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Experimenting with GPT-3: Building an Investment Analyst

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Experimenting with GPT-3: Building an Investment Analyst

We have been experimenting with using GPT3's reasoning capabilities to build an investment analyst that does this : (1) Summarize relevant news of the last 30 days, (2) Write a SWOT analysis, (3) Identify competitors, (4) Identify Key Risks, and (5) Write three investment theses (bull, neutral, bear) Here is an example how this can look like: https://app.finclout.io/tp/MSFT https://app.finclout.io/tp/LYFT https://app.finclout.io/tp/AAPL https://app.finclout.io/tp/UBER So far, the results of getting reliable data back through well-engineered prompts through the API is quite promising. Yet, from an operational perspective, the biggest problem is that even though we are not using the free service but pay for it frequently run into rate limits. Positives: For Apple, the News Summary provided a useful result. "Apple Inc. is launching Apple Pay Later soon to revolutionize the buy now, pay later (BNPL) industry. Its stock is currently indicating a potential move to $167. The company is also facing an investigation by the Pomerantz Law Firm on behalf of iPhone 7 owners who experienced performance degradation following a device update. Apple's latest iPhones are being sold at discounts in China and the company is expected to rollout a high-end iPhone 'Ultra' in 2024. Additionally, two top market watchers discussed tech's wild run this year." For Microsoft, the Bull-case made more sense than the Bear-case Bull-case "Microsoft Corporation is a technology leader in the industry and is well-positioned to benefit from the increasing demand for cloud computing and artificial intelligence. The company's strong balance sheet, competitive advantages, and focus on innovation make it an attractive long-term investment Bear-case "Microsoft Corporation is a large, mature company with limited growth potential. The company is facing increasing competition from smaller, more nimble competitors and is struggling to keep up with the rapid pace of technological change. Investors should b e wary of investing in Microsoft due to its lack of innovation and potential for declining market share." Negatives: GPT3 is only guessing the next best word. Which might sometimes provide strange results. For example, considering the News Summary for Uber "Uber Technologies, Inc. reported a surge in fourth-quarter revenues and a strong outlook for 2021." We are not in 2021 anymore for a long while. Clearly, GPT3's knowledge of the Yahoo finance dataset has been cut off in 2021 and applied the provided news effectively. For AAPL, GPT3 did not identify any competitors. While we could argue that HP, MSFT, or similar hardware+software companies are in fact, competitors. In conclusion, we need to further investigate if the actual reasoning of GPT3 is mature enough to provide a useful analysis of the stock. Especially under liability considerations. Yet, if it works it could be a really powerful game-changer. Clearly, that is only an experiment and shouldn't be used as an investment recommendation nor seen as investment advice in any way.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
98%98% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: apple, new, using · Missing: mac, agents, macos
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue, growth, active · Missing: arr, mrr, profit
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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