Generative AI and large language models (AI) have finally broken into the mainstream. LLM-driven tools are transformational. They’ve revolutionized consumer products (auto-suggest, Google Translate, Grammarly) and now LLMs are coming for the investment research industry.
Artificial intelligence is an extremely powerful and effective tool for equity research. However, there’s no denying that we’re in the midst of a hype cycle. We made this list to help our friends, partners and users cut through the noise, and focus on what matters. Below you will find a list of the AI tools on the market with notes on their strengths and limitations.
Finance-specific tools using generative AI
Hudson Labs is a artificial intelligence-driven investment research software platform. Their core models are highly specialized, trained specifically to read/understand complex financial disclosure. For context, the company's base models were trained on 65 times more SEC filings than BloombergGPT.
Unlike generalist chatbots, Hudson Labs breaks down finance-specific tasks and tackles them with multiple specialist models.This allows Hudson to provide industry-leading accuracy and reliability by ensuring that their models are never put in a position where they are likely to fail. Learn about our in-house AI research breakthroughs here.
Compare AI performance on financial disclosure:
Read more here.
Hila provides question answering for specific documents e.g. for a specific earnings transcript or 10-K annual report. They cover a fixed number of companies so you don’t have to upload the document yourself. Their performance over earnings transcripts is substantially better than the annual filings, because of the smaller length of the documents.
What we like:
The context window is smaller (1 document) which limits hallucination.
They (mostly) give an error message when they cannot locate the answer in the document, instead of making up a plausible incorrect answer.
Hila is up front about what they can and cannot do well. They disclose that Q&A works much better for table and numeric retrieval rather than qualitative question answering.
The cons:
You have to know which document likely contains the answer you’re looking for, which limits usefulness.
No small cap coverage.
You can upload PDFs of filings or earnings transcripts and ask questions about it like ‘List all risk factors in bulleted form’ or ‘give a summary of all legal issues facing this company’. Docalysis provides the page numbers from which the output was extracted plus their library contains some 10-Ks pre-loaded.
Unfortunately, some of the answers are still factually incorrect and not in fact sourced from the document in question.
A fellow Toronto fintech, FinChat uses a combination of ChatGPT and langchain/llamaindex, similar to the tools above but is building for a much broader use case.
What we like:
FinChat is a great Q&A tool for pulling up financial information. FinChat pulls financial data and metrics from Strastephere.io and therefore, has more complete and accurate answers in this domain than the tools listed above.
The team seems to be iterating quickly so we expect improvements coming soon.
The cons:
Qualitative question answering suffers from error and hallucination. For instance, we asked about server useful lives at Alphabet and received an answer about Greenpeace.
ChatBiz is a financial chatbot, as the name implies. We haven’t tried it yet. You can test it here. Once you do, let us know what you think.
A note of caution on generative artificial intelligence
While generative AI tools have grown popular in automating a variety of tasks within investment analysis workflow, anyone who has used ChatGPT, BingChat etc for financial research will know that "hallucination" issues get worse with technical topics. Hallucination means that these tools invent plausible-sounding answers that have no grounding in reality. Generative AI hallucinations are hard to fix. Learn more about the limitations of generative AI and how they can be ovecome - A Comparison of Generative Chatbots to Hudson Labs.
More equity research tools using AI, natural language processing (NLP) and machine learning
Arteria AI uses AI to help with process documentation workflows and automation in financial services. Its AI platform unlocks operational speed and efficiency in areas like trading, lending, and asset management, among others.
Alkymi unlocks your investment and market data and gives you the tools to understand, transform and leverage it in your business.
Aiera AI is powering equity research with advanced earnings transcript tools such as one-click live audio access, real time transcription, keyword search, AI-powered Q&A, and advanced summarization.
Amenity Analytics provides an NLP platform that can be customized to extract and classify the information most relevant to you, capturing key business events, sentiment, context, and temporal relevance consistent with your research strategy.
Hudson Labs (formerly Bedrock AI) built by leading AI researchers and domain experts, Hudson Labs' proprietary finance-specific language models power earnings summaries, automated investment memos and more. Hudson Labs boasts industry-leading accuracy and reliability.
BlueFire AI combines financial statement analysis, behavioural profiling, market data analytics and NLU to provide signals of underperformance in the listed assets of public companies. They also use data visualisation to contextualize information.
Uptrends.ai uses language models to detect bullish vs. bearish sentiment indicators across stocks, topics, and market-wide.
We’ve been known to call other financial sentiment tools ‘purveyors of useless bullshit’ but we love Uptrends. Uptrend’s founders are computational linguists and computer scientists. They aren’t pretending to do lie detection (phew) and generally abstain from the insane claims that we’ve seen from other sentiment tools. We love their newsletter. It’s pure fun.
280First is the granddaddy of financial NLP tools, having been around since 2016. It is a financial services platform that analyzes unstructured data from various sources and produces insights from financial documents for investment professionals. There may be a human-review step in their process but we’re not sure about this.
Nosible AI makes it easy to demonstrate what your portfolio looks like and how it’s different/unique. They have visualization and data libraries.
Roic.ai provides ten years of financial statement data in one, easy to use screen. No clue how they use AI but, let’s be real, who cares.
New Constructs uses forensic accounting and technology to provide research, tracking and alerts for an unlimited number of tickers across 50 portfolios as well as screening and direct access to their databases. They also offer API & Excel Add-Ins.
Kailash Concepts seeks to exploit pricing errors that arise from herding, confirmation bias, and self-attribution bias among others. They offer a monthly research report as well as a free “quick takes” newsletter.
AlphaSense uses machine learning and NLP to offer finance-specific smart search as well as a number of linguistic and sentiment scoring offerings.
Sentieo (owned by AlphaSense) provides a searchable database of public and private company data, documents, and news from relevant sources, along with integrated modeling tools.
Boosted.ai uses finance-specific machine learning algorithms to identify patterns based on your unique inputs. They also highlight which features drove the machine learning decisions and outcomes. Another Toronto fintech!
Bloomberg, FactSet, CapIQ, Refinitiv: All of the major capital market research platforms use AI, ML and NLP, to some extent, in order to do information extraction. FactSet and Bloomberg also provide various sentiment and linguistic indicators.These tools use a human-in-the-loop methodology for information extraction, which improves the accuracy and reliability. Bloomberg recently published a paper on their generative LLM, “BloombergGPT” which isn't yet being used in production. Learn about how Hudson Labs models compare to BloombergGPT here (towards the bottom of the article).
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