Takaaki Ohnishi

Hubs and Authorities on Japanese Inter-Firm Network: Characterization of Nodes in Very Large Directed Networks

Japanese inter-firm network which consists of about one million firms and four million directed links is analyzed by evaluating PageRank, and the authority and hub scores of HITS. We show scale-free degree distribution, power-law distribution of firm-size, fat-tailed distribution of growth rate, positive correlation between degree and firm-size, and similarities between degree and these scores. By comparing with randomized networks, we find that (i) the firm with large firm-size tends to have large PageRank, and small authority and hub scores, and (ii) PageRank correlates significantly with the growth rate which is hardly observed with other quantities.

Random Walk or a Run: Market Microstructure Analysis of the Foreign Exchange Rate Movements Based on Conditional Probability

Using tick-by-tick data for the dollar–yen and euro–dollar exchange rates recorded on the actual transaction platform, a ‘run’—continuous increases or decreases in deal prices for the past several ticks—does have some predictable information on the direction of the next price movement. Deal price movements, that are consistent with order flows, tend to continue a run once it is started. Indeed, conditional probabilities of a run continuing in the same direction after several consecutive observations exceed 0.5. However, quote prices do not show such a run tendency. Hence, a random walk hypothesis is refuted in a simple test of a run using tick-by-tick data. In addition, a longer continuous increase of the price tends to be followed by a larger reversal. The findings suggest that those market participants who have access to real-time, tick-by-tick transaction data may have an advantage in predicting exchange rate movements. The findings reported here also lend support to the momentum trading strategy.

POWER LAWS IN REAL ESTATE PRICES DURING BUBBLE PERIODS

How can we detect real estate bubbles? In this paper, we propose making use of information on the cross-sectional dispersion of real estate prices. During bubble periods, prices tend to go up considerably for some properties, but less so for others, so that price inequality across properties increases. In other words, a key characteristic of real estate bubbles is not the rapid price hike itself but a rise in price dispersion. Given this, the purpose of this paper is to examine whether developments in the dispersion in real estate prices can be used to detect bubbles in property markets as they arise, using data from Japan and the U.S. First, we show that the land price distribution in Tokyo had a power-law tail during the bubble period in the late 1980s, while it was very close to a lognormal before and after the bubble period. Second, in the U.S. data we find that the tail of the house price distribution tends to be heavier in those states which experienced a housing bubble. We also provide evidence suggesting that the power-law tail observed during bubble periods arises due to the lack of price arbitrage across regions.

Ecosystems perspective on financial networks: Diagnostic tools

The economical world consists of a highly interconnected and interdependent network of firms. Here we develop temporal and structural network tools to analyze the state of the economy. Our analysis indicates that a strong clustering can be a warning sign. Reduction in diversity, which was an essential aspect of the dynamics surrounding the crash in 2008, is seen as a key emergent feature arising naturally from the evolutionary and adaptive dynamics inherent to the financial markets. Similarly, collusion amongst construction firms in a number of regions in Japan in the 2000s can be identified with the formation of clusters of anomalous highly connected companies.

Community structure in a large-scale transaction network and visualization

We analyze a transaction network of about 800 thousand Japanese firms to elucidate its community structure. Finding community in networks means the appearance of dense connected groups of vertices and sparse connections between groups. We adopt modularity as a quality function of communities introduced by Newman. The modularity optimization is one of effective approaches to find community. We first use a bottom-up algorithm, which makes the optimization fast by using a greedy algorithm. For the community extraction, the greedy algorithm is widely used, however, may not sufficiently optimize modularity because the optimization tends to be trapped by a local maximum especially for large-scale networks. Alternatively we propose a top-down algorithm with implementation of an annealing method and compare effectiveness of the two algorithms. We also compare the results of the community analysis with images of network structure visualized by molecular dynamics method. The vertices belonging to the same community are spatially located close to each other. The community structure determined by the modularity optimization is well reproduced in the network structure obtained by molecular dynamics.

Structure of global buyer-supplier networks and its implications for conflict minerals regulations

We investigate the structure of global inter-firm linkages using a dataset that contains information on business partners for about 400,000 firms worldwide, including all the firms listed on the major stock exchanges. Among the firms, we examine three networks, which are based on customer-supplier, licensee-licensor, and strategic alliance relationships. First, we show that these networks all have scale-free topology and that the degree distribution for each follows a power law with an exponent of 1.5. The shortest path length is around six for all three networks. Second, we show through community structure analysis that the firms comprise a community with those firms that belong to the same industry but different home countries, indicating the globalization of firms’ production activities. Finally, we discuss what such production globalization implies for the proliferation of conflict minerals (i.e., minerals extracted from conflict zones and sold to firms in other countries to perpetuate fighting) through global buyer-supplier linkages. We show that a limited number of firms belonging to some specific industries and countries plays an important role in the global proliferation of conflict minerals. Our numerical simulation shows that regulations on the purchases of conflict minerals by those firms would substantially reduce their worldwide use.

Temporal and Cross Correlations in Business News

We empirically investigated temporal and cross correlations in the frequency of news reports on companies using a unique dataset with more than 100 million news articles reported in English by around 500 press agencies worldwide for the period 2003-2009. Our main findings are as follows. First, the frequency of news reports on a company does not follow a Poisson process; instead, it is characterized by long memory with a positive autocorrelation for more than a year. Second, there exist significant correlations in the frequency of news across companies. Specifically, on a daily or longer time scale, the frequency of news is governed by external dynamics such as an increase in the number of news due to, for example, the outbreak of an economic crisis, while it is governed by internal dynamics on a time scale of minutes. These two findings indicate that the frequency of news on companies has similar statistical properties as trading activities, measured by trading volumes or price volatility, in stock markets, suggesting that the flow of information through news on companies plays an important role in price dynamics in stock markets.

On the Evolution of the House Price Distribution

Is the cross-sectional distribution of house prices close to a lognormal distribution, as is often assumed in empirical studies on house price indexes? How does the distribution evolve over time? To address these questions, we investigate the cross-sectional distribution of house prices in the Greater Tokyo Area. We find that house prices (Pi) are distributed with much fatter tails than a lognormal distribution and that the tail is quite close to that of a power-law distribution. We also find that house sizes (Si) follow an exponential distribution. These findings imply that size-adjusted house prices, defined by lnPi - aSi, should be normally distributed. We find that this is indeed the case for most of the sample period, but not the bubble era, during which the price distribution has a fat upper tail even after adjusting for size. The bubble was concentrated in particular areas in Tokyo, and this is the source of the fat upper tail.

Community Structure and Dynamics of the Industry Sector-Specific International-Trade-Network

We analyzed the industry sector-specific international trade data from 1995 to 2011 to clarify the structure and dynamics of communities that consist of industry sectors in various countries linked by international trade. We applied conventional community analysis to each time slice of the international trade network, and then identified the links between communities in adjoining years by using the World Input-Output Database. This database contains the industry sector specific international trade data on 41 countries and 35 industry sectors. The identified link structure shows that a triple backbone structure exists in the international trade network. We used the Hilbert transform to evaluate the phase time series of the growth rate of value added for 1435 nodes and then estimated the complex order parameters for communities. The respective amplitude for the order parameter of each community was observed to be greater than the amplitude for all sectors. This means active trade produces higher phase coherence within each community. Phase coherence decreased gradually in the late 1990s but increased sharply in 2001 and 2002.

REPLICATION OF NON-TRIVIAL DIRECTIONAL MOTION IN MULTI-SCALES OBSERVED BY THE RUNS TEST

Non-trivial autocorrelation in up-down statistics in financial market price fluctuation is revealed by a multi-scale runs test(Wald-Wolfowitz test). We apply two models, a stochastic price model and dealer model to understand this property. In both approaches we successfully reproduce the non-stationary directional price motions consistent with the runs test by tuning parameters in the models. We find that two types of dealers exist in the markets, a short-time-scale trend-follower and an extended-time-scale contrarian who are active in different time periods.