simulation under realistic market conditions considering transactions costs, bid–ask spreads, and market impact.

The Intraday Dynamics Predictor: A TrioFlow Fusion of Convolutional Layers and Gated Recurrent Units for High-Frequency Price Movement Forecasting

“…Based on the analysis of prior works in the field, as described by Zaznov et al [11], the decision was made to concentrate on market data as the primary source of features for the stock price prediction model. Specifically, the focus is on high-frequency market data characterised by low latency.…”

“…As discussed in [11], extensive research has been dedicated to predicting stock price movements using diverse data sources and modelling techniques. Earlier studies exhibited a wide range of variations in datasets, experimental setups, and metrics for assessing model performance, rendering them nearly incomparable.…”

“…Specifically, these methods are not well suited to handling high-cardinality data, such as those found in limit order book (LOB) data. Studies, including [10][11][12], have highlighted the inadequacy of traditional models in handling the complexity of high-dimensional LOB data. This complexity arises from the sheer volume and granularity of data present in the LOB, which encapsulates a multitude of orders and transactions.…”

“…Deep learning approaches have emerged as promising solutions to address the limitations of traditional methods. In particular, the use of deep learning models to analyse LOB and order flow (OF) data has shown great potential in predicting stock price movements, as seen in the work by Zaznov I. et al [11]. LOB data contain information about all the buy-and-sell orders for a particular stock at a given time, including the price, volume, and time of each order.…”

Modelo de análisis multivariante aplicado a la predicción de la tendencia del tipo de cambio euro-dólar

“…Entre los tipos de información utilizados como variables de entrada se encuentran: información de mercado [319], indicadores técnicos [317], indicadores económicos [55,320] y noticias financieras [321,322] extraídas de publicaciones en redes sociales y el uso combinado de estos datos.…”