Kiyoshi Izumi

The University of Tokyo | Todai · Department of Systems Innovation

With the increasing trading volume in the Japanese CFD (Contract for Difference) market, the strategy of market making in CFDs has become a significant issue. Unlike other quote-driven markets where market makers face the dilemma of maximizing profits and managing inventory risk, CFD market makers can hedge their risks using the underlying market....

In the post-Turing era, evaluating large language models (LLMs) involves assessing generated text based on readers' reactions rather than merely its indistinguishability from human-produced content. This paper explores how LLM-generated text impacts readers' decisions, focusing on both amateur and expert audiences. Our findings indicate that GPT-4...

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 procurement strategies for a factory to achieve carbon neutralization through a multi-agent model simulatin...

Large Language Models (LLMs) have demonstrated exceptional capabilities across various machine learning (ML) tasks. Given the high costs of creating annotated datasets for supervised learning, LLMs offer a valuable alternative by enabling effective few-shot in-context learning. However, these models can produce hallucinations, particularly in domai...

Policymakers, business managers, and recruitment professionals must be aware of labor market trends to aid their decision-making. For this purpose, a range of official statistics is provided in many countries, offering insights into unemployment rates, workforce numbers, average wages, and so on, typically with a lag of two to three months. However...

This study demonstrates whether financial text is useful for tactical asset allocation using stocks by using natural language processing to create polarity indexes in financial news. In this study, we performed clustering of the created polarity indexes using the change-point detection algorithm. In addition, we constructed a stock portfolio and re...

We introduce a novel generative adversarial network (GAN) designed to generate realistic trading orders for financial markets. Past models of GANs for creating synthesized trading orders have always been focused on continuous spaces because their architecture has constraints coming from the learning algorithm. Contrary to this, actual orders are pl...

Stock embedding is a method for vector representation of stocks. There is a growing demand for vector representations of stock, i.e., stock embedding, in wealth management sectors, and the method has been applied to various tasks such as stock price prediction, portfolio optimization, and similar fund identifications. Stock embeddings have the adva...

The gravity models has been studied to analyze interaction between two objects such as trade amount between a pair of countries, human migration between a pair of countries and traffic flow between two cities. Particularly in the international trade, predicting trade amount is instrumental to industry and government in business decision making and...

In this paper, we attempt to summarize monthly reports as investment reports. Fund managers have a wide range of tasks, one of which is the preparation of investment reports. In addition to preparing monthly reports on fund management, fund managers prepare management reports that summarize these monthly reports every six months or once a year. The...

What would happen if temperatures were subdued and result in a cool summer? One can easily imagine that air conditioner, ice cream or beer sales would be suppressed as a result of this. Less obvious is that agricultural shipments might be delayed, or that sound proofing material sales might decrease. The ability to extract such causal knowledge is...

Firm default prediction is important in credit risk management and understanding economic trends. Both practitioners and academic researchers have long studied it. While traditional statistical methods such as discriminant analysis and logistic regression have been used recently, machine learning and deep learning methods have been widely applied....

Financial documents are increasing year by year, and natural language processing (NLP) techniques are widely applied to process these documents. Specifically, Transformer-based pre-trained models such as BERT have been successful in NLP in recent years. These cutting-edge models have been adapted to the financial domain by pre-training with financi...

Transactions in Contract for Difference (CFD) markets are increasing. However, because of the limited information and data, it is difficult to grasp the characteristics of the market as a whole. In Japanese CFD markets, the market maker system has been adopted. In market maker system, brokers called market makers act as intermediaries. Specifically...

Since the emergence of the COVID-19 pandemic, disruptions in supply chains have significantly impacted both the global economy and asset markets. Despite a rising interest in supply-related data among policymakers, researchers, and financial market participants, existing indicators often wrestle with pervasive issues of low frequency and coarse gra...

Recently, Large Language Models (LLMs) have attracted significant attention for their exceptional performance across a broad range of tasks, particularly in text analysis. However, the finance sector presents a distinct challenge due to its dependence on time-series data for complex forecasting tasks. In this study, we introduce a novel framework c...

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 shown promising results in general recommendation areas including music, movie, and e-commerce recommenda...

With electricity liberalization and decarbonization becoming increasingly popular, electricity procurement for industrial consumers becomes more and more complicated. This study analyzed power procurement strategies for a factory to achieve carbon neutrality. For the analysis, a multi-agent simulation for an electric power market was constructed. T...

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 financial area, fund managers, financial analysts, etc. need causal knowledge for their works. Natural language...

Personalized stock recommendations aim to suggest stocks tailored to individual investor needs, significantly aiding the financial decision making of an investor. This study shows the advantages of incorporating context into personalized stock recommendation systems. We embed item contextual information such as technical indicators, fundamental fac...

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 created polarity indexes using the change point detection algorithm. In addition, we construct a stock portf...

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 computational burden for parameter tuning. This study proposes efficient parameter tuning to address this issue us...

本稿では，新しい人工市場シミュレーションプラットフォームのPAMS: Platform for Artificial Market Simulationsを示す． PAMSは，深層学習技術などとのシームレスな融合を前提におき，Pythonベースのアーキテクチャを採用しつつ，様々なシミュレーションが可能になるように，ユーザーが簡便にエージェントや環境をカスタマイズ可能になっている． 実際に，使用例として，本稿では，深層学習による価格予測を行うエージェントを用いた研究を行い，PAMSの有効性について示す．

This study proposes a new generative adversar-ial network (GAN) for generating realistic orders in financial markets. In some previous works, GANs for financial markets generated fake orders in continuous spaces because of GAN architectures' learning limitations. However, in reality, the orders are discrete. For example, the orders have minimum ord...

Multi-agent simulations are useful in social sciences but they encounter an evaluation difficulty in that many social phenomena are qualitative, and it is difficult to evaluate quantitatively the realness of simulations. Therefore, we propose a new quantitative evaluation method for multi-agent simulation in social sciences using a generative adver...

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 power procurement strategies for a factory to achieve carbon neutralization through a multi-agent model simulating the electr...

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 calculated anomaly scores for the created polarity index using anomaly detection algorithms. The results sho...

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 management, customer relationship management, and etc. However, it needs attention when a financial institu...

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 power procurement strategies for a factory to achieve carbon neutralization through a multi-agent model simulating the elect...

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 Transformers (BERT) have been successful in NLP in recent years. These cutting-edge models have been adapted to the financi...

This study proposes a reinforcement-learning-based method for efficient parameter tuning in multi-agent simulations (MAS). Usually, MAS has a high computational burden because of the agents involved; thus, it is important to tune its parameters efficiently. Our proposed method is centered around actor-critic-based reinforcement learning methods, su...

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 electric power market. Additionally , we developed a factory model reflecting the actual electricity consumpt...

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 are thought of as independent of one another. We call this phenomenon order simultaneity. If order simul...

In this research, we propose a method of using data on CO2 concentrations based on observational data from the Greenhouse Gases Observing Satellite (GOSAT, also known as Ibuki) to arrive at an accurate picture of macroeconomic activity with minimal lag. In doing so, we build on our conventional approach of using fast-breaking consumption-related in...

This study proposes a new scheme for implementing actual data into artificial market simulations at the level of trader agents. Traditionally, agent design is performed by humans, so the reliability of trader agents depends on the sense of the model designer. Because humans can introduce bias or overlook the important features of actual traders, we...

This article proposes a methodology to forecast the movements of analysts' estimated net income and stock prices using analyst profiles. Our methodology is based on applying natural language processing and neural networks in the context of analyst reports. First, we apply the proposed method to extract opinion sentences from the analyst report whil...

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 automated the strategy learning (imitating) of agents using machine learning (ML). We then ran arti...

This study demonstrates whether analysts' sentiments toward individual stocks are useful for stock investment strategies. This is achieved by using natural language processing to create a polarity index from textual information in analyst reports. In this study, we performed time series forecasting for the created polarity index using deep learning...

This study proposes a new generative adversarial network (GAN) for generating realistic orders in financial markets. In some previous works, GANs for financial markets generated fake orders in continuous spaces because of GAN architectures' learning limitations. However, in reality, the orders are discrete, such as order prices, which has minimum o...

In addition to the need for electricity consumers to take into account the complex behavior of the electricity market as a result of electricity deregulation, CO2 emissions from economic activities have also become an issue in response to recent calls for decarbonization, making the electricity procurement environment increasingly complex. In this...

本研究では決算短信や有価証券報告書を用い，言語モデルの BERT と ELECTRA につ いて，事前学習や追加で事前学習 (追加事前学習) を行いモデルを構築する．構築したモデルについて，金融ドメインのタスクによって汎用コーパスを用いたモデルとの性能を比較する．その際，ファインチューニングを行う層の数などパラメーターによる性能についても比較を行う．構築した一部のモデルについては一般に公開する．

Electricity procurement by electricity consumers is becoming more and more complicated due to the trend of electricity liberalization and decarbonization. In this study, we evaluate the cost of carbon neutrality in electricity procurement for a factory as a large electricity consumer using an electricity market multi-agent model and a factory elect...

This study demonstrates whether analysts' sentiment toward individual stocks is useful in predicting the macroeconomic index. This can be achieved by using natural language processing to create polarity indexes from analyst reports. In this study, the created polarity indexes were analyzed using the Vector Autoregressive model with various macroeco...

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 of simulations. Thus, we tried to build a behavior model of financial traders through the traders’ futu...

BERT を始めとする事前学習言語モデルは，様々な自然言語処理のタスクにおいて成果を上げている．これらのモデルの多くは Wikipedia やニュース記事などの一般的なコーパスを用いているため，専門的な単語が使用される金融分野においては十分な効果が得られない．本研究では決算短信や有価証券報告書から事前学習言語モデルを構築する．また金融ドメインのタスクによって汎用モデルとの性能を比較する．

本研究では, アナリストの個別銘柄に対するセンチメントが, マクロ経済指標の予測に役 立つかを実証する. これはアナリストレポートのテキスト情報を自然言語処理を使用して極性指標を作成することで実現可能となる. 本研究では, 作成した極性指標に対し, 各種マクロ経済指標を使用し,VAR モデルを用いた分析を行った. 結果, 極性指標から物価, 為替, 国債等の指標へのグレンジャー因果性があることが確認された. これにより, 極性指標が先行しており, マクロ経済指標の予測に役立つことが示唆された.

Recently, general-purpose language models pre-trained on large corpora such as BERT have been widely used. In Japanese, several pre-trained models based on Wikipedia have been published. On the other hand, general-purpose models may not be sufficiently effective in the financial domain because of the use of specialized phrases. In this study, we co...

This is an extension from a selected paper from JSAI2020. In this study, we propose a stochastic model for predicting the behavior of financial market traders. First, using real ordering data that includes traders’ information, we cluster the traders and select a recognizable cluster that appears to employ a high-frequency traders’ market-making (H...

Recognizing and segmenting surgical workflow is important for assessing surgical skills as well as hospital effectiveness, and plays a crucial role in maintaining and improving surgical and healthcare systems. Most evidence supporting this remains signal-, video-, and/or image-based. Furthermore, casual evidence of the interaction between surgical...

This paper proposes a new model to reverse engineer and predict traders' behaviors for financial market. In this model, we used an architecture based on the transformer and residual block, and a loss function based on Kullback-Leibler divergence. In addition, we established a new evaluation metric, and consequently, succeeded in constructing a mode...

電力の自由化に伴い，工場などの大口の電力需要を持つ事業者は，電力調達の際に市場価格や需給の変動など不確定要素を新たに考慮する必要が出てきた．本研究では，価格高騰時のコスト削減や需給調節などのために需要側がとりうる手段の1つであるデマンドレスポンス（DR）について，電力市場におけるその効果を分析した．具体的にはまず，標準的な工場の用途別電力消費量時系列データから，主成分分析により特性を抽出し，これを基に工場の用途別電力消費モデルを構築した．次に，この工場エージェントに加え電力供給エージェントと需要エージェントが参加する，JEPX（日本卸電力取引市場）の1日前市場を模したマルチエージェントモデルを用いて，シミュレーション実験を行った．実験では，工場のDRシナリオについてそれぞれ市場に対するスケー...

We use a sparse variational dropout Bayesian neural network (SVDBNNs) to propose an investment strategy that gives consideration to predictive uncertainty. The proposed method is validated through simulation on historical orderbook data from the Tokyo Stock Exchange. Our results were found to outperform other standard non-Bayesian approaches on ris...

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 first extract order data of HFT-MM traders from the real order data by clustering. Then, using the data...

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 whether they cause collusion. To test them, we performed multi-agent simulations of a competitive market wi...

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 evaluations. For example, it is confirmed that the market price on this service stably changes according...

In this paper, we address the problem of unintentional price collusion, which happens due to auto pricing, such as systems using reinforcement learning. Previous work has pointed out the risk of unintentional collusion caused by auto pricing using Q-learning, one of the basic models of reinforcement learning, using a market simulation. This study e...

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...

In this research, we extract causal information from textual data and construct a causality database in the economic field. We develop a method to produce causal chains starting from phrases representing specific events. The proposed method can offer possible ripple effects and factors of particular events or situations. Using our approach to Japan...

The relation between the number of passengers in the main stations and the infection rate of COVID19 in Tokyo is empirically studied. Our analysis based on conventional compartment model suggests: 1) Average time from the true day of infection to the day the infections are reported is about $15$ days. 2) The scaling relation between the density of...

Recently financial markets have shown significant risks and levels of volatility. Understanding the sources of these risks require simulation models capable of representing adequately the real mechanisms of markets. In this paper, we compared data of the high-frequency-trader market-making (HFT-MM) strategy from both the real financial market and o...

In this study, we propose a stochastic model for predicting the behavior of financial market traders. First, using real ordering data that includes traders' information, we cluster the traders and select a recognizable cluster that appears to employ a high-frequency traders' market-making (HFT-MM) strategy. Then, we use an LSTM-based stochastic pre...

In this paper, we propose a methodology of forecasting the change rate of net income which an analyst estimates by applying natural language processing and neural networks in the context of analyst reports. We examine the contents of the reports for useful information on forecasting the direction of revision in analyst estimate earnings. First, our...

There have been many studies seeking to predict excess returns in financial time series data. Nevertheless, not many studies have focused on applying machine learning approaches among factors in different asset classes. The main objective of this paper is to analyze whether a predictability of return in financial products could be improved by consi...