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

Analysis of US Airline Stocks Performance Using Latent Dirichlet Allocation (LDA)

The primary objective is to measure the impact of US airlines’ stock performance following aviation related news announcements of differing sentiment and/or topics. The categories which account our data for aviation news are fuel prices, interest rate, inflation, airfares, foreign exchange rates, capital expenditures, and growth in output. The amount of such data and documents is expected to be enormous. So, we use a natural language processing, Latent Dirichlet Allocation (LDA) to investigate and search for patterns that can explain the movement of US airline stock. First, we will mine the aviation related data through text mining and topic modeling. Second, we will employ the LDA model approach to help us identify and capture the extent of certain topics mentioned in aviation news releases. Finally, we will use Event Study Methodology (ESM), which is a common method used to investigate stock price reactions to news announcements. We will apply this method to discover the significance of the relationship between the stock return and the associated event. Findings of this research will help stakeholders or investors understand the relationship between news of the aviation related events and the stock returns of the US airlines

ALGA: Automatic Logic Gate Annotator for Building Financial News Events Detectors

We present a new automatic data labelling framework called ALGA - Automatic Logic Gate Annotator. The framework helps to create large amounts of annotated data for training domain-specific financial news events detection classifiers quicker. ALGA framework implements a rules-based approach to annotate a training dataset. This method has following advantages: 1) unlike traditional data labelling methods, it helps to filter relevant news articles from noise; 2) allows easier transferability to other domains and better interpretability of models trained on automatically labelled data. To create this framework, we focus on the U.S.-based companies that operate in the Apparel and Footwear industry. We show that event detection classifiers trained on the data generated by our framework can achieve state-of-the-art performance in the domain-specific financial events detection task. Besides, we create a domain-specific events synonyms dictionary

Analysis of US Airline Stocks performance using Latent Dirichlet Allocation (LDA)

The primary objective is to measure the impact of US airlines’ stock performance following aviation related news announcements of differing topics. Our data account for aviation news of airlines, airports, regulations, safety, accidents, manufacturers, MRO, incidents, aviation training, general aviation, and others from Aviation Voice. We use a natural language processing, Latent Dirichlet Allocation (LDA) to investigate and search for patterns that can explain the movement of US airline stock. First, we mine the aviation related data through text mining and topic modeling. Second, we employ the LDA model approach to help us identify and capture the extent of certain topics mentioned in aviation voice news releases. Finally, we use multiple regression models to investigate stock price reactions to news announcements. Eventually, we succeed in extracting 10 topics. As hypothesized, the impact of those topics varies greatly from topic to topic. Some of the topics have effect on the US Airline stocks in the short and long terms moving average while other topics have only effect on the medium-term run

Analysis of US Airline Stocks performance using Latent Dirichlet Allocation (LDA)

The primary objective is to measure the impact of US airlines’ stock performance following aviation related news announcements. Our data account for aviation news of airlines, airports, regulations, safety, accidents, manufacturers, MRO, incidents, aviation training, general aviation and others from Aviation Voice. The amount of such data and documents is expected to be enormous. We use a natural language processing, Latent Dirichlet Allocation (LDA) to investigate and search for patterns that can explain the movement of US airline stock. First, we mine the aviation related data through text mining and topic modeling. Second, we employ the LDA model approach to help us identify and capture the extent of certain topics mentioned in aviation voice news releases. Finally, we use Event Study Methodology (ESM), which is a common method used to investigate stock price reactions to news announcements. We apply this method to discover the significance of the relationship between the stock return and the associated event. Eventually, we succeed in extracting 10 topics. As hypothesized, the impact of those topics varies greatly from topic to topic. Some topics have no significant effect on US Airline stocks while others such as aviation fuel price and air travel demand have significant effect

Understanding the Relationship between Online Discussions and Bitcoin Return and Volume: Topic Modeling and Sentiment Analysis

This thesis examines Bitcoin related discussions on Bitcointalk.com over the 2013-2022 period. Using Latent Dirichlet Allocation (LDA) topic modeling algorithm, we discover eight distinct topics: Mining, Regulation, Investment/trading, Public perception, Bitcoin’s nature, Wallet, Payment, and Other. Importantly, we find differences in relations between different topics’ sentiment, disagreement (proxy for uncertainty) and hype (proxy for attention) on one hand and Bitcoin return and trading volume on the other hand. Specifically, among all topics, only the sentiment and disagreement of Investment/trading topic have significant contemporaneous relation with Bitcoin return. In addition, sentiment and disagreement of several topics, such as Mining and Wallet, show significant relationships with Bitcoin return only on the tails of the return distribution (bullish and bearish markets). In contrast, sentiment, disagreement, and hype of each topic show significant relation with Bitcoin volume across the entire distribution. In addition, whereas hype has a positive relation with trading volume in a low-volume market, this relation becomes negative in a high-volume market

Determining Lead-Lag Structure between Sentiment Index and Stock Price Returns

This research contrasts and compares the state-of-the-art techniques of the two approaches within the domain of news sentiment analysis, as well as, investigates a novel document encoding representation of the `TF-IDF momentum matrix'. The presented lexicon-based methodology is centred around Loughran & McDonald financial sentiment word lists and reaches 86.4% explained stock momentum variance, whereas the classification approach follows a thematic analysis pipeline implementing Latent Dirichlet Allocation and achieves that of 94.8%. As an additional element of model evaluation, the research implements Thermal Optimal Path method which relies on a dynamic programming approach for performance optimisation