Bitcoin is a crypto currency that is a peer-to-peer(P2P) network systems based on distributed ledger technology and is being used as an alternative payment system. Reliability and safety are very important aspects of payment system. However, in recent years, with an increase in value, crypto currency becomes a target of a malicious users and the attacks that strike the vulnerability of the system are regarded as a problem. Such a problem significantly reduces reliability and safety as a payment system for crypto currency. Therefore, it is necessary to pay attention to cyber security risks inherent in the system, as well as price uctuations usually focused on financial asset prices. In this research, we propose to use information observed in darknet as alternative data. It is useful in evaluating the risk in the crypto currency market. The darknet is a name of an IP address space unallocated by terminals or the like among spaces that can be assigned IP addresses. The darknet is mainly used for observing signs of security incidents. This is useful for investors to grasp potential risks of crypto currency markets and is important for service providers to explain security risks and measures. In addition, the darknet observation information has a prospect of utilizing not only the crypto currency but also the monitoring of the security risk of companys.

In this research, we use English-language news related to the Bitcoin posted on the Internet as a data source, and use LDA(Latent Dirichlet Allocation) which is one of probabilistic topic models, for each article. We judged what kind of topics (keyword group) the sentence is composed of, and quantitatively expressed the excitement of topics in the period using different topic distribution for each article obtained. Furthermore, by analyzing the relationship with the bitcoin's market price on the Internet, we try to evaluate the influence of the news. We show to the relation between the quantity concerning the bitcoin's price (BTC/JPY) in the target period and the excitement of the topic in the news article related to bitcoin.

The European Central Bank (ECB) decides the euro area's monetary policy at the monetary policy meeting of the Government Council. After the ECB's monetary policy meeting, the ECB president Mario Draghi and the vice-president Vitor Constancio hold a press conference to explain the monetary policy management. In this research, using facial expression recognition algorithms based on deep learning, we analyzed the presidents' facial expression in the press conference and estimated the emotional indexes such as "Happiness", "Anger", "Sadness" and "Surprise". As a result, we found that the president Draghi's index of "Happiness" decreased and the index of "Sadness" increased just before making major policy changes in the phase of ECB policy normalization. In addition, the vice-president Vitor Constancio's index of "Happiness" tends to change in the opposite direction to Draghi's index of "Happiness" regardless of the policy change. We believe that the inverse correlation may have the adjustment effect that the impression of the whole press conference will be neutral.

We analyze the impact of advanced machine learning methods on the performance and risk characteristics of popular Japan equity strategies over the last 10 years. We then propose a possible approach to enhancing each strategy through advanced risk control and we analyze the results.

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.

Recently, with the increase of individual investors, the necessity of investment support technologies is increasing. Although analyst reports on which professional securities analysts forecast business performances or stock prices of companies are regarded as important investment decision materials, writing an analyst report is heavy burden. In this research, we summarize newspaper articles and support the generation of analyst reports by using knowledge of information features which are referred to as reasons for analysts' forecasts of business performances or stock prices in analyst reports.

This paper aims to predict a company's financial index by analyzing articles about the company. The authors propose MultiMedLDA, which is one of supervised topic models. MultiMedLDA assumes that each document has two types of labels, discrete value label and continuous one. It models relation between each document and these labels, and predicts an unknown label based on known labels and the documents. Making use of not only documents but also the known labels, it improves prediction accuracy. We evaluated our model with data from the "Japan Company Handbook". Using comments for each company as a document, the type of industry as a discrete value label and the company's ROE (Return On Equity) as a continuous value label, we predicted the ROE in the evaluation.

In the economic and financial fields, there is a growing interest in obtaining new knowledge from large quantities of data, such as corporate financial data and international exchange transactions. On the other hand, as a technical trend of recent data analysis, deep learning-based models have been successfully applied to various data, such as images, text, and audio. Especially, Recurrent Neural Network (RNN) and its extension of Long Short-Term Memory Network (LSTM) have been developed as deep learning for sequential data or time series. However, regardless of its importance, LSTM has not applied to corporate financial time series, such as in the Financial Statements Statistics of Corporations, to the best of my knowledge. In this research, considering the above-mentioned trends, we conduct regression analysis using LSTM for corporate financial time series. For experiments, we obtain the capital investment rate and other financial indicators, such as the cash flow ratio, for each target company from the Financial Statements Statistics of Corporations, and then use them as the objective and explanatory variables, respectively. By changing the number and types of explanatory variables used in the experiments, we evaluate the contribution of each explanatory variable to regression power to the objective variable at several time steps ahead. Furthermore, as baseline methods for the regression tasks, we evaluate the regression power of classical methods: Autoregressive Integrated Moving Averaging (ARIMA), and discuss the comparative evaluation with the LSTM approach.

Predicting the price movements of stocks based on deep learning and high frequency data has been studied intensively in recent years. Especialy, limit order book which describes supply-demand balance of the market is used as feature of a neural network, however, these methods do not utilize the properties of market orders. On the other hand, order encoding method of our prior work can take advantage of these properties. In this paper, we apply some types of convolutional neural network(CNN) architectures to order-based features to predict the direction of mid-price movements. The results show that smoothing filters which we propose to employ over embedding features of orders improve accuracy. Furthermore, inspection of embedding layer and investment simulation are conducted to demonstrate the practicality and effectiveness of our model.

Open-high-low-close price (also OHLC) series have been widely used for the pricemovement analysis of financial time series, including to draw candlestick charts. Modeling these data is complicated by the fact that such data are often unlikely to be samples of stationary stochastic processes, as can be seen in the well-known phenomenon of volatility clustering. In this research, first we try to remedy this matter by using the sequences of differences between high and low prices, which are pointed out to often have higher autocorrelations than the absolute returns of close-price series, and normalize the scales of OHLC by their exponential moving averages. Under our experimental conditions, the Earth Mover's Distance (EMD) between normalized S&P500 training and test data is about one-seventh of the EMD between the unnormalized data. Second, we try to model the normalized data by introducing 6 generative models for them. The EMDs between data generated by our learned models and the normalized test data are about one-sixth of the EMD between the normalized test data and the delta distribution located at the barycenter of the normalized training data. However, they are about 5 times larger than the EMD between the normalized test and training data.