A financial market is a diversified dynamical system with many constraints, and price movements are modeled in terms of the micro properties of each transactions and the macro properties of dynamical systems. These two properties must be bridged by the multiplicity. The model forcuses on the size of tick and the number of transactions with the price movement compared with the previous transctions and explains stochastic nature of short-term volatilities and persistently stable long-term volatilities as a results of unique behaviour of heterogeneous market participants.

A credit scoring model is a useful tool for Small and Medium-sized Enterprises (SMEs) lending. In this study, we investigated methods to improve the accuracy of the scoring model using machine learning. As a result, we have shown that Gradient Boosting Decision Tree (GBDT) can obtain the highest accuracy. We found out that GBDT shows better performance than other methods especially when we use more than 10000 learning data. In addition, we demonstrated that ensemble learning can further improve accuracy. According to our simplified estimation, it was suggested that the ensemble learning can reduce the default rate by 16% compared with the conventional method.

We analyze Japan equity portfolios for their risk exposures to North Korea geopolitical destabilization effects over the past few months, by using an implementation of the EM algorithm integrated with a GARCH process. The model identifies which latent factors in the Japan equity market relate to North Korea and predicts their risk impact for the near future.

In This paper, we propose a construction method of an artificial intelligence for TOPIX trading. We apply a Multi-layer Neural Networks as a prediction method, and optimize to maximize the Information Ratio using GA. We also conduct a simulation using TOPIX market data from January 2014 to December 2016, showed the effectiveness of the proposed construction method from the information ratio level.

In recent years, many researchers have taken keen interest in analyzing various kinds of relational data, such as social networks and financial networks. These data can be expressed as a graph or network where each vertex or node is an entity and each edge or link is a relation between a pair of entities. Moreover, each link is often associated with continuous and/or discrete relational attributes, such as in financial networks, the interest rate for a transaction and whether the transaction is international or intranational. In this paper we focus on max-margin latent feature relational models (called Med-LFRM) that are based on Indian buffet process (IBP) and maximum entropy discrimination (MED). For the estimation of model parameters, the Bayesian estimation is deemed equivalent to minimizing an objective function, which involves misclassification errors. We focus on link prediction problem for the networks with continuous and discrete relational attributes. We also focused on the time dependent analysis for the networks, and therefore, we estimated the model parameters considering the observations in the previous time interval. We demonstrate, through experiments with inter-bank financial networks, the effectiveness of the above model in terms of the link prediction performance.

The forecasting the stock price of a particular has been a difficult task for many of analysts and researchers. In fact, investors are highly interested in the research area of stock price prediction. However, to improve the accuracy of forecasting a single stock price is a really challenging task, therefore in this paper, I propose a sequential learning model for prediction of a single stock price with corporate action event information and Macro-Economic indices using LTSM-RNN method. The results show the proposed model is expected to be a promising method in the stock price prediction of a single stock with variables like corporate action and corporate publishings.

We study the limits of prediction accuracy of Bitcoin price data in CNY currency using tick data from the OKCoin Bitcoin exchange (source: Kaiko data). The tick data contain the price, volume, and trade direction, and are transformed to the OHLCV format using standard methods. In this report, we deploy the Support Vector Machine algorithm by Vapnik to estimate the sign of the hour-to-hour transaction return using a sampling moving window of varying size on the past data. Several kernel functions are validated. Our first results for all months of the year 2015 show that the hit ratio accuracy level (the fraction of correctly predicted upward or downward events) does not exceed 60%. It remains to be established whether this low result corresponds to the causal extraction limit inherent in the data, or whether it can be improved by deploying other methods, such as LSTM networks in deep learning.

This research has two objectives: (1) to model and analyze the momentum effect, (2) to propose a portfolio reconstruction algorithm that can use the momentum effect to obtain excess profit. The momentum effect tends to be present in the stock market, and describes the phenomenon whereby rising (declining) stocks tend to continue to rise (decline). However, because existing research does not separate momentum effects from stock price fluctuations it is not always possible to obtain excess return when working with an unknown data set that contains a momentum effect. In this research, we define a new External Force Momentum Effect (EFME) model based on bias in stock price rises (declines). We prepared an artificial data set that contained this momentum effect and constructed a portfolio with the proposed algorithm. The relationship between the EFME model and excess return is then analyzed to verify that excess profit can be obtained. Additionally, we confirmed that the proposed method can obtain higher excess return than the existing method when applied to artificial and real stock data sets.

In this research, we propose a method to automatically classify sentences including causal information concerning business performance (e.g. "Orders of semiconductor manufacturing equipment were good.") extracted from summary of financial statements of companies based on business segments of the companies. Moreover, we propose a method to extract performance sentences from summary of financial statements. For example, the sentences including causal information extracted form summaries of financial statements of SUBARU Co., Ltd. are classified to either "automobile" segment or "aerospace" segment. In addition, our method extracts performance sentences, e.g. "Sales were \3,262.0 billion, an increase of \93.7 billion (2.9%) compared with the previous fiscal year.", by deep learning and automatically generates training data.

In this paper, we proposed a method for supporting investors. Our method extracts basis information on analyst's forecasts from analyst reports and assigns polarity to the analyst reports. Analyst reports which are written about a company's performance or profitability by securities analysts are useful for investment but investors can only read it a little because many reports are published. Therefore, a system which judges investing by an artificial intelligence technique is required. By giving polarity to the analyst reports, the proposed method catches a slight change in performance. This ability of method is useful to judge whether investors need to read analyst reports carefully.

These days, a growing number of individual investors is attracting public attentions even in Japan, and securities companies are actively providing them with investment informations. Especially, analyst reports written by professional security analysts are important investment judgment materials, but their timing of publication varies by brands. In this paper, using the structures of causal relationships of sentences in analyst reports, the ways of security analysts paying attention to cause informations concerning business performances were learned, and newspaper articles including similar causal relationships were extracted. We aim to realize a real-time investment supporting system.

In recent years, textual information, which is unstructured data attracts attention as new analytical data in the financial and economic fields and it is expected to structure knowledge on this domain. One such knowledge is a sentiment polarity dictionary in which each word is representing positive or negative. In building the dictionary, it is costly to add the polarity value to a vast number of words manually. Therefore, in this research, we propose a the dictionary construction model especially considering the synonymity and symmetry of words. As a result of the experiment, the proposed method is a more accurate than the model of the previous research. In addition, we extended the conventional dictionary using the proposed method, and we showed that the extended dictionary has higher accuracy than the dictionary which is not extended.

In recent years, many people attempt to apply machine learning to a broad range of fields. At the same time, thanks to improvements of the information technology, we can use the enormous number of the past financial data as a learning data . In this paper, we describe prediction results of the foreign exchange price. The prediction model was made by using Convolutional Neural Network(CNN). Then, we compare the results of the model which learned numeric data with the model which learned image data.

Recent technological breakthrough has enabled us to study the microstructure of financial markets using the high-frequency trading data. In this presentation, we review our recent preprint (arXiv: 1703.06739), in which individual traders's strategies are analyzed on the basis of informative order book data with anonymized trader identifications. We empirically study the trend-following behavior of individual traders on the basis of conditional statistical analysis. We then propose a microscopic model of financial markets on the basis of the empirical finding of trendfollowing of individual traders. We further develop a systematic theory to our microscopic model paralleling to the mathematical formulation of kinetic theory. Finally, the agreement between empirical results and our theoretical predictions are shown in terms of the order book profile and the price movement distribution.

Fundamental factor models are one of the important methods for the quantitative active investors (Quants), so many investors and researchers use fundamental factor models in their work. But often we come up against the problem that highly effective factors do not aid in our portfolio performance. We think one of the reasons why is that the traditional method is based on multiple linear regression. Therefore in this paper, we tried to apply our machine learning methods to fundamental factor models as the return model. The results show that applying machine learning methods yield good portfolio performance and effectiveness more than the traditional methods.

In this paper, we propose order-based approach to predict future movements of a stock price. Our models employ a convolutional neural network(CNN) over embedded orders that have quantitative and qualitative variables. For each dataset of stock codes, the models outperform traditional feature-based approaches. Furthermore, we show that training under less influence of noise can be performed by applying an averaging filter to embedded feature space. Analysis of the embedding layer reveals that the models put emphasis on the features of market orders that are correlated with price return.

After the Bank of Japan (BOJ)'s monetary policy meeting, the BOJ governor makes a press conference to explain the monetary policy. In this research, using facial expression recognition algorithms based on deep learning, we analyzed the governor's facial expressions in the press conference and estimated the emotional indexes such as "Happiness", "Anger", "Sadness", "Surprise". As a result, we found that the indexes of "Anger" and "Disgust" increased much just before making major policy changes. On the other hand, the index of "Sadness" tended to decline after the monetary policy changes. This suggests that the information based on the facial expression analysis can be a useful material for forecasting the future monetary policy.

In this paper, we propose a methodology to effectively capture credit risk from firms' network. In short, our target is to numerically obtain additional credit risk from connected firms on network. Recently, commercial networks are available for investing and managing risk on professional information terminals like Bloomberg and Reuters. They enable us to check commercial connection of firms. We utilize them to expect positive and negative effect on observing firms from neighbor firms especially when the neighbor firms have any credit events. We propose a methodology to analyze/measure impact which observing firms potentially receive from their neighbors. We applied Merton model which is generally utilized for credit risk management to calculate additional risk and simplified the formula for practicability/usability. Also, it enables us to escape from having any difficulties in computation time. We introduce our approach with overviewing simple model guidance and explaining a few samples of numerical experiments.