AI for DIY-Investors

AI for DIY-Investors – is it a ‘Game Changer’?

In the past few weeks my Investment Research and Stock Screening have been significantly enhanced by the application of a variety of AI assisted techniques. I’ve been sharing my experiences with my fellow DIY-Investors, within diy-investors.com and have been encouraged to share my findings more widely… so here we are, starting diy-investors.ai to experiment and evaluate AI for DIY-Investing.

Here’s an overview of how I think AI can and in some instances is transforming areas of DIY-Investing (as at December 2024):

Advanced Stock Screening

AI-powered stock screeners have are already revolutionising the way investors filter and identify potential investments:

• Sophisticated Filtering: Using AI tools, it’s possible to process vast amounts of data to filter stocks based on hundreds of criteria, including fundamental ratios, technical indicators, and even sentiment analysis.
• Smart Synonyms Technology: Some advanced platforms, like AlphaSense, use semantic search capabilities to understand investor intent, delivering results that match not just exact keywords but also related concepts.
• Real-Time Updates: AI screeners can continuously update their results as market conditions change, providing investors with the most current information… but note that for the most part, these are paid subscription sevices.

Comprehensive Data Analysis

AI excels at processing and analyzing large volumes of financial data:

• Multi-Source Integration: AI tools can aggregate data from various sources, including financial reports, news articles, social media, and alternative data sets.
• Pattern Recognition: Machine learning algorithms can identify trends and patterns in stock behavior that might be imperceptible to human analysts.
• Predictive Analytics: By analyzing historical data and current market conditions, AI can forecast potential stock movements and market trends.

Sentiment Analysis

AI-driven sentiment analysis has become a crucial component of investment research:

• Natural Language Processing (NLP): AI systems can analyze text from news articles, social media posts, and financial reports to gauge market sentiment towards specific stocks or sectors.
• Real-Time Sentiment Tracking: These tools can provide up-to-the-minute insights on how public opinion might affect stock prices.
• Sentiment-Based Alerts: DIY-Investors can set up notifications for significant changes in sentiment that could impact their portfolio(s).

Automated Research Reports

AI is streamlining the creation of investment research reports:

• Earnings Summaries: Tools like Hudson Labs can generate automated summaries of earnings reports, saving investors time in digesting crucial financial information. However, I believe that there are many ‘use cases’ for us DIY-Investors to achieve much the same thing for our own stocks.
• Customized Reports: AI can produce tailored research reports based on an investor’s specific interests and portfolio holdings… this is particularly useful, when companies that you hold release a stock market announcement (RNS, or Edgar filing in the US).
• Continuous Updates: Unlike traditional research reports, AI-generated reports can be updated in real-time as new information becomes available.

Valuation Models and Financial Modelling

AI enhances traditional valuation techniques:

• Automated Valuation: Platforms like Finbox (and others) offer AI-powered valuation models, including discounted cash flow (DCF) and comparable company analysis. Whether this is of use, depends on your individual philosophy about ‘black box’ valuation methods and the reliance placed on them.
• Scenario (what-if) Analysis: AI can rapidly run multiple scenarios to assess how different factors might impact a stock’s value.
• Custom Model Building: Some tools allow investors to create and backtest custom financial models using AI to optimize parameters. However, in my opinion, all models that use ‘back-testing’ seem to ignore the human element of emotion in investing.

Summary

Overall, I strongly believe that by leveraging some of these AI-powered tools for our investment research and stock screening, we can make a real difference to our analysis capabilities. In doing so, we can potentially level the playing field with professional investors. However, I feel that there are many ‘simple’ AI applications that we can implement that will make a difference to us. If we use AI wisely, we can get valuable insights, in a timely fashion. If you want to maximise the benefits of AI, then use it in conjunction with sound human judgment and a thorough understanding of investment principles.

I look forward to sharing my AI journey with you, as I add different tools & techniques to the “Three Pillars of DIY-Investing” that I’ve been using over many years. I’ll document what works and what doesn’t and I look forward to receiving any suggestions or observations.

Mick Pavey (17th December 2024)