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.

While exchanges and regulators are able to observe and analyze the individual behavior of financial market participants through access to labeled data, this information is not accessible by other market participants nor by the general public. A key question, then, is whether it is possible to model individual market participants’ behaviors through observation of publicly available unlabeled market data alone. Several methods have been suggested in the literature using classification methods based on summary trading statistics, as well as using inverse reinforcement learning methods to infer the reward function underlying trader behavior. Our primary contribution is to propose an alternative neural network based multi-modal imitation learning model which performs latent segmentation of stock trading strategies. As a result that the segmentation in the latent space is optimized according to individual reward functions underlying the order submission behaviors across each segment, our results provide interpretable classifications and accurate predictions that outperform other methods in major classification indicators as verified on historical orderbook data from January 2018 to August 2019 obtained from the Tokyo Stock Exchange. By further analyzing the behavior of various trader segments, we confirmed that our proposed segments behaves in line with real-market investor sentiments.

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In financial market forecasting, various methods based on statistical analysis and neural networks have been proposed. Accurate forecasting of future market states can be helpful in decision-making related to investment behavior; however, existing forecasting methods have considerable deficiencies due to the nature of financial markets and their complexity, influenceability, and nonstationarity. Forecasting of complex systems, such as financial markets, should be performed considering predictive uncertainty, and decision-making needs to be adjusted accordingly. In the present study, we introduce the concept of uncertainty to neural network-based financial market forecasting. A sparse variational dropout Bayesian neural network (SVDBNNs) is used for stochastic prediction, and on this basis, the corresponding decision-making process is proposed. The proposed method is validated by conducting investment simulation on the historical orderbook data from the Tokyo Stock Exchange and is confirmed to enable more efficient and safe investments compared with the considered alternative approaches.

Grammatical error correction (GEC) is commonly referred to as a machine translation task that converts an ungrammatical sentence to a grammatical sentence. This task requires a large amount of parallel data consisting of pairs of ungrammatical and grammatical sentences. However, for the Japanese GEC task, only a limited number of large-scale parallel data are available. Therefore, data augmentation (DA), which generates pseudo-parallel data, is being actively researched. Many previous studies have focused on generating ungrammatical sentences rather than grammatical sentences. To tackle this problem, this study proposes the BERT-DA algorithm, which is a DA algorithm that generates correct sentences using a pre-trained BERT model. In our experiments, we focused on two factors: the source data and the amount of data generated. Considering these elements proved to be more effective for BERT-DA. Based on the evaluation results of multiple domains, the BERT-DA model outperformed the existing system in terms of the Max Match and GLEU + .