Authorship：Last author   Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

DOI： 10.11517/jsaisigtwo.2025.fin-034_152

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

DOI： 10.11517/jsaisigtwo.2025.fin-034_90

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1007/978-3-031-77367-9_9

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>本研究では、企業のウェブサイト上のESG情報の効率的な収集手法を提案する。ESG情報は投資判断や企業評価において極めて重要な情報であるが、ESG情報自体が多岐に渡り、企業のHPや統合報告書等、多くの場所に分散している。そのため、ESG情報の収集には多くの時間が必要になるとともに、包括的な収集が困難であるという課題が存在する。そこで、本研究では機械学習を活用し、当該課題に対処するための次の二つの問題に取り組む。まず、企業HPのURLからESG情報の存在の有無を二値分類する問題に取り組む。次に、ESG情報が存在すると判定されたウェブサイト本文から、実際にESG情報が含まれるかを判定する問題に取り組む。前者の二値分類問題においてはF1スコア0.924を達成し、後者の本文判定問題においてはF1スコア0.986を達成した。これらの結果は、機械学習を用いることでESG情報の効率的かつ広範な取得が可能であることを示している。</p>

DOI： 10.11517/jsaisigtwo.2024.fin-033_33

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>Text classification using large language models (LLMs) often has low interpretability and is hard to adjust manually. On the other hand, zero-shot learning, which uses definition statements in LLMs for classification, is more interpretable but creating good definition statements is a challenging task. Therfore, we propose a method to automatically generate definition statements using LLMs to improve classification accuracy and interpretability in (binary) text classification. The proposed method first randomly splits the labeled data and generates (initial) definition statements based on sampled data. Then, it classifies the labeled data using these statements and updates them by inputting misclassified data back into LLMs, repeating this process to improve the definition statements. Experiments with real-world texts show that the proposed method performs well compared to fine-tuned BERT model and LLM few-shot learning and creates appropriate definition sentences.</p>

DOI： 10.11517/jsaisigtwo.2024.fin-033_155

Authorship：Last author   Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>本研究では、Chen et.al (2022) の手法に基づいて、日本企業の財務データを用いて、機械学習手法による次期利益の変化方向の予測可能性を検証する。先行研究では、米国市場において機械学習手法がアナリスト予測を上回る予測パフォーマンスを示しているが、本研究ではこの知見の日本市場への適用可能性を探る。本研究では、先行研究同様に機械学習手法として非線形性および相互作用を捉えることのできる決定木系のアルゴリズムを適用し、予測モデルを構築する。予測パフォーマンスの評価には、ROC曲線化面積と、予測に基づくヘッジ・ポートフォリオの異常リターンを用いる。また、ロジスティック回帰モデルや経営者予想との比較を行い、機械学習手法の優位性を検証する。本研究の貢献は、(1)機械学習手法が捉える利益予測における財務情報の非線形性・相互作用の解明、(2)日本市場での会計情報の利益に対する予測力の再評価、(3)効率的市場仮説に対する実証的証拠の提示、の3点にある。これらの知見は、会計情報の役割に関する理論的な理解を深め、日本市場の投資実務や企業価値評価に新たな視座を提供する。</p>

DOI： 10.11517/jsaisigtwo.2024.fin-033_68

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>本研究は、複雑な会計基準に対する質問応答タスクに対応するための手法を提案する。会計基準は多くの条文と参照関係を含み、その複雑さゆえに会計専門家以外には理解が困難であるという問題がある。また、従来の質問応答モデルでは、会計基準の複雑な参照関係を十分に考慮することが難しく、そのため正確な回答を生成することが困難であった。そこで本研究では、会計基準の参照関係を効果的に活用し、質問応答モデルの性能を向上させる。具体的には、会計基準の参照関係をグラフ化し、質問応答モデルに組み込む手法を提案する。収益認識に関する会計基準を使用しグラフを構築し、会計基準の設例や公認会計士試験の論文式問題を対象に提案手法を事前学習モデルやRAGモデルとの比較実験を行った。結果、会計基準グラフの利用が専門的な質問応答タスクでの性能向上に効果的であることを示した。このアプローチは、他の複雑な法規や規制にも適用可能である。</p>

DOI： 10.11517/jsaisigtwo.2024.fin-033_53

Authorship：Last author,　Corresponding author   Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>本論文では、深層学習~(DL)を用いたポートフォリオ最適化に取り組む。DLにより、従来のCAPMやファクターモデルでは扱えなかったリターンの非線形性をモデルに組み込むことが可能となった。しかし、DLを用いたポートフォリオ構築においても、リスクとリターンのトレードオフを最適化することが重要であり、このトレードオフを考慮するためにSharpe lossという損失関数が提案された。この方法は、実証的に有用であるが、Sharpe lossには理論的問題がある。まず、損益が負の値を取る場合、その解釈が難しく、非直感的なポートフォリオを構築すること、次に確率的勾配降下法を用いた際に、勾配が不偏推定量とならないことである。そこで本論文では、DLを用いたポートフォリオ最適化のためのリスク回避型効用関数を用いた新しい損失関数を提案する。リスク回避型効用関数は損益が負の場合でも解釈が容易であり、また、勾配が不偏推定量となるため、Sharpe lossが抱えていた問題を回避することができる。加えて、DLからの出力をベースライン戦略のウエイトから差分とすることで、より優れたポートフォリオが構築可能であることを示す。提案手法の有効性を確認するためにS\&P500の過去データを用いた実験を行った。提案手法がSharpe ratioを含めたいくつかの指標でSharpe lossを用いた場合よりも良いパフォーマンスが得られることを示す。</p>

DOI： 10.11517/jsaisigtwo.2024.fin-033_169

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>This study investigates the mean-variance (MV) trade- off in reinforcement learning (RL), an instance of sequential decision-making under uncertainty. Our objective is to obtain MV-efficient policies, whose means and variances are located on the Pareto efficient frontier with respect to the MV trade- off; under this condition, any increase in the expected reward would necessitate a corresponding increase in variance, and vice versa. To this end, we propose a method that trains our policy to maximize the expected quadratic utility, defined as a weighted sum of the first and second moments of the rewards obtained through our policy. We subsequently demonstrate that the maximizer indeed qualifies as an MV-efficient policy. Previous studies that employ constrained optimization to address the MV trade-off have encountered computational challenges. However, our approach is more computationally efficient as it eliminates the need for gradient estimation of variance, a contributing factor to the double sampling issue observed in existing methodologies. Through experimentation, we validate the efficacy of our approach.</p>

DOI： 10.11517/jsaisigtwo.2024.fin-033_177

Authorship：Last author   Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>本研究では、SAR(合成開口レーダー)衛星画像を用いて、テーマパークの来場者数を推定する手法を検討する。テーマパークの来場者数は、特に投資家やアナリストにとって、企業の業績や消費者需要を評価するための重要な指標である。来場者数データは、収益予測や投資判断に直結するため、リアルタイムな情報が求められているが、一般には公開されておらずデータの収集が困難である。そこで本研究は、SAR衛星画像の高解像度と全天候対応の特性を活かし、ケーススタディとして東京ディズニーリゾートの敷地内における駐車場を解析し、来場者数を推定する。また、公表されている来場者数や売上高などとの比較を行い、推定値の有効性を分析する。</p>

DOI： 10.11517/jsaisigtwo.2024.fin-033_61

Authorship：Lead author   Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1016/j.irfa.2024.103493

Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/iiai-aai63651.2024.00075

Kind of work：Joint Work  

<title>Abstract</title> <p>Quantum computers are gaining attention for their ability to solve certain problems faster than classical computers, and one example is the quantum expectation estimation algorithm that accelerates the widely-used Monte Carlo method in fields such as finance. A previous study has shown that quantum generative adversarial networks(qGANs), a quantum circuit version of generative adversarial networks(GANs), can generate the probability distribution necessary for the quantum expectation estimation algorithm in shallow quantum circuits. However, a previous study has also suggested that the convergence speed and accuracy of the generated distribution can vary greatly depending on the initial distribution of qGANs' generator. In particular, the effectiveness of using a normal distribution as the initial distribution has been claimed, but it requires a deep quantum circuit, which may lose the advantage of qGANs. Therefore, in this study, we propose a novel method for generating an initial distribution that improves the learning efficiency of qGANs. Our method uses the classical process of {\it label replacement} to generate various probability distributions in shallow quantum circuits. We demonstrate that our proposed method can generate the log-normal distribution, which is pivotal in financial engineering, as well as the triangular distribution and the bimodal distribution, more efficiently than current methods. Additionally, we show that the initial distribution proposed in our research is related to the problem of determining the initial weights for qGANs.</p>

DOI： 10.21203/rs.3.rs-4590355/v1

Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>The number of funds remains high and the amount of economic and social news information increases rapidly, increasing the burden on asset management companies in preparing disclosure documents. These disclosure documents are important for mutual fund holders, and in particular, market comments and outlooks are essential to understanding the current and future investment environment. Writing these documents takes a lot of time, adding to the workload of asset management companies. Recently, advancements in Large Language Models (LLMs) have expanded their use in various tasks. However, LLMs struggle to easily learn new information due to computational resources and costs, making it challenging to generate market comments and outlooks reflecting the latest economic data. Retrieval Augmented Generation (RAG) is a solution to this problem. In this study, we use ChatGPT, a type of LLM, to develop a tool that automatically generates market comments and outlooks. This tool can incorporate the latest news information and generate comments based on the information while suppressing hallucinations. We propose two methods: few-shot learning, which uses past market comments as examples, and zero-shot learning, which does not use past market comments. We collected actual market comments from publicly available mutual fund disclosure documents and conducted both qualitative and quantitative evaluations in comparison with the generated comments.</p>

DOI： 10.1527/tjsai.39-4_fin23-b

Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>In network analysis, traditional centrality measures are now complemented by the concept of curvature from differential geometry, which offers a refined understanding of network structures. Ricci curvature, an indicator of space distortion, is well-studied within the field of Riemannian geometry but has recently been adapted to discrete networks through the development of discrete curvature. Among the forms of discrete curvature, LLY Ricci curvature and Forman Ricci curvature are notable. LLY Ricci curvature, grounded in optimal transport theory, correlates with link density and clustering coefficients, offering a means to identify globally central nodes. Despite its effectiveness in diverse domains, including financial networks, its complex nature prevents its application to large networks due to computational constraints. On the other hand, Forman Ricci curvature, simpler and computationally more efficient due to its reliance on degree centrality, proves useful in identifying network bottlenecks but is limited by its sole dependence on network degree, constraining its applicability in constructing intricate graph algorithms. Therefore, this paper introduces a new graph algorithm that integrates both LLY and Forman Ricci curvatures, aiming to leverage their strengths while mitigating their limitations. To empirically validate the practical applicability, we applied our method to the financial market for stock screening, a critical process for investors. We conducted an empirical analysis using a well-known financial benchmark dataset to examine the performance of our proposed algorithm from a risk management perspective. The empirical results suggest that the stocks selected using our method are a robust set that maintains low correlation under normal conditions and responds effectively to events such as financial crises.</p>

DOI： 10.1527/tjsai.39-4_fin23-k

Authorship：Last author   Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>In this paper, we propose a method for automatically evaluating integrated reports in terms of interpretability using a deep learning model with hierarchical attention to two types of information: sentences and pages. Specifically, we newly constructed a dataset labeled with investor evaluation labels based on the external evaluation of integrated reports by the GPIF. In addition, we proposed a deep learning model with a Bi-LSTM layer for learning page order and two attention layers for sentences and pages. In the evaluation experiments, the proposed model achieved a classification performance of F1-score 0.847 for integrated reports of companies that do not appear during training. In the discussion, by visualizing the weights of the attention layer of the model, we confirmed that the information of interest in the model is generally consistent with the evaluation criteria of investors. In addition, we examined the practicality of the proposed method by taking into account the bias of the dataset, and showed that the proposed method is able to automatically evaluate both learned and unknown integrated reports of companies.</p>

DOI： 10.1527/tjsai.39-4_fin23-e

Authorship：Last author   Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>Deep Hedging has garnered attention as a novel approach utilizing deep learning to address challenges in pricing and hedging in incomplete financial markets．However, the effectiveness of Deep Hedging when applied to multiple options has not been thoroughly examined．Additionally, financial market data is often noisy, making it challenging to train deep learning models effectively. Therefore, in this study, we aim to address these issues by evaluating the effectiveness of Deep Hedging for multiple options using data from the Bitcoin options market．We verify its effectiveness for multiple options and assess the impact of introducing smoothing techniques. Specifically, we introduce a technique called Deep Smoothing to reduce noise and prevent arbitrage opportunities when dealing with a portfolio composed of multiple European options with the same underlying asset, the same maturity, but different strike prices．We combine this smoothing technique with the structure of the Implied Volatility Smile（IVS）to propose a new framework of Deep Hedging for multiple options. We validate our empirical results with Bitcoin options market data, demonstrating that: （1） Deep Hedging outperforms traditional delta hedging, （2） when hedging multiple options, our method achieves performance equal to or better than conventional Deep Hedging targeting a single option, and （3） the application of Deep Smoothing to the input data leads to improved hedging performance．</p>

DOI： 10.1527/tjsai.39-4_fin23-h

Authorship：Last author   Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>Financial results briefings are a bidirectional communication channel between companies and stakeholders, complementing unidirectional communication through annual reports and integrated reports, among others. Financial results briefings provide a platform for companies to promptly explain their financial performance, business status, and strategies. They typically consist of a presentation by management and a QA session, enabling participants to directly inquire about financial performance and resolve concerns. We analyze the information value of financial results briefings from both the company and investor perspectives in the Japanese market. For companies, the value lies in determining whether information disclosed during these financial results briefings reduces the cost of capital. Higher-quality information disclosure is theorized to mitigate information asymmetry among market participants and, consequently, lower the cost of capital. Therefore, we assign sentiments to text data from financial results briefings, considering the corresponding financial results. We then examine the correlation between text sentiments, text lengths, and the cost of capital. For investors, the question is whether information disclosure during these financial results briefings influences post-disclosure abnormal returns. We examine the relationship between sentiment and postdisclosure abnormal returns. As a result, from a company’s perspective, we find that the cost of capital tends to be lower when the text length in the QA session is large. This suggests that sufficient information disclosure contributes to reducing the cost of capital, and is consistent with existing empirical studies and theoretical models. From the investors’ perspective, we find that the sentiment of the QA session and the excess sentiment beyond the expected from the explanation are associated with short-term abnormal returns. This result confirms that QA is a valuable source of information for investors. We underscore the significance of financial results briefings in both company IR activities and from the perspective of investors.</p>

DOI： 10.1527/tjsai.39-4_fin23-c

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1016/j.frl.2024.105101

Authorship：Last author   Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>再帰的強化学習は価値関数を用いずに方策を更新する強化学習アルゴリズムであり、方策の更新をある目的関数の勾配に基づいて行う手法がトレーディング戦略に応用されている。しかしながら、それらは少数の具体的な目的関数に基づくものであり、柔軟な目的関数の選択が可能であるとは言えない。そこで本論文では、再帰的強化学習における目的関数を劣微分が1点集合となる凸リスク尺度と、2つの時間整合的な動的凸リスク尺度に設定し、それらの勾配の計算方法を提案する。これにより複数のリスク尺度から柔軟に目的関数を選択することが可能となり、さらにリスクの時間整合的な評価が可能となる。実証分析では、人工データおよび実際の市場データを用いて提案手法の有効性を検証する。</p>

DOI： 10.11517/jsaisigtwo.2023.fin-032_57

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>連邦公開市場委員会(FOMC)によって決定される金融政策は、多様な資産に影響を及ぼす。この研究では、FOMCの会合日の二週間前から当日にかけて、金融政策の影響が特に顕著に現れる時期を対象とし、各種資産の価格変動をモデル化する。分析には、市場、マクロ経済データと国内景況感を示すBeige Bookのテキストデータを用い、XGBoostモデルを活用して予測を行った。さらに、連邦準備制度が「物価の安定と雇用の最大化」を目的に政策を運営していることを踏まえ、これらの目標に関連するテキストデータを特徴量化し、モデルの性能向上を図った。</p>

DOI： 10.11517/jsaisigtwo.2023.fin-032_65

Authorship：Last author   Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>Sentiment analysis plays a critical role in financial text mining. However, conventional approaches typically focus on texts that directly describe positive or negative impacts on financial performance. Considering practical investment decisions, it's necessary to extract sentiment from texts that may not explicitly express sentiment, taking into account the background context. Particularly, such an inferring sentiment becomes even more important for small and mid-cap companies, which rarely receive news coverage. Therefore, in this study, we utilize the inference capabilities of a Large Language Model (LLM) to tackle an inference-based sentiment analysis task. Specifically, we input the business profile of a company as background context and infer the impact of a particular major event on the company performance, subsequently assigning sentiment. We conduct the experiments to assess the practical usefulness of the model's output.</p>

DOI： 10.11517/pjsai.jsai2024.0_4xin211

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>In recent years, the establishment of new authorized corporations and the introduction of the new NISA system have led to increased interest in financial education across various age groups. Concurrently, there is an expected rise in the use of large language models (LLMs) in services that support financial education, such as chatbots and robo-advisors. However, LLMs are often regarded as 'black boxes', raising concerns about biases in their outputs, including potential racial discrimination. Therefore, in this study we develop metrics to measure and evaluate the biases of LLMs within the context of financial education. Drawing from behavioral economics, we have developed methods to assess LLMs in terms of risk preference, time preference, and social preference. Furthermore, we evaluate various LLMs, including ChatGPT and PaLM, using these proposed metrics and present our findings.</p>

DOI： 10.11517/pjsai.jsai2024.0_3xin236

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>In this paper, we focus on the residual returns that are not explained by the common factors in financial asset returns. We propose a novel method to extract well-behaved residual returns based on principal component analysis (PCA). Traditional PCA requires determining the number of common factors, presenting a trade-off: increasing the number reduces common factors but also increases the potential for noise. Our proposed method randomly divides returns into two groups, extracts factors (PC) from one, and estimates eigenvalues from the other. Then, by creating a projection matrix that aims to transform eigenvalues to the same level, the proposed method can extract residual returns with better and more stable properties than PCA. Finally, we demonstrate that our method is capable of extracting residual returns with desirable properties through analysis based on both synthetic and real market data.</p>

DOI： 10.11517/pjsai.jsai2024.0_3d5gs205

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>Recent developments of machine learning techniques have made AI traders more prominent in financial markets, drawing attention to their market impact. We focus on the GARCH(1,1) model, a key financial time series model, to analyze the influence of AI traders. The GARCH model is the most common method for modeling conditional variance capable of replicating volatility clustering, but its micro-foundations have not yet been fully understood. We categorize market investors into noise traders, fundamental traders, and AI traders and construct the GARCH model with artificial markets using them. We explore how each group affects the GARCH model's parameters and examine the role of AI traders in market dynamics and volatility, using theoretical models and simulations.</p>

DOI： 10.11517/pjsai.jsai2024.0_3xin201

Authorship：Last author   Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>In this study, we explore the Management Discussion and Analysis (MD&A) section of Japanese Annual Securities Reports, a mandatory disclosure known for providing crucial qualitative information about management perspectives. Our research primarily utilizes the ChatGPT to extract qualitative expressions within the MD&A texts. Firstly, we compare the proportion of qualitative expressions presented in their MD&A across different companies, quantifying the extent of qualitative information. Our hypothesis that a higher prevalence of qualitative information may indicate a deeper understanding by management of their company’s business model, market environment, and strategy, potentially leading to more accurate performance forecasts. We then analyze the impact of the proportion of qualitative expressions in the MD&A on the accuracy of management earnings guidance in financial results summary. We aim to understand how the nature of information in MD&A–whether more qualitative–correlates with the precision of managerial predictions on company performance.</p>

DOI： 10.11517/pjsai.jsai2024.0_2g6gs601

Authorship：Last author   Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>This paper introduces LLM-Traders, a novel approach to analyzing financial time series (FTS) through the use of fine-tuned large language models (LLMs) and prompt engineering. The research addresses three primary challenges inherent in FTS analysis: (1) pervasive noise, (2) complex, diverse range of models, and (3) constantly evolving dynamics. Our methodology initially concentrates on reducing overfitting, a prevalent issue caused by noisy data. This is achieved by meticulously fine-tuning the LLMs to recognize and interpret the unique attributes of FTS. Subsequently, we implement strategic prompt engineering within these models. This strategy enables effective navigation and adaptation to the multifaceted nature of FTS and accommodates the wide array of existing models. To adapt to the dynamic nature of FTS, we propose an innovative dynamic ensemble method. This approach combines multiple prompt responses in a synergistic manner, enhancing the versatility and accuracy of the analysis. Overall, our integrated approach provides a comprehensive, robust, and flexible framework for addressing the complexities of modern FTS analysis.</p>

DOI： 10.11517/pjsai.jsai2024.0_3xin283

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1109/IJCNN60899.2024.10650271

Authorship：Last author   Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>In recent years, the increase in customer trading volumes at Japanese FX brokers has highlighted the significance of analyzing their customer trends. Traditional finance research has focused on the stock market, analyzing volume changes after earnings or news announcements and the differences between individual and institutional investors. However, the response of individual investors in the FX market, particularly to economic announcements, remains underexplored. This study aims to examine individual investors' trading behaviors in response to the FOMC and Beige Book releases, assessing whether they trade based on the information (H1) and the tone of the Beige book information (H2). Our findings confirm that the publication of the FOMC and Beige Book reports influences trading volume among individual investors. Additionally, we observe that individual investors react to specific topics mentioned in the Beige Book, which is released prior to the FOMC meeting.</p>

DOI： 10.11517/pjsai.jsai2024.0_3xin224

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1109/BigData62323.2024.10826008

Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1109/IIAI-AAI63651.2024.00063

Authorship：Last author   Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1109/IIAI-AAI63651.2024.00058

Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1109/IIAI-AAI63651.2024.00089

Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1109/IIAI-AAI63651.2024.00060

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>The Black-Litterman (BL) method is a useful tool for portfolio construction, treating expected returns and investors' outlook as random variables, and estimating asset weights through Bayesian updating. However, the BL method involves numerous parameters that can influence asset weights, potentially leading to excessive rebalancing, increased transaction costs, and deteriorated performance. Therefore, in this study, we propose the Sparse Black-Litterman (SBL) method to reduce transaction costs. Specifically, by incorporating the Spike and Slab prior distribution into weight changes, we introduce sparsity to weight fluctuations, thereby suppressing unnecessary rebalancing. This approach allows for the construction of an efficient portfolio that integrates investor views while reducing transaction costs. Theoretically, we prove that the introduction of a prior distribution can be reduce rebalancing. In the empirical analysis, we use both synthetic and real data to validate the effectiveness of our proposed method and its impact on reducing transaction costs.</p>

DOI： 10.11517/pjsai.jsai2024.0_4xin255

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>We propose a method for transforming multivariate stock time series data into music. Typically, investors rely on visual information, such as stock charts, to make investment decisions based on stock time series. However, watching numerous stock data simultaneously and for long time is highly demanding and challenging. Therefore, we focus on representing essential information for investment decisions (trends and sudden changes) through sound information, or more precisely, music, which does not depend on visual interpretation. We expect that decision-making through musical transformation can reduce cognitive load and be accessible to a broader range of users. We conduct experiments to determine whether the essential information can be successfully acquired from the music generated by the proposed method.</p>

DOI： 10.11517/pjsai.jsai2024.0_3xin237

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>The optimal execution problem involves finding the optimal execution strategy that minimizes the cost of trading a specific volume of stocks within a certain period. To address this, deep reinforcement learning methods including the Deep Q Network~(DQN) which approximates the action-value function Q through deep learning have been proposed for finding optimal execution strategies. However, deep reinforcement learning faces challenges, such as instability in learning and the need for a huge amount of data. Therefore, we propose incorporating strategies derived from insights of the financial field into conventional DQN methods during the learning process. This approach is expected to be able to learn high-performing policies more stably. Numerical experiments are conducted in environments with various noise tendencies to verify the effectiveness of the proposed method. The results show that the proposed method can consistently reduce costs across all environments compared to baseline methods.</p>

DOI： 10.11517/pjsai.jsai2024.0_3xin280

DOI： 10.2139/ssrn.5001299

DOI： 10.2139/ssrn.4952943

Authorship：Lead author   Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1016/j.frl.2023.104585

Authorship：Last author   Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

DOI： 10.11517/jsaisigtwo.2023.fin-031_110

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

DOI： 10.11517/jsaisigtwo.2023.fin-031_81

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

DOI： 10.11517/jsaisigtwo.2023.fin-031_61

Authorship：Last author   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/iiai-aai59060.2023.00086

Authorship：Last author,　Corresponding author   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/iiai-aai59060.2023.00073

Authorship：Last author,　Corresponding author   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/iiai-aai59060.2023.00063

Authorship：Last author,　Corresponding author   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/iiai-aai59060.2023.00107

Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.frl.2023.103639

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)  

Deep hedging (Buehler et al. 2019) is a versatile framework to compute the
optimal hedging strategy of derivatives in incomplete markets. However, this
optimal strategy is hard to train due to action dependence, that is, the
appropriate hedging action at the next step depends on the current action. To
overcome this issue, we leverage the idea of a no-transaction band strategy,
which is an existing technique that gives optimal hedging strategies for
European options and the exponential utility. We theoretically prove that this
strategy is also optimal for a wider class of utilities and derivatives
including exotics. Based on this result, we propose a no-transaction band
network, a neural network architecture that facilitates fast training and
precise evaluation of the optimal hedging strategy. We experimentally
demonstrate that for European and lookback options, our architecture quickly
attains a better hedging strategy in comparison to a standard feed-forward
network.

DOI： 10.3905/jfds.2023.1.125

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

This study investigates the relationship between expected returns on cryptocurrencies and macroeconomic fundamentals. Investors employ a lot of macroeconomic indicators for their investment decision, and hence adopting a few macroeconomic indicators is not sufficient in capturing a change in economic states. Moreover, due to aggregation, macroeconomic indicators are not measured precisely. To overcome these problems, we employ a dynamic factor model and extract common factors from a large number of macroeconomic indicators. We find that the common factors are strongly linked to the cryptocurrency expected returns at a quarterly frequency, while we do not observe this relationship using macroeconomic indicators such as inflation and money supply. This suggests that macroeconomic information matters in a longer term, which contrasts with the previous literature that explores a short-term relationship. The cryptocurrency prices are not determined by macroeconomic fundamentals in a short-term period since speculators impact the prices. However, in a long-term period, the prices are more linked to macroeconomic fundamentals.

DOI： 10.24135/afl.v11i.540

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>Financial time series prediction with machine learning is an important research topic both practically and academically. Financial time series are noisy, non-stationary, and may contain confidential information, which makes them more troublesome for researchers. To deal with these challenges, we propose a novel competition-based prediction method called Augmentation and Bagging method for Confidential Data series Forecasting (ABCD-Forecast). Our approach is inspired by the framework of data science competitions where multiple analysts submit their predictions and receive the feedback. ABCD-Forecast first distributes various de-noised versions of the data to virtual analysts, enabling the generation of diverse datasets without noise. Combining the predictions of these virtual analysts through a competition format allows us to obtain diverse and accurate models. Our method is applicable for different situations to handle non-stationary data. Furthermore, preprocessing and distributing the dataset through our method ensures data confidentiality, which is substantial in many actual situations. Empirical analysis using real-world data demonstrates the effectiveness of the proposed method in achieving good prediction accuracy.</p>

DOI： 10.11517/pjsai.jsai2023.0_3xin409

Authorship：Last author   Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1109/IIAI-AAI59060.2023.00080

Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/bigdata55660.2022.10021096

Authorship：Last author,　Corresponding author   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/dsaa54385.2022.10032351

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.frl.2022.103130

Authorship：Lead author   Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1080/1351847x.2022.2100715

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.frl.2022.103216

Authorship：Last author,　Corresponding author   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/iiaiaai55812.2022.00094

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/iiaiaai55812.2022.00065

Authorship：Last author,　Corresponding author   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/iiaiaai55812.2022.00089

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1016/j.frl.2021.102375

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.1504/IJDMMM.2022.123357

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>With growing interest in stakeholder capitalism, ESG, and the SDGs, quantifying a company's social reputation is becoming more important. It is useful not only for investors but also for various stakeholders to evaluate the content of the integrated report, which describes a message from companies to stakeholders. In this study, we propose IR score that quantifies the goodness of the integrated report by a BERT model. The BERT model is fine-tuned using ECS which is a score that quantifies the evaluation from stakeholders. We analyze the relationship between the IR score and the financial characteristics of the company. As a result of empirical analysis, we found that the IR score is related to the profitability of the company.</p>

DOI： 10.11517/pjsai.jsai2022.0_4yin256

Authorship：Last author   Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1109/IIAIAAI55812.2022.00066

Authorship：Last author   Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>Stock return predictability is an important research theme as it reflects our economic and social organization, and significant efforts are made to explain the dynamism therein. Although machine learning methods are increasingly popular and effective in stock return prediction in the cross-section, still most of the previous studies rely on a simple quintile portfolio. In this paper, we apply deep learning for stock return prediction in the cross-section and propose a more sophisticated portfolio construction framework called Enhanced Quintile Portfolios. These portfolios are inspired by Pure Quintile Portfolio that overcome the main drawbacks of simple quintile portfolios based on a single sort. The formulation of Enhanced Quintile Portfolio is a quadratic programming problem that considers the trade-off between portfolio alpha and stock diversification, while maintaining the characteristics of a simple quintile portfolio. The experimental comparison shows that the proposed approach outperforms a simple quintile portfolio.</p>

DOI： 10.11517/pjsai.jsai2022.0_3yin243

Authorship：Last author   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.frl.2021.102370

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/sym13060922

Authorship：Last author   Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.3390/e23050560

Authorship：Last author   Publishing type：Research paper (scientific journal)  

DOI： 10.1527/tjsai.36-1_WI2-G

Authorship：Last author   Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1609/aaai.v35i1.16095

Authorship：Last author  

Investors try to predict returns of financial assets to make successful
investment. Many quantitative analysts have used machine learning-based methods
to find unknown profitable market rules from large amounts of market data.
However, there are several challenges in financial markets hindering practical
applications of machine learning-based models. First, in financial markets,
there is no single model that can consistently make accurate prediction because
traders in markets quickly adapt to newly available information. Instead, there
are a number of ephemeral and partially correct models called "alpha factors".
Second, since financial markets are highly uncertain, ensuring interpretability
of prediction models is quite important to make reliable trading strategies. To
overcome these challenges, we propose the Trader-Company method, a novel
evolutionary model that mimics the roles of a financial institute and traders
belonging to it. Our method predicts future stock returns by aggregating
suggestions from multiple weak learners called Traders. A Trader holds a
collection of simple mathematical formulae, each of which represents a
candidate of an alpha factor and would be interpretable for real-world
investors. The aggregation algorithm, called a Company, maintains multiple
Traders. By randomly generating new Traders and retraining them, Companies can
efficiently find financially meaningful formulae whilst avoiding overfitting to
a transient state of the market. We show the effectiveness of our method by
conducting experiments on real market data.

Other URL： https://dblp.uni-trier.de/conf/atal/2021

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1109/IIAI-AAI53430.2021.00085

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

There are three distinguishing features in the financial time series, such as stock prices, are as follows: (1) Non-normality, (2) serial correlation, and (3) leverage effect. All three points need to be taken into account to model the financial time series. However, multivariate financial time series modeling involves a large number of stocks, with many parameters to be estimated. Therefore, there are few examples of multivariate financial time series modeling that explicitly deal with higher-order moments. Furthermore, there is no multivariate financial time series model that takes all three characteristics above into account. In this study, we propose the generalized orthogonal (GO)-Glosten, Jagannathan, and Runkle GARCH (GJR) model which extends the GO-generalized autoregressive conditional heteroscedasticity (GARCH) model and incorporates the three features of the financial time series. We confirm the effectiveness of the proposed model by comparing the performance of risk-based portfolios with higher-order moments. The results show that the performance with our proposed model is superior to that with baseline methods, and indicate that estimation methods are important in risk-based portfolios with higher moments.

DOI： 10.3390/math8111990

Authorship：Last author   Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

DOI： 10.11517/jsaisigtwo.2020.fin-025_76

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

Authorship：Last author   Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>In this paper, we analyzes the relationship between brand value and market value in Japanese companies. As a proxy for brand value, we use the corporate brand index BBES measured by the survey method using business card network information. One of the characteristics of BBES is that only the persons who hold business cards: the persons who have business relations with the surveyed company, are included in the survey. This feature of BBES allows measurement of brand power and reputation of B2B companies with low general awareness. The purpose of this paper is to statistically verify whether this BBES data shows a positive association with market value as a proxy for brand value. Empirical analysis for Japanese companies revealed that brand value by BBES additionally explains the market value given the information on net assets and profits. The explanatory power showed a high value especially in B2B companies.</p>

DOI： 10.11497/jjasmin.29.2_87

CiNii Article

Authorship：Last author   Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

CiNii Article

Authorship：Last author   Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1145/3399871.3399889

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.4236/jmf.2020.102020

Other URL： https://www.scirp.org/xml/100361.xml

Authorship：Last author   Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>The importance and necessity of investment in intangible asset value of companies, including ESG investment, has been increasing in recent years. Corporate brands are an important element of intangible assets. However, especially in B2B companies, there are many unclear points regarding the relationship between corporate brand value and business performance. In this study, we quantitatively evaluated the relationship between B2B corporate brands and performance using a new large-scale corporate brand survey index created from a business card exchange network. We examined the relationship between the corporate brand index and ROE for B2B companies. As a result, we found that companies with a high corporate brand index had a significantly higher profit margin, a significantly lower turnover rate and no significant association with financial leverage. These results show that even in B2B companies, companies with high corporate brands have implemented a differentiation strategy that allows a high margin range, while companies with low brands have adopted cost leadership strategies with high turnover rate.</p>

DOI： 10.11517/jsaisigtwo.2020.fin-024_198

Authorship：Last author,　Corresponding author   International / domestic magazine：International journal  

Optimal asset allocation is a key topic in modern finance theory. To realize
the optimal asset allocation on investor's risk aversion, various portfolio
construction methods have been proposed. Recently, the applications of machine
learning are rapidly growing in the area of finance. In this article, we
propose the Student's $t$-process latent variable model (TPLVM) to describe
non-Gaussian fluctuations of financial timeseries by lower dimensional latent
variables. Subsequently, we apply the TPLVM to minimum-variance portfolio as an
alternative of existing nonlinear factor models. To test the performance of the
proposed portfolio, we construct minimum-variance portfolios of global stock
market indices based on the TPLVM or Gaussian process latent variable model. By
comparing these portfolios, we confirm the proposed portfolio outperforms that
of the existing Gaussian process latent variable model.

DOI： 10.3390/math8030449

Other URL： http://arxiv.org/pdf/2002.06243v1

Publishing type：Research paper (scientific journal)  

DOI： 10.2197/ipsjjip.28.650

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/IIAI-AAI50415.2020.00108

Other URL： https://dblp.uni-trier.de/db/conf/iiaiaai/iiaiaai2020.html#NakagawaSK020

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/DSAA49011.2020.00051

Other URL： https://dblp.uni-trier.de/db/conf/dsaa/dsaa2020.html#NakagawaAK20

Authorship：Last author   Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>The challenge in the research for B2B corporate brands is the identification of perception elements and how they should be measured. In this study, we used a new B2B brand rating score, which was developed based on the reputation for the company among people who have a connection with employees of the company. By analyzing the reputation data using the NLP method, we identified the elements of corporate brand perception in the industrial market. And we demonstrated that the impression of the element "high technology, the attractiveness of products" shows higher relevance to corporate value than other features. These findings are important for brand managers who consider B2B corporate branding and stakeholder engagement that link to corporate value.</p>

DOI： 10.11517/pjsai.jsai2020.0_4rin163

CiNii Article

Authorship：Last author   Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

DOI： 10.1007/978-3-030-58790-1_6

Authorship：Last author   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1145/3442536.3442547

Other URL： https://dblp.uni-trier.de/db/conf/aiccc/aiccc2020.html#AbeN20

Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-030-69886-7_13

Other URL： https://dblp.uni-trier.de/db/conf/pkaw/pkaw2020.html#ManabeNH20

Authorship：Last author  

Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：International journal  

A lead-lag effect in stock markets describes the situation where one (leading) stock return is cross-correlated with another (lagging) stock return at later times. There are various methods for stock return forecasting based on such a lead-lag effect. One of the most representative methods is based on the supply chain network. In this research, we propose a stock return forecasting method with an economic causal chain. The economic causal chain refers to a cause and effect network structure constructed by extracting a description indicating a causal relationship from the texts of Japanese financial statement summaries. We examine whether the lead-lag effect spreads to the 'effect' stock group when there is a large stock fluctuation in the 'cause' stock group in the causal chain. We confirm the profitability of the proposed strategy and the evidence of stock return predictability across causally linked firms in the Japanese stock market.

DOI： 10.1109/iiai-aai.2019.00136

Authorship：Lead author,　Last author,　Corresponding author   Publishing type：Research paper (scientific journal)  

Authorship：Last author   Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.3390/e21020119

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

Various methods to predict stock prices have been studied. In the field of empirical finance, feature values for prediction include “value” and “momentum”. In this research, we use the pattern of stock price fluctuations which has not been fully utilized in the financial market as the input feature of prediction. We extract the representative price fluctuation patterns with k-Medoids Clustering with Indexing Dynamic Time Warping method. This method is k-medoids clustering on dissimilarity matrix using IDTW which measures DTW distance between indexed time-series. We can visualize and grasp a price fluctuation pattern effective for prediction with the proposed method. To demonstrate the advantages of the proposed method, we analyze its performance using TOPIX. Experimental results show that the proposed method is effective for predicting monthly stock price changes.

DOI： 10.1002/ecj.12140

Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/IIAI-AAI.2019.00143

Other URL： https://dblp.uni-trier.de/db/conf/iiaiaai/iiaiaai2019.html#UchiyamaKN19

Authorship：Last author   Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>In this paper, we try to analyze relationships between mission statements and protability. The mission statements are very important messages for companies, because they include founders' spirits, the business policies and so on. Therefore, we consider that mission statements affect protability. Mission statements and protability (The Return On Asset (ROA) was used) were gathered from annual securities reports because descriptions are accurate and reports are easy to obtain. As mission statements were written in natural language and data to be analyzed becomes complicated, a neural computational method called `potential learning' which can interpret internal representations was used. As a result, we found that a generalization performance of the model was 0.6125 (accuracy) and mission statements composed of multiple messages may affect ROA.</p>

DOI： 10.11517/pjsai.jsai2019.0_3a3j1303

CiNii Article

Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

<p>It has been reported that the customer satisfaction which represents corporate brand affects future risk of cash flows and shareholder value in B2C companies. In this study, we verify whether the same relationship holds for B2B companies. For that, we used new B2B brand score which was developed based on the reputation for the company among people who have connection with employees of the company. We showed that the B2B brand score is positively associated with shareholder value.</p>

DOI： 10.11517/pjsai.jsai2019.0_4rin126

CiNii Article

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：International journal  

DOI： 10.1007/978-3-030-13463-1_3

Publishing type：Research paper (scientific journal)  

DOI： 10.1541/ieejeiss.138.992

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.1541/ieejeiss.138.986

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)  

Authorship：Lead author   Publishing type：Part of collection (book)  

DOI： 10.1007/978-3-319-93794-6_7

Publishing type：Research paper (scientific journal)  

DOI： 10.3390/ijfs6020052

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

Other URL： http://orcid.org/0000-0001-5046-8128

Publishing type：Research paper (scientific journal)   International / domestic magazine：Domestic journal  

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

Other URL： http://orcid.org/0000-0001-5046-8128

We consider controlling the false discovery rate for testing many time series
with an unknown cross-sectional correlation structure. Given a large number of
hypotheses, false and missing discoveries can plague an analysis. While many
procedures have been proposed to control false discovery, most of them either
assume independent hypotheses or lack statistical power. A problem of
particular interest is in financial asset pricing, where the goal is to
determine which ``factors" lead to excess returns out of a large number of
potential factors. Our contribution is two-fold. First, we show the consistency
of Fama and French's prominent method under multiple testing. Second, we
propose a novel method for false discovery control using double bootstrapping.
We achieve superior statistical power to existing methods and prove that the
false discovery rate is controlled. Simulations and a real data application
illustrate the efficacy of our method over existing methods.

Other URL： http://arxiv.org/pdf/2102.07826v2

For supervised classification problems, this paper considers estimating the
query's label probability through local regression using observed covariates.
Well-known nonparametric kernel smoother and $k$-nearest neighbor ($k$-NN)
estimator, which take label average over a ball around the query, are
consistent but asymptotically biased particularly for a large radius of the
ball. To eradicate such bias, local polynomial regression (LPoR) and multiscale
$k$-NN (MS-$k$-NN) learn the bias term by local regression around the query and
extrapolate it to the query itself. However, their theoretical optimality has
been shown for the limit of the infinite number of training samples. For
correcting the asymptotic bias with fewer observations, this paper proposes a
\emph{local radial regression (LRR)} and its logistic regression variant called
\emph{local radial logistic regression~(LRLR)}, by combining the advantages of
LPoR and MS-$k$-NN. The idea is quite simple: we fit the local regression to
observed labels by taking only the radial distance as the explanatory variable
and then extrapolate the estimated label probability to zero distance. The
usefulness of the proposed method is shown theoretically and experimentally. We
prove the convergence rate of the $L^2$ risk for LRR with reference to
MS-$k$-NN, and our numerical experiments, including real-world datasets of
daily stock indices, demonstrate that LRLR outperforms LPoR and MS-$k$-NN.

Other URL： https://dblp.uni-trier.de/db/journals/corr/corr2112.html#abs-2112-13951

Other URL： https://dblp.uni-trier.de/db/journals/corr/corr2102.html#abs-2102-07826

Risk management is critical in decision making, and mean-variance (MV)
trade-off is one of the most common criteria. However, in reinforcement
learning (RL) for sequential decision making under uncertainty, most of the
existing methods for MV control suffer from computational difficulties caused
by the double sampling problem. In this paper, in contrast to strict MV
control, we consider learning MV efficient policies that achieve Pareto
efficiency regarding MV trade-off. To achieve this purpose, we train an agent
to maximize the expected quadratic utility function, a common objective of risk
management in finance and economics. We call our approach direct expected
quadratic utility maximization (EQUM). The EQUM does not suffer from the double
sampling issue because it does not include gradient estimation of variance. We
confirm that the maximizer of the objective in the EQUM directly corresponds to
an MV efficient policy under a certain condition. We conduct experiments with
benchmark settings to demonstrate the effectiveness of the EQUM.

Other URL： http://arxiv.org/pdf/2010.01404v3

In time-series analysis, the term "lead-lag effect" is used to describe a
delayed effect on a given time series caused by another time series. lead-lag
effects are ubiquitous in practice and are specifically critical in formulating
investment strategies in high-frequency trading. At present, there are three
major challenges in analyzing the lead-lag effects. First, in practical
applications, not all time series are observed synchronously. Second, the size
of the relevant dataset and rate of change of the environment is increasingly
faster, and it is becoming more difficult to complete the computation within a
particular time limit. Third, some lead-lag effects are time-varying and only
last for a short period, and their delay lengths are often affected by external
factors. In this paper, we propose NAPLES (Negative And Positive lead-lag
EStimator), a new statistical measure that resolves all these problems. Through
experiments on artificial and real datasets, we demonstrate that NAPLES has a
strong correlation with the actual lead-lag effects, including those triggered
by significant macroeconomic announcements.

Other URL： http://arxiv.org/pdf/2002.00724v1

In real-world decision-making problems, risk management is critical. Among various risk management approaches, the mean-variance criterion is one of the most widely used in practice. In this paper, we suggest expected quadratic utility maximization (EQUM) as a new framework for policy gradient style reinforcement learning (RL) algorithms with mean-variance control. The quadratic utility function is a common objective of risk management in finance and economics. The proposed EQUM framework has several interpretations, such as reward-constrained variance minimization and regularization, as well as agent utility maximization. In addition, the computation of the EQUM framework is easier than that of existing mean-variance RL methods, which require double sampling. In experiments, we demonstrate the effectiveness of the proposed framework in the benchmarks of RL and financial data.

Other URL： https://dblp.uni-trier.de/db/journals/corr/corr2010.html#abs-2010-01404

J-GLOBAL

A linear multi-factor model is one of the most important tools in equity
portfolio management. The linear multi-factor models are widely used because
they can be easily interpreted. However, financial markets are not linear and
their accuracy is limited. Recently, deep learning methods were proposed to
predict stock return in terms of the multi-factor model. Although these methods
perform quite well, they have significant disadvantages such as a lack of
transparency and limitations in the interpretability of the prediction. It is
thus difficult for institutional investors to use black-box-type machine
learning techniques in actual investment practice because they should show
accountability to their customers. Consequently, the solution we propose is
based on LSTM with LRP. Specifically, we extend the linear multi-factor model
to be non-linear and time-varying with LSTM. Then, we approximate and linearize
the learned LSTM models by LRP. We call this LSTM+LRP model a deep recurrent
factor model. Finally, we perform an empirical analysis of the Japanese stock
market and show that our recurrent model has better predictive capability than
the traditional linear model and fully-connected deep learning methods.

Other URL： http://arxiv.org/pdf/1901.11493v1

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Stock return predictability is an important research theme as it reflects our
economic and social organization, and significant efforts are made to explain
the dynamism therein. Statistics of strong explanative power, called "factor"
have been proposed to summarize the essence of predictive stock returns.
Although machine learning methods are increasingly popular in stock return
prediction, an inference of the stock returns is highly elusive, and still most
investors, if partly, rely on their intuition to build a better decision
making. The challenge here is to make an investment strategy that is consistent
over a reasonably long period, with the minimum human decision on the entire
process. To this end, we propose a new stock return prediction framework that
we call Ranked Information Coefficient Neural Network (RIC-NN). RIC-NN is a
deep learning approach and includes the following three novel ideas: (1)
nonlinear multi-factor approach, (2) stopping criteria with ranked information
coefficient (rank IC), and (3) deep transfer learning among multiple regions.
Experimental comparison with the stocks in the Morgan Stanley Capital
International (MSCI) indices shows that RIC-NN outperforms not only
off-the-shelf machine learning methods but also the average return of major
equity investment funds in the last fourteen years.

Other Link： http://arxiv.org/pdf/1910.01491v1

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Other Link： http://orcid.org/0000-0001-5046-8128

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