Today’s financial markets are inextricably linked with financial events like acquisitions, profit announcements, or product launches. Information extracted from news messages that report on such events could hence be beneficial for financial decision making. The ubiquity of news, however, makes manual analysis impossible, and due to the unstructured nature of text, the (semi-)automatic extraction and application of financial events remains a non-trivial task.
News items are major drivers for asset prices, maybe more so than conventional price and economic data. Yet it is impossible for any financial professional to read and analyze the vast and growing flow of written information. This is becoming the domain of natural language processing; a technology that supports the quantitative evaluation of humans’ natural language.
“Natural language processing converts various texts (unstructured) into an easier-to-use format (structured). In a structured form, one can more easily use texts in the investment process.” NLP can extract useful data elements from unstructured, raw data. Using language- and grammar-specific constructs, it builds on a unique combination of algorithms and artificial intelligence tools to analyze, extract, and classify human communications from unstructured data
The motivation for using NLP in the financial markets is supported by recent literature which has focused much effort on the use of news-derived information to predict the direction of movement of a stock. Studies show that information extracted from news sources is better at predicting the direction of underlying asset volatility movement, or its second-order statistics, rather than its direction of price movement.
NLP has proved to be a useful tool for information retrieval and the classification of financial statement content. Studies have shown that NLP can be used to significantly reduce the manual processing required to retrieve corporate data from news articles, financial reports, press releases, and social media.
NLP and other technologies transfer the traditional jobs in the financial industry and by expanding to a more tech-savvy job description, for example
“Who are we looking for?
The ideal candidate will be an expert in Natural Language Processing, through academic or industry experience (or both).
You will have experience applying machine learning and deep learning methods to a range of NLP-related tasks, such as Named Entity Recognition, Entity Linking, Sentiment Analysis and Text Classification.
Experience working with existing NLP and deep learning libraries (word embeddings, spaCy, CoreNLP, NLTK, PyTorch / TensorFlow / keras, etc.).
Familiarity with the many recent exciting advances in NLP (pre-trained Language models such as BERT, contextualised word embeddings such as ELMo, Attention and novel neural network architectures, etc.).
You will have an interest in applying mathematical and NLP concepts to real-world financial problems, and implementing theoretical insights as working code.
Previous financial experience is not required, although interest in finance and the motivation to rapidly learn more is a prerequisite for working here.”
Based on this job description as other similar job offerings, we can see that previous financial experience is not mandatory while key competencies in programming are considered the top factors for hiring.
NLP and other technologies are disrupting the financial industry and we are in a tipping point of industry major changes, and for the better…
Using big data and NLP technologies to capture alpha by collecting, structuring, and revealing events from news articles, press releases, and financial social media.
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