Novel and topical business news and their impact on stock market activities

We propose an indicator to measure the degree to which a particular news article is novel, as well as an indicator to measure the degree to which a particular news item attracts attention from investors. The novelty measure is obtained by comparing the extent to which a particular news article is similar to earlier news articles, and an article is regarded as novel if there was no similar article before it. On the other hand, we say a news item receives a lot of attention and thus is highly topical if it is simultaneously reported by many news agencies and read by many investors who receive news from those agencies. The topicality measure for a news item is obtained by counting the number of news articles whose content is similar to an original news article but which are delivered by other news agencies. To check the performance of the indicators, we empirically examine how these indicators are correlated with intraday financial market indicators such as the number of transactions and price volatility. Specifically, we use a dataset consisting of over 90 million business news articles reported in English and a dataset consisting of minute-by-minute stock prices on the New York Stock Exchange and the NASDAQ Stock Market from 2003 to 2014, and show that stock prices and transaction volumes exhibited a significant response to a news article when it is novel and topical.Comment: 8 pages, 6 figures, 2 table

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.

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.

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.Econophysics, Power law, Bubbles, House prices, Land prices, Price dispersion

High quality topic extraction from business news explains abnormal financial market volatility

Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc.) affect trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affect stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their "thematic" features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized fact in financial economies, namely that at certain times trading volumes appear to be "abnormally large," can be partially explained by the flow of news. In this sense, our results prove that there is no "excess trading," when restricting to times when news are genuinely novel and provide relevant financial information.Comment: The previous version of this article included an error. This is a revised versio

On the Evolution of the House Price Distribution

Is the cross-sectional distribution of house prices close to a (log) normal 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 for the period 1986 to 2009. We find that size-adjusted house prices follow a lognormal distribution except for the period of the housing bubble and its collapse in Tokyo, for which the price distribution has a substantially heavier right tail than that of a lognormal distribution. In addition, we find that, during the bubble era, the sharp price movements were concentrated in particular areas, and this spatial heterogeneity is the source of the fat upper tail. These findings suggest that the shape of the size-adjusted price distribution, especially the shape of the tail part, may contain information useful for the detection of housing bubbles. Specifically, the presence of a bubble can be safely ruled out if recent price observations are found to follow a lognormal distribution. On the other hand, if there are many outliers, especially near the upper tail, this may indicate the presence of a bubble, since such price observations are unlikely to occur if they follow a lognormal distribution. This method of identifying bubbles is quite different from conventional ones based on aggregate measures of housing prices, and therefore should be a useful tool to supplement existing methods

The Evolution of 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.