Published papers

組織におけるビジネス構造の転換を伴う経営意思決定を形式的かつ比較可能に表現するモデルとして，「経営意思決定記述モデル」(MDDM)が提案されている．当該モデルは，実際のビジネスケースやエージェントモデルのシミュレーション・ログを一定の共通形式で表現できる可能性が指摘されている．本稿では，企業組織の外部環境に対する認識の伝播を扱った実際のビジネスケースをMDDMを用いて記述した．更に，エージェント・ベースの組織シミュレーションモデルを構築し，シミュレーション・ログから生成された仮想ケースを同様に記述した．その上で，リアルなケースとバーチャルなケースの双方が相互比較可能であることを示した．

This paper analyses the influence text information gives to the credit market, focusing on Headline News, a source of information that has immediate influence to the money market, and also which is regarded as one of the important information when making investment decisions. Text automatic classification algorithm has been applied for analysis.After classifying the headline news into several types using this algorithm,verification were made whether there were any characteristic changes in the credit market.

While the importance of financial education increases in recent years, the technique for deepening an understanding of finance theory is needed. In this research, we analyize learning method of the finance theory about the investment project selection and capital structure determination using the business game technique. As a result of analysis, the participant understood the investment project selection method and interesting phenomena – an understanding progresses about the method of determining the capital structure which raises capital stock value – were seen. These results show the effectiveness of the business game technique to study of finance theory.

This article analyzes the impacts of mergers and acquisitions (M&A) on financial markets. As a result of intensive analysis, we have concluded that (1) dynamics of credit spread of companies which purchase another company are different depending on means for the purchase (cash or stock), (2) the differences become larger when financial markets are unstable, (3) problems of wealth transfer could arise under certain conditions, (4) differences in takeover premium could contribute to differences in dynamics of credit spread and stock prices. These results are significant from both practical and academic points of view.

This study constructs a text analysis system for analyzing financial markets. This system enables us to investigate the influence of news article on intraday price changes. In this study, we examine the automobile companies in Japan to analyze the relationship between news articles and stock price reactions. As a result of empirical analyses, we confirmed that stock prices reflect news information in a timely manner. These results are suggestive from both academic and practical view points. More detailed analyses are planned for the future.

In recent years, the labor share has been declining in Japan, causing a hollowing out of the realeconomy. The purpose of this study is to clarify the relationship between labor shares and payout ratios. Weplan to examine whether the relationship between the labor share and payout ratio can be demonstrated byusing nonlinear analysis such as machine learning methods.

In recent years, the labor share has been declining in Japan, causing a hollowing out of the realeconomy. The purpose of this study is to clarify the relationship between labor shares and payout ratios. Weplan to examine whether the relationship between the labor share and payout ratio can be demonstrated byusing nonlinear analysis such as machine learning methods.

投資家は取引を行う過程で執行コストを負担する.投資家は発注量や発注のタイミングを変えることによって，間接的なコストを最小化するような執行戦略を考える必要がある.しかし，現実の市場データに基づく数理的な手法では困難である.そこで，本研究においては， 人工市場シミュレータU-Martを用いて最良執行戦略の探索を行う．

Company executives play an important role in corporate activities. In this research, we focus onthe characteristics of the top executive of each company, who make the important decisions. We attemptto measure the facial emotional score of these executives from the photographs published on their firm'sannual report. In addition, we create a narcissism index from the number of times these executives appearon their reports. Finally, we analyze how these emotional scores or narcissism indexes relate with theircompany's performance and behavior.

News articles have great impacts on asset prices in the financial markets. Many attempts havebeen reported to ascertain how news influences stock prices. However, the limitations in the number ofavailable data sets usually become the hurdle for the model accuracy. In this study, we propose a newsevaluation model utilizing GPT-2. A news evaluation model is a model that evaluates news articlesdistributed to financial markets based on price fluctuation rates and predicts fluctuations in stock prices.Reuter's news texts are classified based on each return through LSTM models. Using co-occurrencenetwork analysis, we reviewed the overview of the news articles retrieved. News articles generated by GPT-2 was used with original news articles, and the model accuracy was examined. The results showed thatcreated news articles are influential over the prediction of stock price fluctuation.

This study analyzes the influence of indexing strategy on investors' behavior and financial markets through agent-based modeling. In this analysis, I focus on smart beta index, which is proposed as a new stock index and condsidered to have better characteristics than traditional market capitalization-weighted indices. As a result of intensive computational simulation studies, we have concluded that a smart beta strategy is effective even in cases where the initial number of smart beta investors is small. This study also finds a significant relationship between the number of smart beta investors and trading volume. These results are significant from both practical and academic viewpoints.

Stock indices play a significant role in asset management business. Price indices are popular in practical business. However, recent empirical analyses suggest that a smart beta, which is proposed as a new stock index, could achieve a positive excess return. With these arguments in mind, this study analyzes the effectiveness of smart beta through agent-based modeling. As a result of intensive experiments in the market, I made the following finding that effectiveness of smart beta could be influenced by the extent of a diversity of investors' behavior. These results are significant from both practical and academic viewpoints.

Studies on financial crisis have focused on "systemic risk", whereby the collapse of one or more financial institutions has a ripple effect on the financial system itself. To prevent the occurrence or spread of such risks, a variety of discussions and studies have examined the role of central banks. This study deals with a case where central banks play a critical backup role in interbank markets. To realize the study aims, the authors develop an agent-based simulation platform and analyze the influences of central bank financing on operative reaction collapses of financial institutions. The main results are as follows: First, their financing may prevent operative reaction collapses by easing cash-flow situations. Additionally, there exist other risks such as financial institutions becoming excessively dependent for funding on the central bank, and unhealthy institutions remaining alive when they deserve to fail.

© Springer International Publishing AG, part of Springer Nature 2019. This study focuses on the influence of financial institutions’ behavior under financial regulation on financial systems. For this purpose, the authors propose simulation models of systemic risks expressing financial regulation and the financing and investment behavior of financial institutions. Using this model, scenario analysis is performed based on cases generated from a combination of various regulations. We then approach the issue of the trade-off between the stability of the financial system and a decline in liquidity. Our numerical experiment shows that the liquidity of marketable assets could be reduced by imposing a BS restriction.

News articles have great impacts on asset prices in the financial markets. Many attempts have been reported to ascertain how news influences stock prices. Stock price fluctuations of highly influential companies can have a major impact on the economy as a whole. In particular, the automobile industry is a colossal industry that leads the Japanese industry. However, the limitations in the number of available data sets usually become the hurdle for the model accuracy. In this study, we constructed a news evaluation model utilizing GPT-2. A news evaluation model is a model that evaluates news articles distributed to financial markets based on price fluctuation rates and predicts fluctuations in stock prices. We have added news articles generated by GPT-2 as data for analysis. Besides, we used a co-occurrence network analysis to review the overview of the news articles. News articles were classified through Long Short-Term Memory (LSTM). The results showed that the accuracy of the news evaluation model improved by generating news articles using a language generation model through GPT-2. More detailed analyses are planned for the future.