Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data

Mäkinen, Ymir; Kanniainen, Juho; Gabbouj, Moncef; Iosifidis, Alexandros

doi:10.1080/14697688.2019.1634277

Cited by 46 publications

(49 citation statements)

References 68 publications

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“…As from above, Easley et al (2019) investigated price dynamics in future contract markets using random forests. Mäkinen et al (2019) proposed an approach with attention forecasting jump arrivals 1-minute ahead in stock prices. This mechanism, convolutional neural network, and Long Short-Term memory model are compared in their experiments.…”

Section: Related Workmentioning

confidence: 99%

Ascertaining price formation in cryptocurrency markets with machine learning

Fang

Chung

Ventre

et al. 2021

The European Journal of Finance

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The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using machine learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a machine learning approach to predict the direction of the mid-price changes on the upcoming tick. We show that there are universal features amongst cryptocurrencies which lead to models outperforming asset-specific ones. We also show that there is little point in feeding machine learning models with long sequences of data points; predictions do not improve. Furthermore, we solve the technical challenge to design a lean predictor, which performs well on live data downloaded from crypto exchanges. A novel retraining method is defined and adopted towards this end. Finally, the trade-off between model accuracy and frequency of training is analyzed in the context of multi-label prediction. Overall, we demonstrate that promising results are possible for cryptocurrencies on live data, by achieving a consistent 78% accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs. US dollars.

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Section: Related Workmentioning

confidence: 99%

Ascertaining price formation in cryptocurrency markets with machine learning

Fang

Chung

Ventre

et al. 2021

The European Journal of Finance

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“…This again is in contrast to extensive research on the information content of the deeper layers and its impact on prices and the bid-ask spread, which has been the topic of extensive debate in the literature. See, for example, (Mäkinen et al, 2019 ; Nousi et al, 2019 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Quoted Depth With the Limit Order Book

Libman

Haber

Schaps

2021

Front. Artif. Intell.

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Liquidity plays a vital role in the financial markets, affecting a myriad of factors including stock prices, returns, and risk. In the stock market, liquidity is usually measured through the order book, which captures the orders placed by traders to buy and sell stocks at different price points. The introduction of electronic trading systems in recent years made the deeper layers of the order book more accessible to traders and thus of greater interest to researchers. This paper examines the efficacy of leveraging the deeper layers of the order book when forecasting quoted depth—a measure of liquidity—on a per-minute basis. Using Deep Feed Forward Neural Networks, we show that the deeper layers do provide additional information compared to the upper layers alone.

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“…The attention mechanism is widely used in financial time series analysis in recent years [41], [42]. According to the attention mechanism, context vector c i depends on a sequence of annotation (ℎ 1 , … , ℎ ) .…”

Section: ) Lstm Blockmentioning

confidence: 99%

Discovery and Prediction of Stock Index Pattern via Three-Stage Architecture of TICC, TPA-LSTM and Multivariate LSTM-FCNs

Ouyang

Wei

2020

IEEE Access

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In this study, we attempt to discover and predict stock index patterns through analysis of multivariate time series. Our motivation is based on the notion that financial planning guided by pattern discovery and prediction of stock index prices maybe more realistic and effective than traditional approaches, such as Autoregressive Integrated Moving Average (ARIMA) model. A three-stage architecture constructed by combining Toeplitz Inverse Covariance-Based Clustering (TICC), Temporal Pattern Attention and Long-Short-Term Memory (TPA-LSTM) and Multivariate LSTM-FCNs (MLSTM-FCN and MALSTM-FCN) is applied for pattern discovery and prediction of stock index. In the first stage, we use TICC to discover repeated patterns of stock index. Then, in the second stage, TPA-LSTM that considers weak periodic patterns and long short-term information is used to predict multivariate stock indices. Finally, in the third stage, MALSTM-FCN is applied to predict stock index price pattern. The Hangseng Stock Index and eleven industrial sub-indices are used in the experiment. Empirical results show that the three-stage architecture achieves satisfactory and better performance than traditional methods, such as Naive Bayes Classifier (NB), Support Vector Machine Classifier (SVM), Random Forest (RF), etc. Moreover, we construct equal proportion portfolios based on the bullish trading rules to further analyze the feasibility of the proposed threestage architecture. Seven comprehensive stock indices are used in the experiment. Empirical results show that the portfolio based on the proposed three-stage architecture presents better performance than the marketbased portfolio. These findings may provide new direction for the portfolio construction and risk aversion. INDEX TERMS Stock index pattern, pattern discovery, pattern prediction, multivariate time series.

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Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data

Cited by 46 publications

References 68 publications

Ascertaining price formation in cryptocurrency markets with machine learning

Ascertaining price formation in cryptocurrency markets with machine learning

Forecasting Quoted Depth With the Limit Order Book

Discovery and Prediction of Stock Index Pattern via Three-Stage Architecture of TICC, TPA-LSTM and Multivariate LSTM-FCNs

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