In this study, we propose the use of exchange value as the stable numeraire for pricing financial assets. The price of currency is used to illustrate our method. In the currency market, the price of a currency is usually measured by using its exchange rate to another currency, for example, the dollar-yen rate. We point out that the price of currency can also be measured by using the proposed numeraire, namely the exchange value. In particular, we calculated the exchange values of different currencies from their exchange rates under a statistical approximation. Finally, probability distributions of differently measured price returns are compared with each other to evaluate the stability with the use of the proposed numeaire.

In recent years, portfolio optimization with the use of a vector autoregressive (VAR) model to estimate future stock returns has been subject of research. In this paper, we propose an optimization model that uses time series models to predict not only the future returns but also the conditional variance-covariance matrix of returns. More speci?cally, the future returns are predicted by using the VAR model, and the conditional variance-covariance matrix is estimated by using a dynamic conditional correlation (DCC) multivariate GARCH model. We evaluate the out-of-sample investment performance of our model using historical data of U.S. stock market.

We detect turning points of the non-stationary time series data of Nikkei 225 index for the period between 1993 and 2010 using 'change finder'. We also calculate the market sentiment using news data prior to the turning point. Our findings are in two-fold. Firstly, the 'change finder' signals the bullish turning points following the rise of the optimistic sentiment and vice versa. Secondly, bullish change occurs significantly more in the first half of the year than the latter half. Our findings are consistent with the view that the reported 'Dekansho-bushi' effect in the Japanese Stock Market is driven by the market psychology.

We put forward an optimized method of stock factors prediction model which can be easily extended to realtime prediction system., based on the new version of Yahoo Financial text board of 2012.11~2013.6 with about 4000 companies. Preceding studies have verified that BBS text can be used to forecast trade volume and return. On this basis, LSA (Latent Semantic Analysis) and multi-SVM model are put forward in our framework to improve the accuracy of natural language processing and the prediction.

Recent research has explored the proper method to analyse the relationships in financial markets for risk management, In this paper, we apply transfer entropy to construct a stationary network which represents the information propagation between stocks. This network can differ significantly from other static networks, such as correlations network and minimal spanning tree network, because it can include the direction information. We demonstrate that this method reveals meaningful hidden relations of cause and effect between stocks.

It had been believed that the risk of a bank going bankrupt is lessened in a straightfor-ward manner by transferring the risk of loan defaults. But the failure of American International Group in 2008 posed a more complex aspect of nancial contagion. This study presents an ex-tension of the asset network systemic risk model (ANWSER) to investigate whether credit default swaps mitigate or intensify the severity of nancial contagion. The empirical distribution of the number of bank bankruptcies is obtained with the extended model. Systemic capital bu er ratio is calculated from the distribution. The ratio quanti es the e ective loss absorbency capability of the entire nancial system to force back nancial contagion. The key nding is that the the risk transfer in an interbank network does not mitigate the severity of nancial contagion.

In this study, we propose a new forecasting method of a financial time series based on Deep Belief Network (DBN) by enhancing the approach of the Chao et al. First, a new topology for a regression training is proposed. Second, we forecast a Nikkei Stock Average renewing a training term. Third, Self-Organized-Map (SOM) is introduced for reducing the computational time in DBN. It is shown by some experiments that some improved performance indexes can be obtained, and reduction of the computation time is achieved.

We propose a method to predict stock prices by SVMs using news on foreign exchange rates on the Web. Our method targets Japanese news and stocks. We compare several parameters for predicting the span, and fixed span to 50 minutes. We then apply the proposed method to 15 different stock issues from Nikkei 225. Although our preliminary results are encouraging, we plan to further improve the accuracy of our approach in future.

To clarify connections between social media data and ?nancial market data, we studied the quantitative evaluations of relations between time series of word appearances on Japanese blogs and those of stock prices. In particular, we proposed the method of comparison of some correlation indices such as the Spearman's rank correlation coe?cient, based on the man-made related stock information. We found that the Spearman's rank correlation coe?cients over time series of 562 keywords can hardly pick the correct combinations of related stocks out the pool of more than 3,000 stocks on the Tokyo Stock Exchange. However, we show that the composite correlation indicators, which re?ect multiple features of the time series, can pick the correct stocks up to a certain level of statically signi?cant.

In this paper, we analyze how the relationship among Nikkei average futures and individual stocks changes when a big event occurs, intending to give investors useful information for the risk management. We showed that the strong relationships had appeared after the Great East Japan Earthquake and we could detect them by using order books properly.

We introduce analysis of arbitrage opportunities in the foreign exchange market by using high-frequency data. We showed two kinds of arbitrage opportunities, negative spread arbitrages and triangle arbitrages, and we modeled the occurrence of the arbitrage with volatility, the number of deals and the number of computer traders. The market has changed over the last ten years. In particular an emergence of computer traders, which have trading algorithms in computers, is one of the biggest news in financial markets, and the computer traders can detect triangular arbitrages much faster than human traders. We also modeled the disappearance probability of triangular arbitrages within one second that is the minimum observation interval of our data by using volatility, the number of deals and the number of computer traders.