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

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

A Proposal of a Real Time Economic Sentiment Indicator Based on Twitter and Google Trends for the Spanish Economy

The main aim of this paper is to build a real time economic sentiment indicator (RT-ESI) for Spain, based on text mining and deep learning from Twitter and Google Trends, that can anticipate GDP and household consumer behaviour. This work contributes to the literature, firstly by carrying out a sentiment analysis with a set of selected keywords that are related to emotions and expectations, then we apply a factor analysis to create the composite indi cator, next we use a descriptive analysis to highlight the main associations between indexes, and finally we employ fractional integration and cointegration techniques (ARFIMA and FCVAR) to assess the RT-ESI behaviour against the European Commission´s consumer confidence indicator and the GDP. The results show that the GDP (YoY) presents the same behaviour as ourleading indicator, finding mean reversion. The behaviour of the CCI series differs from the others.post-print288 K

A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis.

The main aim of this paper is to build a Real Time Leading Economic Indicator (RT-LEI) that improves Composite Leading Indicators (CLI)’s performance to anticipate GDP trends and turning points for the Spanish economy. The indicator has been constructed using a Factor Analysis and is composed of 21 variables concerning motor vehicle activity, financial activity, real estate activity, economic sentiment, and industrial sector. The data sources used are Google Trends and Thomson Reuters Eikon-Datastream. This work contributes to the literature, studying the dynamics of GDP, CLI and RT-LEI using Fractional Cointegration VAR (FCVAR model) and Continuous Wavelet Transform (CWT) for its resolution. The results show that the model does not present mean reversion and it is expected the RT-LEI reveals a bear trend in the next two years, alike IMF and Consensus FUNCAS′ forecasts. The reasons are mostly associated with escalating global protectionism, uncertainty related to Catalonia and faster monetary policy normalization.pre-print990 K

Lagged correlation networks

Technological advances have provided scientists with large high-dimensional datasets that describe the behaviors of complex systems: from the statistics of energy levels in complex quantum systems, to the time-dependent transcription of genes, to price fluctuations among assets in a financial market. In this environment, where it may be difficult to infer the joint distribution of the data, network science has flourished as a way to gain insight into the structure and organization of such systems by focusing on pairwise interactions. This work focuses on a particular setting, in which a system is described by multivariate time series data. We consider time-lagged correlations among elements in this system, in such a way that the measured interactions among elements are asymmetric. Finally, we allow these interactions to be characteristically weak, so that statistical uncertainties may be important to consider when inferring the structure of the system. We introduce a methodology for constructing statistically validated networks to describe such a system, extend the methodology to accommodate interactions with a periodic component, and show how consideration of bipartite community structures in these networks can aid in the construction of robust statistical models. An example of such a system is a financial market, in which high frequency returns data may be used to describe contagion, or the spreading of shocks in price among assets. These data provide the experimental testing ground for our methodology. We study NYSE data from both the present day and one decade ago, examine the time scales over which the validated lagged correlation networks exist, and relate differences in the topological properties of the networks to an increasing economic efficiency. We uncover daily periodicities in the validated interactions, and relate our findings to explanations of the Epps Effect, an empirical phenomenon of financial time series. We also study bipartite community structures in networks composed of market returns and news sentiment signals for 40 countries. We compare the degrees to which markets anticipate news, and news anticipate markets, and use the community structures to construct a recommender system for inputs to prediction models. Finally, we complement this work with novel investigations of the exogenous news items that may drive the financial system using topic models. This includes an analysis of how investors and the general public may interact with these news items using Internet search data, and how the diversity of stories in the news both responds to and influences market movements

Peeling away the layers of news : climate change sentiments and financial markets

Unlike other news, climate news conveys the uncertainty around the substantial trajectory and the economic consequences of climate change. Since solutions to climate change are uncertain, unknown, or undesirable, climate change news may trigger counter-productive responses like denial, avoidance, and disagreement; thus, news on climate change becomes an excellent source for disagreement and uncertainty. This thesis examines the effect of climate disagreement and uncertainty sentiments on stock performances in the U.K. Based on a large sample of climate news with data set of 3,747,807 daily observations in the sample window from 2008 to 2019, the results from panel regression models show that both disagreement and uncertainty sentiments are positively associated with daily trading volume and future stock price volatility. The positive relations between disagreement and uncertainty sentiments with stock volatility are vital for firms operating in environmentally sensitive industries. Furthermore, disagreement and uncertainty sentiments induce significantly more positive trading volumes but less positive abnormal returns for firms without ESG scores than those who have ESG score available. I also propose and implement a procedure to hedge climate change risk in the second chapter dynamically. As I found in the previous chapter that sentiments in climate change news significantly impact stock performance, the thesis builds a portfolio model to hedge against these news innovations (i.e., news-based sentiment or climate change topics) as well as other national-level uncertainties. A mimicking portfolio approach is then used to build climate change hedge portfolios. I discipline the exercise using ESG performance and ESG report scores for firms in different industries to model their climate risk exposures. The thesis constructs an effective hedge portfolio to mitigate the risk posed by climate change and national-level uncertainties. Climate risk does not impact only the stock market but also the bond market. The green bond market has been growing swiftly internationally since its first introduction in 2007. One of the biggest challenges the green bond market faces is the “greenwashing” concern. Greenwashing exploits investors’ genuine environmental concerns, which create problems such as limiting investors’ ability to make actual environmentally friendly decisions or generating confusion and skepticism towards all products promoting green credentials, including those that are genuinely more environmentally friendly. Using a sample of green bonds from five countries spanning from 2007 to 2019, this study is the first empirical study that detailed environmental performance’s natural effect of green bond issuance by firms during 2007–2019, using propensity score matching and Difference in Difference model. Furthermore, the third chapter’s results show strong evidence that climate communication plays an essential role in firms’ commitment to improving their environmental footprint