Large language models (LLMs) 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. 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. We’ve included a list of some of the AI tools on the market with notes on their strengths and limitations.
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 to work around them here - A Comparison of Generative Chatbots to Bedrock AI.
Finance-specific tools using generative AI
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.
They’re 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.
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.
FinChat.io from Stratesphere.io
A fellow Toronto fintech, FinChat launched a few weeks ago and created a lot of excitement on the interwebs. 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 founders are cool ;) and seem to be iterating quickly.
Qualitative question answering suffers from error and hallucination. For instance, we asked about server useful lives at Alphabet and got back 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.
Bedrock AI is a artificial intelligence-driven investment research software platform. Our core language models are highly specialized (they are trained specifically to read/understand complex financial disclosure). Our in-house models specialise in computational “understanding” instead of generating text. For context, our models were trained on 65X more SEC filings than BloombergGPT.
We use generative AI for narrow tasks, where we can maintain 100% factuality. We ensure we never put our models in a situation where they're likely to fail.
Comparing performance - find the details of here.
Learn about our in-house NLP-breakthroughs here.
More equity research tools using natural language processing (NLP) and machine learning
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.
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.
Nosible AI makes it easy to demonstrate what your portfolio looks like and how it’s different/unique. They have visualization and data libraries.
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.
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!
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.
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.
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 our models compare to BloombergGPT here (towards the bottom of the article).
Last updated: Nov 15, 2023
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