Encoding of high-frequency order information and prediction of short-term stock price by deep learning

Tashiro, Daigo; Matsushima, Hiroyasu; Izumi, Kiyoshi; Sakaji, Hiroki

doi:10.1080/14697688.2019.1622314

Cited by 35 publications

(15 citation statements)

References 7 publications

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“…In their study, the result of comparing the predictive performance of CNN with ANN and SVM illustrated that the CNN proved a better performance in forecasting stock price. Tashiro et al [48] firstly criticize the current models for the price prediction in the stock markets that these models ignore properties of market orders. Therefore, they come up with the CNN architecture integrating orderbased features to predict the mid-price trends in the stock markets.…”

Section: Cnnmentioning

confidence: 99%

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Data Science in Economics

Nosratabadi¹,

Mosavi²,

Duan³

et al. 2020

Preprint

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This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The novel data science methods and applications are investigated in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide range of economics research, from stock market, marketing, E-commerce, to corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on the advancement of hybrid models. On the other hand, based on the accuracy metric it is also reported that the hybrid models outperform other learning algorithms. It is further expected that the trends would go toward the advancements of sophisticated hybrid deep learning models.

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Section: Cnnmentioning

confidence: 99%

“…Similar to LSTM, the CNN algorithm is applied mainly for financial time series data to stock price prediction [41,46,48,58] and cryptocurrencies price prediction [84]. CNN algorithm is also used for analyzing social media data for the purpose of sentiment analysis [52].…”

Section: Cryptocurrencymentioning

confidence: 99%

Data Science in Economics

Nosratabadi¹,

Mosavi²,

Duan³

et al. 2020

Preprint

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“…The network consist of three networks-a market feature network, an actor network, and a critic network. The market feature network uses a long short-term memory (LSTM) (Hochreiter and Schmidhuber 1997) layer to extract features from the market price series according to previous studies (Bao et al 2017;Fischer and Krauss 2018), as well as convolutional neural network (CNN) layers to extract orderbook features that have positional information (Tashiro et al 2019;Tsantekidis et al 2017). The agent features are extracted by dense layers and the actor network outputs action probabilities while the critic network outputs predicted state values.…”

Section: Network Architecturementioning

confidence: 99%

Deep Reinforcement Learning in Agent Based Financial Market Simulation

Maeda

deGraw²,

Kitano

et al. 2020

JRFM

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Prediction of financial market data with deep learning models has achieved some level of recent success. However, historical financial data suffer from an unknowable state space, limited observations, and the inability to model the impact of your own actions on the market can often be prohibitive when trying to find investment strategies using deep reinforcement learning. One way to overcome these limitations is to augment real market data with agent based artificial market simulation. Artificial market simulations designed to reproduce realistic market features may be used to create unobserved market states, to model the impact of your own investment actions on the market itself, and train models with as much data as necessary. In this study we propose a framework for training deep reinforcement learning models in agent based artificial price-order-book simulations that yield non-trivial policies under diverse conditions with market impact. Our simulations confirm that the proposed deep reinforcement learning model with unique task-specific reward function was able to learn a robust investment strategy with an attractive risk-return profile.

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“…In their work, the authors processed Flex Full order data on a millisecond time scale with a recurrent neural network known as long short-term memory (Hochreiter and Schmidhuber 1997). Further, the author recently extended this method (Tashiro et al 2019) using a convolution neural network (Krizhevsky et al 2012). Moreover, Nanex (2010) mined and reported some distinguishing ordering patterns from order data.…”

Section: Related Workmentioning

confidence: 99%

Impact Analysis of Financial Regulation on Multi-Asset Markets Using Artificial Market Simulations

Hirano

Izumi

Shimada

et al. 2020

JRFM

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In this study, we assessed the impact of capital adequacy ratio (CAR) regulation in the Basel regulatory framework. This regulation was established to make the banking network robust. However, a previous work argued that CAR regulation has a destabilization effect on financial markets. To assess impacts such as destabilizing effects, we conducted simulations of an artificial market, one of the computer simulations imitating real financial markets. In the simulation, we proposed and used a new model with continuous double auction markets, stylized trading agents, and two kinds of portfolio trading agents. Both portfolio trading agents had trading strategies incorporating Markowitz’s portfolio optimization. Additionally, one type of portfolio trading agent was under regulation. From the simulations, we found that portfolio optimization as each trader’s strategy stabilizes markets, and CAR regulation destabilizes markets in various aspects. These results show that CAR regulation can have negative effects on asset markets. As future work, we should confirm these effects empirically and consider how to balance between both positive and negative aspects of CAR regulation.

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Encoding of high-frequency order information and prediction of short-term stock price by deep learning

Cited by 35 publications

References 7 publications

Data Science in Economics

Data Science in Economics

Deep Reinforcement Learning in Agent Based Financial Market Simulation

Impact Analysis of Financial Regulation on Multi-Asset Markets Using Artificial Market Simulations

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