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Electricity procurement of industrial consumers is becoming more and more complicated, involving a combination of various procurement methods due to electricity liberalization and decarbonization trends. This study analyzed and improved power ...

The general personality traits, notably the Big-Five personality traits, have been increasingly integrated into recommendation systems. The personality-aware recommendations, which incorporate human personality into recommendation systems, have ...

This paper proposes a method to extract financial causal knowledge from bi-lingual text data. Domain-specific causal knowledge plays an important role in human intellectual activities, especially expert decision making. Especially, in the ...

This study demonstrates whether financial text is useful for the tactical asset allocation method using stocks. This can be achieved using natural language processing to create polarity indexes in financial news. We perform clustering of the ...

This study proposes a new efficient parameter tuning method for multi-agent simulation (MAS) using deep reinforcement learning. MAS is currently a useful tool for social sciences, but is hard to realize realistic simulations due to its ...

With the development of online platforms, people can share and obtain opinions quickly. It also makes individuals' preferences change dynamically and rapidly because they may change their minds when getting convincing opinions from other users. Unlike ...

The personalized stock recommendation is a task to recommend suitable stocks for each investor. The personalized recommendations are valuable, especially in investment decision making as the objective of building a portfolio varies by each retail ...

The application of natural language processing (NLP) to financial fields is advancing with an increase in the number of available financial documents. Transformer-based models such as Bidirectional Encoder Representations from ...

This study analyzed various electricity procurement scenarios for a factory in terms of carbon neutrality, using multi-agent simulations. We performed a multi-agent simulation with power-consuming and power-generating agents to simulate the ...${}_{\mathrm{}}$

This study analyzed the effect of order simultaneity in financial markets on data mining tasks, using multi-agent simulations. In financial markets, multiple orders are submitted almost simultaneously or within very quick succession; such orders ...

Transaction data owned by financial institutions can be alternative source of information to comprehend real-time corporate activities. Such transaction data can be applied to predict macroeconomic indicators as well as to sophisticate credit ...

In this study, we demonstrate whether analysts’ sentiment toward individual stocks is useful for stock market analysis. This can be achieved by creating a polarity index in analyst reports using natural language processing. In this study, we ...

This study proposes a new scheme for implementing actual data into artificial market simulations at the level of trader agents. Because humans can introduce bias or overlook the important features of actual traders, we implemented the actual data and ...

In this paper, we propose a novel approach to capture inter-company relationships from banking transaction data using graph neural networks with a special attention mechanism and textual industry or sector information. Transaction data owned by ...

This study proposes a new model to reverse engineer and predict traders’ behavior for the financial market. This trial is essential to build a more reliable simulation because the reliability of models is a fundamental issue in the increasing use ...

In this paper, we address the problem of unintentional price collusion, which happens due to auto pricing, such as systems using reinforcement learning. Firstly, Q-learning, sarsa, and deep Q-Learning models were used for auto pricing to test ...

In this paper, we propose a new scheme for implementing the machine-learned trader-agent model in financial market simulations based on real data. The implementation is only focused on the high-frequency-trader market-making (HFT-MM) strategy. We ...

Electric power sharing among households based on the bidding method is studied as a future service. In order to verify the feasibility of such a service, a new multi-agent simulation model has been designed. We validated this model through some ...

The risk on which the whole economic system is exposed is called “systemic risk”, a topic that has received a great of attention from researchers, recently. As one of the studies, we studied effcts of the interaction between banks and firms ...

This study aims to visualize financial documents to swiftly obtain market sentiment information from these documents and determine the reason for which sentiment decisions are made. This type of visualization is considered helpful for nonexperts ...