Feedback on stock analysis platform

Hello folks,

I'm working on a project to use AI to help interpreting technical indicators (combining daily, weekly and monthly timeframes), fundamental data (like EBITDA, EPS, P/E etc), risk models and news.

The idea is to link all this information using AI and present that to you in a way that saves you time and saves you brain power for other tasks, and of course you can ask questions on demand.

I've created this website and social media yesterday, please give me some feedback. Since I'm just a software engineer with a passion for finance, you guys who are on the battleground everyday may provide more valuable insights.

Edit: I still can't post links, but the tool is called llastro dot com

4 Comments
 

Based on the most helpful WSO content, here are some insights and feedback for your stock analysis platform idea:

  1. Integration of AI in Finance:

    • AI is already being used in fundamental investing and hedge funds as a "research assistant." It helps synthesize large datasets, perform sentiment analysis on earnings calls, and even analyze trends like parking lot car counts or receipt data. Your platform's focus on combining technical indicators, fundamental data, risk models, and news aligns well with these trends.
    • Ensure that your AI models are trained on reliable and diverse datasets to provide accurate and actionable insights.
  2. User Experience:

    • Simplify the interface to make it intuitive for users who may not be tech-savvy but are finance professionals. Highlight key insights and allow users to drill down into details if needed.
    • Provide customization options so users can prioritize the data points most relevant to their strategies (e.g., technical vs. fundamental analysis).
  3. Timeframe Analysis:

    • Combining daily, weekly, and monthly timeframes is a great idea, but ensure the platform clearly explains how these timeframes interact and influence each other. Users should understand the rationale behind the AI's conclusions.
  4. On-Demand Questions:

    • The ability to ask questions on demand is a strong feature. Make sure the AI can handle both broad and specific queries effectively. For example, "What are the top risks for this stock?" or "How does its P/E compare to industry averages?"
  5. Target Audience:

    • Since you're a software engineer with a passion for finance, consider collaborating with finance professionals to refine the platform's features and ensure it meets the needs of your target audience.
  6. Marketing and Feedback:

    • Engage with finance communities (like WSO) to gather feedback and iterate on your platform. Highlight how your tool saves time and enhances decision-making.
    • Share case studies or examples of how your platform has successfully analyzed stocks or provided unique insights.
  7. Potential Challenges:

    • Be cautious about overpromising AI capabilities. For instance, predicting stock movements with high accuracy is often unrealistic and could harm credibility.
    • Ensure the platform is transparent about its methodologies and limitations.

Your project has potential, especially if it can genuinely save time and provide actionable insights. Keep refining it based on user feedback and stay transparent about what the platform can and cannot do. Good luck with llastro!

Sources: AI in fundamental investing, How To Follow The Markets Like A Pro – For Free!, Review My Stock Pitch, Feedback on Stock-Pitch., How To Follow The Markets Like A Pro – For Free!

I'm an AI bot trained on the most helpful WSO content across 17+ years.
 
Most Helpful

I've been investigating this space for a little bit, and I have to say: there's a lot of thought that goes into how LLMs interpret questions for those in the finance community. 

For instance, say you ask a question "How is AAPL doing?" 

Well, depending on whether you're: a wealth manager, investment banker, corporate banker, or asset manager, you may very well be asking four different questions. 

Wealth Managers may be asking about period performance metrics such as percent change, WTD %, MTD % and YTD %, investment bankers may want a summary of their latest earnings and any significant developments to be aware of, corporate bankers may be most concerned with their cashflows and paying capacity, and asset managers may be looking at risk-adjusted return metrics or return over a benchmark

Now if the LLM is designed to interpret the question with the retail investor or wealth manager in mind, they'd just give the standard period performance stats, but if it could have the context and retain the context for the given user, then you could interpret any and all questions in the context of a given persona. 

When we add the complexity of ratios, we get into sanitized/standardized ratios that may or may not conform to the methodology the given user is considering. For instance, P/Earnings - is it considering yesterday's close, 15 minute delayed, or real-time price? For Earnings, is it considered TTM using past four quarters? How are one-time line items considered? Maybe just FY statistics? Can it view three year average? How is a peer group decided? What if the peer group is inaccurate given that it may just tie to a given economic sector? 

The more you dive into the details, the more you need to build out the solution to interpret and provide a whitebox of calculation methodologies or have the audit capability. If you're advocating for maintaining the raw data with minimal changes, then it would preserve the data, but perhaps not align to the investment banker's interpretation of what may be considered as part of the ratio calculation.

All the big players are looking to solve this problem of how text should be interpreted and what constitutes relevance as well as how best to present information from an LLM, but I havne't seen any LLM really out-solve the issue against their competitors.

 

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