近年，投資ファンドがある業界のすべての企業の大株主となる"水平株式保有"(horizontal shareholding)(または"共同保有"(common ownership) ともよばれる) が，公正な企業間の競争を阻害し，産業の発展を妨げているという主張が増えてきた．特にパッシブファンドによる水平株式保有が大きな割合となっており，大きな議論となっている．本研究では，人工市場モデルを用いてパッシブファンドの増加が企業間競争と市場価格へ与える影響を分析した．その結果，パッシブファンドの割合がさほど大きくなくても，競争を阻害する可能性を示した．また，競争に勝った企業の市場価格が増加したファンダメンタル価格以上に上昇して割高となり競争を促す株主が離れて競争力を弱くする一方，競争に負けた企業の市場価格が減少したファンダメンタル価格よりさらに下落して割安となり競争を促す株主が増え競争力を強くして，企業間競争のバランスをとるメカニズムが存在する可能性があることを示した．パッシブファンドの増加はこのようなメカニズムを弱める恐れがあると考えられる．

In recent years the risk of mutual funds has become difficult to assess. This is because mutual funds have come to choose various assets, some of which may have high risk, and there may be some funds whose performances depend on dangerously much those of a part of the assets that the funds hold, and so on. Companies managing mutual funds are expected to perform risk control to prevent investors from taking unforeseen risk of funds. A related revision to the investment fund legal system in Japan led to establishing what is known as"the rule for investment diversification." Some papers discussed that the rule for investment diversification affected price formation at the time of a market crash; however, we could not find that it affects price formation at the time of a soaring market. In this paper, we investigated that the rule for investment diversification affected price formation in financial markets where two types of investors who followed the rule and did not follow it participated at the time of a soaring (crashing) market that was caused by the bulge (collapse) of an asset fundamental price using agent-based simulations and discussed the difference between these effects. As results, we found that, in two-asset markets where two types of investors who followed the rule and did not follow it participated, when one asset fundamental price soared, the rule for investment diversification prevented its price rising, and when one asset fundamental price collapsed, the rule promoted the other's price to decline.

株価を予測するために, 様々なファクターが特徴量として多数提案されてきた. 一方で,近年これら大量のファクターを同時に扱うために, 機械学習, とりわけ深層学習を用いた株価予測手法が提案されてきた. しかしながら, 日々の運用を意識した具体的な実践事例はほとんどない. そこで本稿では, 日本株式市場において機械学習を用いたマルチファクターモデルに基づく実運用を意識した実証分析を行う. そして, 深層学習の有効性を確認するとともに，分析を通じて得た知見について考察する．

Recent popularity in algorithmic trading has spurred on researchers to investigate the variety and the evolution of the trading strategies. In this talk, we present our recent study (under review in PLOS ONE), in which the strategy distribution of limit orders is analyzed by using the high frequency data set including anonymized trader IDs. We first identify timescales for each trader to measure market-price trends by the multi-regression analysis. Clustering the timescales into several clusters, we then show the frequencies of the submissions and transactions for each cluster. Furthermore, we provide the microstructure insight to their frequencies in terms of the average shape of limit orders. Finally, we quantify the activity level of each cluster, and show that some clusters are unique to the local time in Tokyo or New York.

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.

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.

本研究では、資産価格リターンの予想を行うResidual Network (ResNet)を、マクロ経済の理論モデル（DSGE）とのマルチタスク学習により正則化する方法を提案する。経済理論が対象とする概念の一部は、現実の資産価格の決定メカニズムにも共有されている。したがって、マルチタスク学習によって共有因子を鮮明化することは汎化性能を改善する可能性がある。そこで、多数の経済金融時系列を入力とし、リターンを予測するResNet の損失関数に対して、経済理論への整合性が制約となるようなペナルティを課す（正則化項の加算）、というマルチタスク学習を行った。実際、複数のハイパーパラメターで検証した結果、マルチタスク化によって、テストデータへの汎化性能が改善する傾向が確認された。すなわち、多数の経済金融時系列を教師データとして受け取ったAI は、リターン予想に資する特徴の抽出を、経済理論とある程度整合的な仕方で行い、それが過学習の予防となって汎化性能が向上した可能性がある。また、Layerwise Relevance Propagation を応用することで、マルチタスク共有中間層のうち、任意の経済ファクター（DSGE によって識別）と関連の深いユニットを特定し、それらが同時に資産リターンに与えた影響を抽出することで、AI 内部で形成されている経済ファクターと資産価格リターンの相関構造を記述することが可能となり、解釈性を高めることにもなる。

In This paper, I propose a method to use Artificial Intelligence for early warning in TOPIX trading. I construct an early warning system with AI that includes AI traders who preliminarily learned TOPIX market data, and by using forecast of AI traders as early warning indicators, efficiency of scoring the indicators has been improved. In addition, I conducted a TOPIX trading simulation using the early warning system with AI, which resulted in a higher Sharpe ratio than that of TOPIX.

In this research, I have developed a prediction model of long-term interest rate (long-term government bond yield) using machine learning method (SVM, nonlinear SVM, decision tree, RF, logistic regression, LSTM). As a result, it was confirmed that the accuracy of the LSTM-based model is relatively higher in the long-term interest rate prediction than the other models. Furthermore, long-term interest rate is influenced by interest rate fluctuations in the surrounding maturity due to the influence of arbitrage transactions. Therefore, I constructed a fluctuation model of the yield curve incorporating the relationship between long-term interest rate and the other maturity rates in the form of extending the above-mentioned LSTM-based prediction model. As a result, when using the yield curve fluctuation model for predicting long-term interest rate, some improvement was seen in the prediction accuracy of long-term interest rate.

高頻度取引(HFT) により得られる高頻度注文情報を用いた短期の市場動向予測に注文が集まっている．高頻度注文情報は同時多発的な注文の系列であるため，隣接するデータの順序情報にノイズが大きく，より広いデータ範囲におけるロバストなパターン認識が必要である．本研究では注文情報のパラメータ化および畳み込みニューラルネットワーク(CNN) の活用を通し，この問題の解決を図った．注文情報に対する適切な情報抽出を行い，CNN を用いて予測モデルを構築することで，注文パターンに対しロバストな予測が可能になる手法を提案する．実験の結果，FLEX FULL注文データに対する解析で，本手法が高い精度で未来の株価動向を予測できることを確認した．

In foreign-exchange (FX) dealing, FX brokers basically cancel out the orders from their customers to prevent the price fluctuation risk by cover transactions with global megabanks called Counter Party (CP). Each CP has huge amount of money to play a role of market reader, and might have proprietary know-how to foresee future price movements. From this viewpoint, we try to extract their knowledge by a machine learning approach, and therefore we apply the stacking method that aggregates some predictors to extract the ensemble knowledge. If CP's price quotations are decided by foreseeing future price possibilities, their quotations can be considered as predictors. From this concept, we apply the stacking method to their quotations and obtain the ensemble knowledge from them. Through some simulations using real price data, we could confirm that the given ensemble knowledge improves the prediction accuracy of FX price movements compared to the machine learning using a single CP's price quotation.

近年，人工知能分野の手法や技術を，金融市場における様々な場面に応用することが期待されており，例えば，膨大な金融情報を分析して投資判断を支援する技術が注目されている．特に，個人投資家・機関投資家を問わず，大量の情報の中から投資に有用な情報を抽出し，提示するシステムが求められている．そこで，我々は個人，もしくは，専門家が容易に新聞記事や決算短信などの金融テキストを分析できるテキスト解析プラットフォームを構築することを目指す．

In this research, we propose a method of extracting business segments from securities reports and extracting sentences including causal and result information concerning business performance for each extracted business segments. For example, our method extracts "In the aluminum rolled products business, shipments of high purity foils for aluminum electrolytic capacitors for industrial equipment and automotive use increased and sales increased." as a sentence including causal information concerning business performance. Moreover, our method estimates that the sentence belongs to business segment "aluminum". Our method extracts "As a result of this segment sales in this segment were 105,439 million yen, operating profit was 6,697 million yen." as the result information belong to "aluminum" segment.

In this paper, we propose a method for classifying timely disclosures of company information with risk to the corporate performance by using deep learning. For example, our method classifies a timely disclosure "A notice of recording a extraordinary loss" as "Including risk". Moreover, our method classifies the timely disclosure as "Extraordinary loss". Our method makes timely disclosures of company information easy to see and investors will be useful to make investment decisions. We evaluate our method and it attained 87.4% accuracy.

In this paper, we propose a method for extracting causal relation between performance factors and performance results from summaries of financial statements by deep learning. For example, our method extracts "Sales of copper tubes for air conditioners declined due to high demand due to the hot summer heat caused by the hot summer of the year" as a performance factor, and "Consolidated operating profit was 4,859 million yen" as a performance result corresponding to the performance factor. By extracting such causal relation, it is possible to analyze what kind of factors have changed the performance.

In this paper, we examined a new method to predict fluctuation of foreign exchange rate. We input information extracted from historical data into PointNet++, which is proposed by C.R.Qi et al.[3] and predicted US Dollar to Yen Exchange rate 5 minutes later. The results implied that although there was tendency of overfitting, our method might capture a part of some structured factors of foreign exchange fluctuation. It is suggested that various range of approach related to machine learning could be useful for financial problems.

深層学習をガウス過程で構築する手法が近年提案された。これは深層学習に対するカーネル関数をガウス過程の共分散関数として使用し、深層学習モデルの完全なベイズ推論を行う。それにより通常の深層学習にはない、予測の不確実性が考慮できる等のメリットが得られる。我々は、この手法を一般に裾の厚いといわれる金融データへ適用することを目的に、ガウス過程からt 過程への拡張を行う。実証分析の結果、金融時系列においてガウス過程よりも良好な結果が得られた。

本研究では、1 分ごとのビットコイン価格データをもとに、その収益率時系列の統計的性質を調べた。収益率時系列の性質は他の金融時系列によく見られるファットテイル分布となっていることが分かった。ボラティリティの時間変動はGARCH 及びRGACH モデルによって解析した。その結果、ボラティリティの持続性は大きく、またボラティリティプロセスの非対称性は小さいことが分かった。尖度は非常に大きいが、時間スケールが大きくなるにしたがってゆっくりとガウス分布の値に近づくことが分かった。マルチフラクタル解析からは、一般化ハースト指数h(q)を決定した。一般化ハースト指数はq の値に依存し、マルチフラクタル性を持つことがわかった。更に、絶対値収益率のべき乗の自己相関を調べたところ、べき指数が約0.4 のところで自己相関が最大となり、ビットコイン価格時系列にテイラー効果が存在することが分かった。

Foreign-exchange trading (FX) is well known as an asset management method like stock investment. Individual traders basically send their orders to an FX broker, and the FX broker executes cover transactions with global megabanks to prevent the price-fluctuation risk. If it is possible to foresee the future price movement, FX brokers can make their cover transactions more efficient. Fortunately, FX brokers can see the trading positions of their customers. If each customer has a little predictive power, the aggregation of all customers' positions might improve the predictive power in terms of the wisdom of crowds. From this viewpoint, we tried to extract the collective knowledge from all customers and applied it to improve cover transactions. As a result, our idea worked well to make FX brokerage business less risky and more profitable.