by Shubhi Gupta
Financial events are pivotal in shaping market sentiment and influencing investment decisions. In finance, professionals and investors rely heavily on the latest news to keep abreast of changing market dynamics. The ability to quickly and accurately classify financial events can mean the difference between success and failure in the fast-paced world of stock market trading.
Artificial Intelligence can be used to interpret current news and tweets about an organization to help investors make informed decisions.
News/Tweets ∝ Stock Market
The impact of news and tweets on the stock market cannot be overstated. A single positive or negative event can trigger a chain reaction of buying or selling that can dramatically impact stock prices. News articles provide a succinct summary of recent events related to a company or the economy, as well as the potential effects that these events may have on market performance. Tweets communicate opinions about the steps taken by an organization among the general public. In this way, stock prices reflect not only a company’s current performance but also the market’s perception of its prospects.
A positive market sentiment regarding a company formed due to a good earnings report, an announcement of a new product, a corporate acquisition, and positive economic indicators can translate to its stock price rise. On the other hand, a bad earnings report, a lapse in corporate governance, big-picture economic and political uncertainty, and unfortunate occurrences could create a negative sentiment and can even see the company’s worth being wiped out in a day.
But the problem is not about where or how to find such news and tweets to read as you can easily find them published at sites like BusinessInsider, BusinessToday, Twitter, etc. The problem is that the sheer volume of financial events available can make it challenging for traders to identify and analyze relevant events promptly.
Discovering a reliable approach to navigating the seemingly chaotic stock market may offer investors more consistent and stable returns, thereby reducing the perception of the market as a form of gambling.
What is NLP and why use it for Market Analysis?
Artificial Intelligence(AI) has taken the world by storm. Its applications cover a wide variety of problems and one such application is Natural Language Process(NLP). NLP encapsulates a range of computational techniques for analyzing and representing naturally occurring texts at one or more levels of linguistic analysis to achieve human-like language processing for a range of tasks or applications. There are many helpful natural language tasks such as keyword extraction, summary generation, and sentiment analysis to extract relevant information from large texts and display it to the user in a concise manner.
Investing without thorough research can lead to a series of losses. Professional traders dedicate a significant portion of their time attempting to predict upcoming news cycles to execute stock trades in advance of official data releases.
Feeding data from government economic reports, company and industry news, market indicators, social media posts, etc., into NLP models, can help one to process huge amounts of events happening all around in less time and hence provide more time and information to investors to help anticipate and crunch down the numbers.
Financial events analysis includes three main tasks:
- Event Detection: Sifting through a vast ocean of information and detecting what events are relevant, worth analyzing, and may provide viable data about a specific organization’s day-to-day working status.
- Event Classification: Classifying events into appropriate finance categories. This step clusters the events talking about the same topic together so that the investors can check out events of the category that interests them.
- Event Summarisation: When stock prices fluctuate with each passing moment, you need a short crisp summary of events to quickly and efficiently process the relevant information.
Events Classification
Now that we have established that there is a strong interplay between financially related news and tweets and the financial market, it has become an indispensable step to classify these massive internet financial events.
Financial events are often classified to help people make sense of the vast amounts of information that are available about the financial markets and the economy. Here are a few reasons why financial events are often classified:
- To provide context: It provides context for complex financial news, making it easier to understand for those unfamiliar with financial terms and concepts.
- To identify trends: It enables traders to identify trends and patterns in the financial markets and the economy, allowing them to make informed predictions about future market movements.
- To prioritize information: It helps prioritize information, allowing traders to focus on news that is most relevant to their investments.
- To inform investment decisions: It informs investment decisions, providing crucial information about investment opportunities and the risks and rewards of different investment options.
Classification of financial news and tweets for focused reading and analysis as seen in a newsletter published by AlgoFabric (https://algofabric.algoanalytics.com/dashboard)
Models for Events Classification
Natural Language Processing (NLP) provides a diverse range of tools that can be effectively utilized for the task of event classification. From traditional machine learning models such as LinearSVC and RandomForest to more advanced models such as XGBoost, DistilBERT, and BERT, there are numerous options available for accurate event classification.
- LinearSVC: It can be a good option because it is capable of learning linear decision boundaries between the different labels, which makes it effective in dealing with high-dimensional feature spaces.
- Random Forest Classifier: It is an ensemble machine learning model that can be used for multilabel text classification because it can handle high-dimensional feature spaces and is less prone to overfitting, making it effective in dealing with complex classification tasks.
- XGBoost: It is designed to handle large datasets and can automatically learn the importance of each feature, making it effective in dealing with high-dimensional feature spaces and improving the accuracy of the predictions.
- DistilBERT: It is a pre-trained transformer-based neural network that can be fine-tuned for multilabel text classification tasks. It can capture complex relationships between words and handle large datasets, making it effective in dealing with a wide range of text classification tasks.
Source: AlgoFabric(https://algofabric.algoanalytics.com/dashboard)
While LinearSVC and RandomForest are generally effective machine learning models for multilabel text classification, DistilBERT, a pre-trained transformer-based model, showed even better results, outperforming them in classifying real-time news and tweets. It provided fast and efficient computation, as well as high accuracy and generalization capabilities. Its smaller size and faster training and inference make it a more practical choice for many applications, while its transfer learning capabilities allow for fine-tuning on specific tasks with smaller amounts of labeled data.
AlgoFabric-Your stock investment assistant
https://algofabric.algoanalytics.com/
AlgoFabric uses the latest state-of-the-art artificial intelligence technologies to improve one’s investing experience. It is a one-stop destination for all the financial news and sentiment trends about Nifty stocks.
AlgoFabric implements event classification on its website as well as its daily newsletters dedicated to each stock. In addition to Event Classification, information like Public Sentiments, Trending Topics, News Summarization, Analytical Insights, Quarterly / Annual Reports, and much more are provided. One can also get daily updates on events, trends, analysis, and prices, all mailed right into their inbox in the form of newsletters dedicated to their favorite stocks.
In short, one can get a 360-degree view of the stock market and make an informed decision using the expertise and technology that AlgoFabric provides.
Conclusion
In conclusion, the ability to classify financial events quickly and accurately is crucial for success in the stock market.
Classifying events helps investors and analysts in identifying patterns and trends over time. By analyzing and categorizing events such as Mergers & Acquisitions, Investments & Joint Ventures, and Products & Services, etc., investors can gain insights into the potential impact of these events on the stock market.
NLP offers powerful tools and solutions for traders seeking to make informed investment decisions based on the latest financial news. By staying updated on relevant news and social media posts and analyzing them in real time, traders can improve their chances of achieving sustainable returns and minimizing the risks associated with stock market trading.
References:
- HuggingFace community: https://huggingface.co/
- HuggingFace and Microsoft’s DistilBert: https://huggingface.co/docs/transformers/model_doc/distilbert
Investopedia: https://www.investopedia.com/
