Overview of quantitative news interpretation methods applied in financial market predictions

This paper describes currently known methods of quantitative news interpretation applied in financial market predictions. Brief summaries are made regarding all the listed methods of automatic news interpretation, some commercial applications are mentioned and finally a conclusion is drawn about the usability and prospects of quantitative news analysis with statistical machine learning methods. The aim of this paper is to provide an overview on the related research activities performed so far and explore further research directions to improve the predictive capability of currently known methods

Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models

We examine the potential of ChatGPT and other large language models in predicting stock market returns using news headlines. We use ChatGPT to assess whether each headline is good, bad, or neutral for firms' stock prices. We document a significantly positive correlation between ChatGPT scores and subsequent daily stock returns. We find that ChatGPT outperforms traditional sentiment analysis methods. More basic models such as GPT-1, GPT-2, and BERT cannot accurately forecast returns, indicating return predictability is an emerging capacity of complex language models. Long-short strategies based on ChatGPT-4 deliver the highest Sharpe ratio. Furthermore, we find predictability in both small and large stocks, suggesting market underreaction to company news. Predictability is stronger among smaller stocks and stocks with bad news, consistent with limits-to-arbitrage also playing an important role. Finally, we propose a new method to evaluate and understand the models' reasoning capabilities. Overall, our results suggest that incorporating advanced language models into the investment decision-making process can yield more accurate predictions and enhance the performance of quantitative trading strategies.Comment: Previously posted in SSRN https://papers.ssrn.com/sol3/papers.cfm?abstract_id=441278

Mechanically Extracted Company Signals and their Impact on Stock and Credit Markets

I analyze company news from Reuters with the 'General Inquirer' and relate measures of positive sentiment, negative sentiment and disagreement to abnormal stock returns, stock and option trading volume, the volatility spread and the CDS spread. I test hypotheses derived from market microstructure models. Consistent with these models, sentiment and disagreement are strongly related to trading volume. Moreover, sentiment and disagreement might be used to predict stock returns, trading volume and volatility. Trading strategies based on positive and negative sentiment are profitable if the transaction costs are moderate, indicating that stock markets are not fully efficient.Content Analysis, Company News, Market Microstructure

Incorporating Fine-grained Events in Stock Movement Prediction

Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement prediction. Firstly, we propose a professional finance event dictionary built by domain experts and use it to extract fine-grained events automatically from finance news. Then we design a neural model to combine finance news with fine-grained event structure and stock trade data to predict the stock movement. Besides, in order to improve the generalizability of the proposed method, we design an advanced model that uses the extracted fine-grained events as the distant supervised label to train a multi-task framework of event extraction and stock prediction. The experimental results show that our method outperforms all the baselines and has good generalizability.Comment: Accepted by 2th ECONLP workshop in EMNLP201

Long- and Short-Term Impact of News Messages on House Prices: A Comparative Study of Spain and the United States

This study adopts data mining methods to analyze the short- and long-term dynamic between news message content and property prices in Spain and the United States. We construct news sentiment indices based on various text mining methods which exhibit remarkable similarities to the respective property prices. Comparing dictionary-based and dynamic approaches, our results indicate that static methods produce the best estimators for investor sentiment in real estate markets. Using a Vector Error Correction Model framework, we analyze similarities between real estate markets both in the short- and long-run. The main finding of this study is a significant relationship between news messages and property prices in the long-run. Our results are stable, including a number of fundamental variables, and are underlined by forecast error variance decompositions and impulse response estimates, which additionally highlight the appropriateness of news sentiment as a crucial determinant for decision making