In this study, the statistics of price change patterns is investigated firstly in Speculation Game (an agent-based market model characterized with round-trip trades), then in several real financial instruments, and finally in two other representative market models. The occurrences of historical patterns from Speculation Game suggest that the speculative spirit of the market may be demonstrated as the significant deviation from the uniform frequency patterns. This implication can also be verified from the statistical results of those highly speculative assets, such as gold price and foreign exchange rates. Furthermore, it is found that the reproduction of such historical patterns requires a bottom-up modeling of the markets, as the price change patterns can hardly be achieved in stochastic process models.

The increase of high-frequency trading (HFT) in stock market increases the importance of the analysis of its behaviors. In this study, we analyze it based on the previous research which analyzed it from the viewpoint of "Make and Take orders". The characteristics of our analysis is "analysis of tick distance", and our findings include: 1) the upgrade of stock trading system enables the handling of current trading volume that increased 4 times from 2010 to 2018, 2) Although HFT is making "make orders" away from the best quotes, such orders will not be executed.

This paper investigates the stock of Terilogy Co., Ltd.(code 3356, JASDAQ) using VPIN (Volume-Synchronized Probability of Informed Trading) proposed in Easley, et al. [1]. Especially, the stock is attracting attention in SNS etc., stock price is fluctuating. We examine the investor behavior with VPIN, and also consider how to use this theory is applied to the stock trading.

金融資産価格の収益率にはStylized facts と呼ばれる共通に現れる性質があることが知られている。例えば、収益率分布のファットテイル性や絶対値収益率の長期記憶性、ボラティリティクラスタリンなどがある。ボラティリティには長期記憶性が現れるのが知られているが、何故そのような性質が現れるのかは解明されていない。近年、株価の対数ボラティリティ変化の時系列に注目すると、長期記憶性はなく、ハースト指数が0.5 以下の反持続的な（ラフな）時系列となることが知られてきている。本研究では、ビットコイン時系列に注目し、ビットコインの実現ボラティリティを解析し、対数ボラティリティ変化の時系列に反持続的な性質があるかどうかについて分析をした。また、ボラティリティと取引高や取引数は相関があることから、取引高と取引数の変化にもボラティリティと似た性質が現れると考えられるので、これらについても分析し、反持続的な性質が現れるかどうかを調べた。その結果、ボラティリティ及び取引高と取引数の変化の時系列は反持続性を示すことが分かった。また、これらの時系列はマルチフラクタル性も示すことが分かった。

現在，暗号資産は金融分野で注目を集めており，代表的な暗号資産の一つであるビットコインの1 日の取引量は5 億を超える．本研究では，ソーシャルネットワークサービス上で，強い影響力を持ったインフルエンサーと呼ばれる人々の暗号資産に関するツイートの影響に着目する．我々はインフルエンサーのツイートが暗号資産価格に影響すると考え，インフルエンサーのツイートを用いて，ビットコイン価格の上昇/下降を予測する手法を提案する．インフルエンサーのツイートを収集し，それを言語処理の手法を用いて特徴抽出し，機械学習に用いる素性を生成した．実験の結果，我々はインフルエンサーツイートが暗号資産の価格に影響する可能性があることを示唆した．

There are many studies predicting a stock market using social media. Suwa et al. (2017) proposed a VI index prediction model. They assumed that changes sentiments of investors are topics change posted on social media. However, in their prediction model, the data to use verification is included in the data to use to develop their topic model. Hence, their model might be overfitting. Therefore, we propose a prediction model of VI index avoiding overfitting. We developed a program that applies new posting messages to topic models of a learning period. We created data for verification using this program. As a result, we found that a logistic regression using time series topics on the past seven trading days may predict a rise in the VI index.

For financial institutions, it is important to monitor the performance and performance factors of corporate customers. Financial institutions accumulate large amounts of data to monitor customer information including their performance. It is relatively easy to extract information on performance from structured data, while it is difficult for non-structured data like text data. In this research, we tried to extract sentences that represent customer's performance factors from text data created by financial institutions. Our proposed method using deep learning extracts sentences similar to sentences prepared as correct examples in advance. Due to the difference in properties of text data, our method showed better performance than the prior research.

In this paper, we propose a methodology to forecast the direction and extent of volatility in mid-to-long term excess return of stock price by applying natural language processing and neural networks on the context of analyst reports. Analyst reports are prepared by analysts in research department in stock brokerage firms and we consider the content of reports include usefull information to forecast movements in stock prices. First, our method extracts opinion sentences from analyst reports, while the remaining parts correspond to non-opinion sentences. Second, our method predicts stock price movements by inputting opinion sentences and non-opinion sentences to neural networks separately.

In this paper, we proposed a new approach taking causal relationship into consideration with text mining for analyst report and news in automatic summarization. This approach can be used for reducing work load to read analyst report for institutional investors and gathering important economic information for investment decision for investment analysts. First, we analyzed the validity of the method in extracting causal relationship which can be evaluated from the textual data of Nomura Securities Co., Ltd. As a result, the using method could extract basis information of analyst's opinion from analyst report in higher precision, and we could confirm the style of analyst in expression of opinion and basis.

In this paper, we try to analyze relationships between analyst reports and corporate performances. The analyst reports are documents written about markets forecasts and they are useful for investment judgment. As analyst reports are 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 0.6773 (accuracy) and words related to `word category: abstraction' may affect corporate performances.

本研究では、投資信託の販売促進を行う対話AI の研究を行った。全体の流れは、Seq2Seqを利用した雑談エンジンを通し会話の結果を返す。会話中の言葉の傾向の分析、会話によるリスク許容度の推定、そしてリスク許容度に応じた投資信託をすすめるという流れとなっている。今回の研究では、投資信託を薦める部分について取り組み、有価証券報告書や運用報告書月次レポートを用いて学習をした。学習器についてはLSTM の階層的attention モデルの評価が各種方面で評価が高いのを鑑み、今回の研究でも利用した。また同時に学習が軽量でテキスト分類にもよく用いられるFastText も検証した。

本研究では、経済新聞記事などの経済テキストから、日経平均株価などの市況について言及している記事を抽出し、それらの内容を自動的に要約することによりマーケットレポートにおける市況分析コメントを自動生成する手法の開発を行う。しかし，日経平均株価の市況について言及している記事のみでは，指定した期間において重要な内容について言及している文の数が少なく，そのために重要な内容が市況分析コメントに含まれないことがある．そこで本研究では，ある期間において日経平均株価に影響を与えたイベントを推定し，そのイベントについて述べた記事を関連記事として自動的に検索し，検索された関連記事をも使用することで，生成する市況分析コメントの精度を高める手法を提案する．

In this research, we introduce a system for searching causal relationships of events related to economics and finance in a chain-like manner from a database extracted from economic text data. This system also introduces service application ideas such as inferring economic ripple effects from events entered by users and presenting related stocks tracing the causal chain from news articles.

In recent years, investment strategies using artificial intelligence have attracted a significant amount of research attention. However, it is difficult to construct an efficient investment strategy using artificial intelligence owing to the variable factors in market prices. Therefore, this study aims to focus on a trading method called the NT ratio transaction to reduce the number of price-variable factors. This transaction is an arbitrage transaction, which utilizes the difference in the price movements between Nikkei 225 futures and TOPIX futures. These futures generally exhibit similar price movements and even if the price differences expand, they tend to return to their original separation. Using this transaction, we can target profits from this price difference while offsetting a considerable number of price-variable factors. Therefore, in this study, we construct a model to acquire an investment strategy based on NT ratio transactions via deep reinforcement learning and confirm the effectiveness of this model.

Researchs for financial time series in stock or foreign exchange markets, have been one of traditional themes of financial market analysis. Statistical model approaches such as ARMA and GARCH were mainstream of conventional analysis. However, it is difficult to understand and predict financial time series structures, which are generally characterized by high noise level and low autocorrelation. Meanwhile, researchs to capture the structure by artificial intelligence has been increasing in recent years. In particular, Long Short-Term Memory (LSTM), which can capture time series structure, is already widely used in the field of natural language processing and speech recognition. Therefore, in this study, we investigated the model performance in each TOPIX core30 constituent stock by using logistics regression (LOG), random forest (RAF), gradient boosting (GBT), support vector machine (SVM), and LSTM. The performance was evaluated by metrics such as prediction accuracy, F1 measure, AUC, and return. As a result, LSTM showed the best performance in the models. Moreover, we discussed the effectiveness of the stock market neutral strategy by applying the above prediction models. 10-quantile portfolios using the predicted probability outputted by the model, remarks higher accuracy and returns than individual stock trading in all models. Furthermore, LSTM outperformed the others and it is consistent with the result of S&P500 constituent stocks analysis.

近年，リーマンショックや欧州債務危機に代表される海外発の金融システム不安が，デリバティブ契約等のグローバルな金融取引を通じて，日本の金利市場に大きな影響を与える場面が散見されてきた．この点を踏まえ，本研究では海外市場での金利変動が日本の金利市場に与える影響を検証すると共に，機械学習手法を用いて，海外市場からの影響を考慮した日本市場の金利変動モデルの構築を行った．具体的には，日本国債のイールドカーブの変動を対象として，各種機械学習手法を用いて先行きの予測モデルを構築する際に，米国債のイールドカーブの変動データも学習データとして用いることで，日本の長期金利（長期国債の利回り）の予測精度が向上する傾向を確認した．更に，米国市場のデータを学習に用いた場合に，日本市場の予測精度が向上する度合いが，近年高まっていることも併せて確認した．このことは，海外金利の情報が現在の日本市場の金利予測において有用な情報となることを示唆する結果と言えよう．

In recent years, many researchers have studied stock trading using technical analysis. However, it is necessary to have deep knowledge to use such technical analysis and it is difficult to make a profit using such techniques. Therefore, we construct an evolutionary model to create a profitable investment strategy using technical indicators.

The goal of our research is to build default prediction models on the basis of machine learning models and to obtain useful information for corporate credit risk evaluation. The novelty of this work is twofold. The first point is on how to use time-series information of macroeconomic indexes for the default prediction model for small and medium-sized companies. Since macroeconomic indexes and financial data are different in frequency of being obtained, we considered how to combine these two kinds of data, as input of the default prediction model. In order to combine these data, we summarized time-series information of macroeconomic indexes in the form of mean, percentage change, and volatility. Regarding percentage change, some periods were adopted for the purpose of summarizing both of macrotrends and microtrends. The summarized forms and corporate financial indicators were used as input of the default prediction model in this research. As a result, the default prediction model with inputs of the financial indicators and the macroeconomic indexes outperformed the model with inputs of only financial indicators. Furthermore, the model, to the inputs of which the percentage changes in the fine periods summarizing microtrends were added, outperformed the model not considering the percentage changes in the fine periods. Therefore, considering macroeconomic indexes, especially our proposed method summarizing macrotrends and microtrends, has been found effective for default prediction. The second point is regarding which financial indicators are important in default prediction for small and medium-sized companies by industry sectors. We divided companies into eight industry sectors and investigated which financial indicators are important in each industry sector on the basis of variable importance evaluated with random forest.

Cash management is important to avoid cash-shortage, especially for Small and Medium Enterprises(SME) due to lack of excess cash. Whereas, cash management at SME is not sufficient because of its complexity and high cost. Therefore, we investigate a convenient method to predict a time to cash-shortage and an amount of shortage by Option Approach. In this study, We calculates a probability of default by using Option Approach, regarding its " asset " and " debt " as cash amount and any amount respectively. We evaluated Area Under Curve (AUC) by changing a prediction term, debt amount and calculation term of asset. We found several facts: max AUC is 0.81, short prediction term or large cash decrease makes high AUC, and calculation term is very small relation to AUC. As a result, Option Approach can predict a cash of SME in short term or large change. This approach is useful for cash management of SME to decide an acceptable debt amount and terms.

Recent globalism of economic makes the effect of a certain small bankruptcy enhance internationally beyond of the area and country. So, we propose the model in which the interaction between banks and companies is focused and simulate and consider the effect of whether banks invest the funds or not to the growth of companies. In particular, by varying the width of change of the growth rate of companies according to whether they are invested, we researched the difference in the distribution of the final size of companies. The model of banks is based on actual data such as financial statement analysis edited by Japanese Bankers Association. As the results, the investment of banks did not raise the mean of growth rate but widened the disparity in size among companies. As future plans, we'd like to reproduce and analyze the cause of large chain bankruptcy using the extended model.

近年，市場の「流動性」に関心が集まっている．流動性は金融市場の盛況を表す尺度とされる．流動性が高ければ，市場参加者は市場の中間価格に近い価格で意図した数量を円滑に売買することができるため，流動性は「取引のしやすさ」ととらえることもできる．実証研究では，それぞれの研究目的に沿うよう流動性指標を定義し，その有用性を議論してきた．しかし，どのような市場要因がこれらの指標に影響を与えるのかは明確にされていない．そこで本研究では，人工市場を用いて，どの市場要因が主要な流動性指標（Volume，Tightness，Resiliency，Depth）にどのような影響を与えるのか，指標間にどのような相関性があるのか調査する．さらに実証研究では計測困難な価格の復元速度(Resiliency) についても調査する．

In this paper, we modeled stock markets to investigate the effect of index investing on stock price formation. We showed that index investing has little effect on stock price formation in our stock markets model by analyzing results from experiments with various market settings.

Asset Management Industry may experience a drastic change. The added value of the industry might be lower due to the improvement of technology (Fintech), Fiduciary duty and passive trend. Under such situation, the differentiation of traditional investment will be more and more difficult, and investor will favor alternative products. However, Asset Management companies face the big hurdle in providing alternative products to individual investors due to the Liquidity. Japan regulator tend to restrict the illiquidity assets from the perspective of investor protection. This study will focus on the Real Estate, one of the major alternative products. Real Estate itself is illiquidity asset, while equity Real Estate Investment Trusts (REIT), one of the Real Estate investment products, is liquidity asset and every investor can trade it every day, every time as equity. And regulator tend to restrict other less liquidity Real Estate investment products from the perspective of investor protection as many investors had painful experience in 90s. There were many empirical researches for the liquidity issue of equity REIT. However, all there are using the historical data, and it is difficult the analysis without the market environmental change. This study uses a financial market simulation (artificial market) constructed virtually on computer to assess the relationship between liquidity and market price. Artificial market method has been using for short selling rule and other regulation rule in Japan, but no trade frequency simulation has been conducted before. Analysis results showed that 1. High liquidity might heighten the volatility of the marketable securities. And it will case the price divergence between market price and original value. 2. Fundamental investors and Technical investors should have the same trade frequency. 3. For real estate assets, due to the information disclosure frequency, the trade frequency which can make market efficiency might be once per monthly.

株式や外国為替等の金融市場において、特徴的な統計性質がいくつか知られており、それらの発生メカニズムを解明する為、様々なモデルが考案されてきた。ところが、研究者自身がモデル構築を行う場合、研究者が事前に想定した複雑さの因果関係までしかモデルに含むことが出来ない。そこで、遺伝的プログラミングによって、市場の統計性質を満たすようにモデルを自動生成する。本研究はその第一歩としてfat tail とvolatility clustering を再現する確率過程モデルの構築を行った。その結果、fat tail は比較的単純な式で再現出来るが、volatility clustering の再現には煩雑な式が要求されることが判明した。両者の同時再現においては要求される式の複雑さや誤差縮小速度の違いにより、ハイパーパラメータの細かな調整が必要である。

The financial authorities around the world have been trying to prevent the occurrence of the financial crisis and to reduce the impact by creating a common framework for finance. In response to the Lehman shock that occurred in 2008, many financial institutions were forced to fail due to lack of liquidity, and from the reflection that not only the capital adequacy ratio regulation but also the liquidity ratio regulation is necessary, Basel III regulation consisting of the capital adequacy ratio regulation and liquidity ratio regulation was formulated in 2010. However, some problems are pointed out in the liquidity ratio regulation established under the Basel III regulation. Liquidity ratio regulation formulated under the Basel III regulation is trying to encourage banks to prepare for the financial crisis accompanying liquidity shock by ensuring adequate liquidity but this will lower the bank's asset management yield and it is feared that it may be a factor that puts pressure on management and, on the contrary, makes the financial system unstable. Therefore, in this research, we evaluate the influence of the liquidity ratio regulation on the Basel III regulation on the stability of the financial system by simulation. There are Liquidity Coverage Ratio (LCR) regulation and Net Stable Funding Ratio (NSFR) regulation in the liquidity ratio regulation on the Basel III regulation. In this research, we will examine the merits and demerits of the impact of the LCR regulation and the NSFR regulation, which is the liquidity ratio regulation of Basel III regulation, on the financial system.