How AI And Technology Are Changing Value Investing

Introduction: The New Frontier in Value Investing

Value investing has always been a tedious work. Countless hours spent in reading annual reports, scouring SEC filings and tracking earnings calls before you can even sit down to condense your insights and formulate a plausible thesis for or against an investment.

In my experience, the work breaks down into a few distinct stages:

Screen for potentials and create a shortlist
Conduct due diligence – this is where the tedium comes in, and,
Formulate a thesis

If I have to venture an estimate, about 90% of my time is spend in phase #2, conducting due diligence.

With new technology, specifically AI, this particular phase can be drastically sped up.

Investors who leverage AI can process vast amounts of information faster and more efficiently than ever before. In this article, you’ll see how AI and technology are revolutionizing value investing and how you can use these tools to gain a competitive edge.

AI-Powered Research: Accelerating Data Analysis

As a value investor you are aware of the sheer number of hours you spend reviewing financial reports, earnings transcripts, industry trends, press releases and news stories. There was a time when I had to find a news story from 1950s to understand the terms of an acquisition. You will still continue to perform the data retrieval part of the process, but AI can now allow you to process and extract insights from massive datasets in seconds.

Tools like Notebook LM enable investors to create customized folders with financial reports, news articles and press releases allowing instant data retrieval through natural language queries. You can add upto 50 data sources in one folder, and these can be a mix of urls, pdf documents, text you type in, etc. It can also generate an audio podcast with 2 AI hosts that summarize and discuss the main gist of all the documents in the folder in a fun way – which is very nice as a way of getting an audio overview while you are working out, for example.

I have used Notebook LM in my research/analysis process and I am exceedingly pleased with the results. The AI is limited to the data that is available in the folder, and therefore the chances of hallucinations are minimized. It is still a good idea to double check. The goal is to use it as a tool to help you with the analysis, not use it as your only source of analysis.

You could for example list a few years worth of annual reports, recent press releases, quarterly reports, earnings call transcripts, etc. You can start by asking some basic questions about the company history, recent financial performance, competitive landscape, management’s strategy, etc. You can then listen to the generated podcast to get the AI Summary of the data that you have in front of you. Write down any questions that occur to you while you are listening. For example, changes in key metrics across multiple years, how did a new sales campaign perform, etc. You can come back and ask these questions to the AI. The AI answers cite the documents it pulled the insights from so you can always cross-reference and double check.

All this should go into the thesis you are building outside of the AI. Yes, the Investment Thesis is your own. With AI streamlining data collection and aiding insight generation, you can now focus ore on interpretation and strategic decision making.

Machine Learning and Financial Modeling: Smarter Valuations

Notebook LM can do some of this but it could be a nice project to build using some of the newer reasoning models. You can create machine learning models to detect patterns in financial statements that may be invisible to human analysts. For example, there may be certain patterns that have historically lead to outsized gains – we don’t yet know what these patterns are. I am reminded of all the different factors of stock returns that we keep discovering.

AI-powered stock screeners have started to pop up. These screeners should be able to include more than simple valuation ratios, such as sentiment analysis, earnings trends and macroeconomic signals, to generate recommendations. You could potentially use AI models to backtest your strategy/thesis to get ore insights.

The field is rapidly evolving and the potential for the investors to be early adopters is vast.

Real-Time Market Intelligence: AI as an Early Warning System

AI can scan thousands of news sources, earnings call transcripts, and analyst reports in real-time, detecting signals before they become obvious to the broader market. There are various sentiment analysis AI tools that are now available that can be used to analyze the sentiment on earnings calls, detect management tone shifts, etc. to help investors get a sense of whether the management is being honest or upfront about issues. I have spend countless hours listening to the management calls trying to sense what the management actually feels or believes in. This information can be as important as insider selling (or buying), earnings revisions, etc.

AI can also track in real time all the filings being made every day by 1000s of public companies, and surface those that fit your criteria (watchlist, opportunity, etc).

While AI aids in research, investors must still apply judgment and skepticism when interpreting AI-generated insights.

AI Limitations and the Role of Human Judgment

Despite its power, AI is not infallible—models can be biased based on training data, and machine-generated insights lack the qualitative judgment of experienced investors.

Example: AI may flag a stock as undervalued based on historical financials but fail to recognize strategic shifts, leadership changes, or industry disruption.

Value investors must continue using critical thinking to differentiate between quantitative signals and qualitative business fundamentals. However, this doesn’t mean you should eschew AI tools altogether! Wherever these tools help you to capture more insights, more efficiently, and save you time and give you an edge in the market, you should absolutely incorporate them in your workflow. With AI becoming more embedded in investing, it’s crucial to stay ahead of emerging tools and trends.

The Future of AI in Value Investing

The next wave of AI-powered investing will likely integrate predictive analytics, automated portfolio construction, and advanced risk management. AI could democratize deep fundamental analysis, giving individual investors tools previously reserved for hedge funds and institutions. Experiment with AI-driven research tools like Notebook LM supplementing your current research tools like Stock Rover. Use AI to enhance, not replace, your existing investment process. With experience you will find a good workflow that works for you with correct balance of AI assistance and your own human judgement and experience.

Embrace AI as a Competitive Advantage

AI and technology is not a threat. It is not replacing value investing, but in fact it is enhancing it. If you adopt AI-driven tools, you can process information faster, uncover hidden insights and refine your strategies quicker. If you use it, you will gain an edge in the market. If you don’t you will fall behind. The key is to integrate AI into your workflow in a way that maintains the discipline of your value investing principles.