Data is being generated at an exponential rate, and the technology powering the algorithms used to parse it is growing just as fast, opening up new opportunities for investing and innovative ways to leverage alternative data. Alternative data differs from traditional data like financial statements and is often in an unstructured format like news articles, thus requiring more sophisticated algorithmic methods to gain insights. The explosive growth of unstructured data has reshaped the way investment professionals think about the sources of information that go into their investment process.
Investors seeking to use unstructured and alternative data are confronted with the buy or build dilemma, facing the decision to either purchase vendor data or invest in developing in-house data science capabilities. In recent years, the field of natural language processing (NLP) has witnessed remarkable advancements largely attributed to three factors; the development of more sophisticated algorithms, the availability of more extensive and diverse datasets, and the exponential growth in computational power.
As the use of unstructured and alternative data has grown increasingly important, the rise in use of deep learning algorithms to extract value from such data has grown in step. NLP, a form of deep learning, has been particularly useful in finance as so much of the perceived value in investing comes from interpreting textual data.
The investment industry is, by its very nature, a confidential arena. Financial institutions, investment firms, and individual traders often hold close their trading strategies, market analyses, and proprietary algorithms. This guarded approach extends to data, where exclusive data sources and proprietary databases can often provide an edge.
Platforms like ChatGPT are sited as democratized access to powerful large language models (LLMs), shifting the conversation around the future of investing. The introduction of generative AI (GenAI) may signal a new phase of the AI plus human intelligence.
Natural language processing, the subfield of machine learning (ML) that parses our spoken and written language and encapsulates AI (like ChatGPT), is particularly suited to dealing with many of these alternative and unstructured datasets due to the value ingrained in the narratives around the information in financial reports. Fine-tuning these powerful models on proprietary data can provide more value than what the underlying models provide in isolation.
The democratization of access to cutting-edge tools and resources, the explosion of unstructured data, and the open-source community’s collaborative spirit have created a fertile environment for innovation and discovery. As the data depth and breadth continue to grow and new technologies emerge, investment professionals are now increasingly embracing a more holistic and scientific approach to investing to stay ahead.
In response to an ever-growing demand for diverse and company-specific insights, we at Bitvore have expanded our data coverage to include a broader range of both public and private companies to over 580,000 entities, adding circa 150,000 entities in 2023. Over 500,000 companies on our database are now matched to DUNs numbers.
Bitvore now also offers enriched data at the article and entity level that includes advanced tagging for people, industry, key phrases, Geo Lat/Long, entity relationships, plus many more. Additional enriched data fields include; document sentiment, topic vector, key phrases, relationships, salience and confidence in addition to advanced entity extraction. These enhanced capabilities provide deeper data and gives greater insights into complex relationships.
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