Masanori Hirano

Masanori Hirano
The University of Tokyo | Todai · Department of Systems Innovation

Master of Engineering

About

33
Publications
3,627
Reads
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31
Citations
Introduction
Masanori Hirano currently belongs to Izumi Lab., Department of Systems Innovation, School of Engineering, The University of Tokyo. Masanori does research in Social Simulation, Text Mining, Data Mining, Financial Market, and Artificial Intelligence.
Additional affiliations
September 2020 - present
The University of Tokyo
Position
  • PhD Student
August 2019 - present
The University of Tokyo
Position
  • Technical Assistant
Education
September 2020 - September 2023
The University of Tokyo
Field of study
  • Department of Systems Innovation, School of Engineering
February 2020 - March 2020
University College London
Field of study
  • Computer science
April 2019 - September 2020
The University of Tokyo
Field of study
  • Department of Systems Innovation, School of Engineering

Publications

Publications (33)
Conference Paper
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...
Preprint
Full-text available
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...
Conference Paper
Full-text available
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...
Conference Paper
Full-text available
Deep Hedging, which uses deep learning and price time-series simulations to optimize option hedging, has recently been in the spotlight because it enables more realistic hedging that can take into account frictions such as transaction fees (imperfect market). However, the situation of hedging an option by other options has never been addressed by d...
Conference Paper
Full-text available
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.
Conference Paper
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...
Conference Paper
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...
Article
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...
Conference Paper
Full-text available
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.
Conference Paper
Full-text available
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.
Conference Paper
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...
Chapter
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...
Conference Paper
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...
Conference Paper
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...
Chapter
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...
Chapter
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...
Preprint
Full-text available
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...
Article
Full-text available
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...
Conference Paper
Full-text available
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...
Article
Full-text available
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 s...
Conference Paper
Full-text available
We propose a scheme for selecting stocks related to a theme. This scheme was designed to support fund managers who are building themed mutual funds. Our scheme is a type of natural language processing method and based on words extracted according to their similarity to a theme using word2vec and our unique similarity based on co-occurrence in compa...
Preprint
Full-text available
In this paper, we compare data of the high-frequency-trader market-making (HFT-MM) strategy from both the real financial market and our simulation. For the former, we extracted trader clusters and identified one cluster whose statistical indexes indicated HFT-MM features. Subsequently, we analyzed the difference between these traders' orders and th...
Conference Paper
This paper proposes a pointwise prediction for a sentence boundary detection task. The proposed pointwise prediction is combined with our original word embedding method and three-layered perceptron. It predicts whether the targeted words have the role of the beginning/end of a sentence or not by using word features around the targeted words. We tes...
Conference Paper
本研究は,金融市場における高頻度取引(HFT)のマーケットメイク(MM)戦略と呼ばれる注文行動について分析を行うことを目的とした.株式会社日本取引所グループより提供を受けた,東京証券取引所の注文データを使用し,仮想サーバーの名寄せを前処理として行なった.その結果得られた,取引主体別の注文データを,いくつか指標を使うことで,クラスター分析を行い,高頻度マーケットメイク戦略(HFT-MM)を取っている取引主体を抽出し,それらの注文が,直近約定価格から何ティック離れたところに置かれているかについて計算した.その結果,HFT-MMとされる行動主体は,直近約定価格からかなり離れた位置(5-10ティック)のところにも注文を置いていることが明らかになった.この結果は,HFT-MMとされる取引主体が,マーケ...
Preprint
Full-text available
This paper proposes a pointwise prediction for a sentence boundary detection task. The proposed pointwise prediction is combined with our original word embedding method and three-layered perceptron. It predicts whether the targeted words have the role of the beginning/end of a sentence or not by using word features around the targeted words. We tes...
Conference Paper
Full-text available
本研究において,文書内における単語の共起を利用した上位下位概念の推定の手法を提案した.本手法に基づき,Wikipediaの記事を文書として使い,Wordnetに含まれる上位下位概念をデータとして実験を行った. その結果,精度は低いものの,一定の有効性を確認することができた.本手法は非常に少ない訓練データで必要なパラメータのチューニングが可能であることもわかった.
Article
Full-text available
We propose an extended scheme for selecting related stocks for themed mutual funds. This scheme was designed to support fund managers who are building themed mutual funds. In our preliminary experiments, building a themed mutual fund was found to be quite difficult. Our scheme is a type of natural language processing method and based on words extra...
Conference Paper
We propose a method to select and rank stocks related to a given theme. The proposed method has two flows; obtaining related words, and selecting related stocks based on obtained related words. First, on the basis of the given theme word, the proposed method selects words with high similarity using an ensemble of word2vec models. Then, we modify th...
Conference Paper
In this study, we assessed the impact of capital adequacy ratio (CAR) regulation like that in the Basel regulatory framework. To asses impact such as destabilizing effects (e.g., whether CAR regulation destabilizes markets or not), we conducted simulations of an artificial market, one of the computer simulations imitating real financial markets. An...
Conference Paper
Basel regulatory framework, one of CAR (capital adequacy ratio) regulations, is said to make markets destabilized in a previous study. But the previous study included some inappropriate assumptions. So, this study assessed this destabilizing effects with a new model. In my model, FCN agents and 2 kinds of portfolio agents, CAR regulated ones and no...

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