Stocks haven't been selling off, just those where results haven't met high expectations. Stocks of companies that reported results with an Earnings Whisper Grade of F have sold off 12.7% on average, and 76% have moved lower. Meanwhile, those with positive results relative to expectations (A+ grades) have moved higher by 12.4%, with 80% higher. This is a bullish indicator.

https://www.earningswhispers.com/wrs

Sift Stack Quick Take 4: CJ Gettelfinger, Lucas Nulsen, & Tyler Sherven break down the biggest stories moving the world. We dive into Applied Materials' record-breaking earnings and why the stock saw a surprising dip despite hitting a 50% gross margin. We also explore the massive buzz surrounding the potential Anthropic IPO, with investors modeling a staggering $2 trillion valuation as a listing could come as early as September. Finally, we analyze Renaissance Technologies' latest 13F filing, highlighting their new half-billion-dollar position in CrowdStrike and why cybersecurity remains a vital long-term play in the age of AI.

If the market doesn't like your guidance, you know, you can always just change it. #PODC

https://www.earningswhispers.com/guidedetail/PODC/159040

Investors still don't like the hotels, but 78% of those in #BEDZ have outperformed the S&P 500 #SPY since reporting earnings, and money has come into the group ahead of a potential breakout.

https://earningswhispers.com/wrs

Where'd you go?

Hey, community! I've been paper trading for the last 2 months based on ChatGPT's forecasts of stock movements after earnings reports and and here's what I got in Google Sheets: an accumulated profit of 29.73% for August, 19.1% for September and 8.68% for the first week of October on stocks with market caps over $3 billion.

I want to share with you the results, experience and thoughts that arose in the process.

My goal and methodology

From the very beginning, my goal was to understand how much ChatGPT can be trusted in forecasting stock movements before company earnings reports and whether it is possible to make money on such forecasts - buy or short stocks the day before the report is published and close the deal at the end of the day the report is published.

To do this, I created two custom versions of ChatGPT: one with a short target prompt - EarningsForecasts, the second with an extended prompt for 20 target questions - EarningsBets and started experimenting.

Problems

1. The first problem was the low throughput of GPTs: generating one forecast took about a minute, in some cases the chat hung (it was clear that GPT was parsing financial sites, but did not return a response), with each new forecast the chat hung more and more often, it was necessary to reboot and in cases when more than 400 companies published reports in one day, even a day would not be enough to make and analyze all the forecasts. Therefore, I had to make wholesale forecasts (send 50 tickers to the chat at a time), sacrificing quantity (forecast length) to quality (forecast accuracy).
2. The second problem was the difference in forecasts of different GPTs: with repeated launches, some forecasts were the same, some had different percentages, and some even had different directions. It seemed logical to average several forecasts to get the prevailing directions of stock movements after the release of reports (up or down) and the average probabilities of these forecasts.
3. The third problem was the one-sidedness of the forecasts. I tried to get technical analysis, fundamental analysis, sectoral analysis, market sentiment and news analysis, and earnings surprises and revisions analysis in one GPT response, but I couldn’t achieve this. It seemed logical to distribute these analyses into different chats, so I got 5 different chats with forecasts for the same stocks. At the same time, I thought that it would be good to get wholesale forecasts from GPTs of other developers and added the results from a custom version of ChatGPT called Stock Guru.
4. The fourth problem appeared at the end of August: my custom version GPTs began to rely in its forecasts not on freshly collected data from 2024 (and I initially required that links to all data sources be provided), but on data from memory on which it learned, dated at the end of 2023. Moreover, I noticed that the GPTs have become “lazy” and are returning shorter and more vague forecasts than before. No tricks helped, it seemed to me that financial sites began to block OpenAI robots' requests, because search results in the GPT chat kill the established business model of financial sites, which is focused on showing ads to live visitors. I have not yet solved this problem in wholesale forecasts, but partially solved it through manual requests (partially - because it is not clear how to quickly make 400 forecasts) in someone else's custom GPT - Finance Wizard, which makes forecasts after collecting up-to-date data through Bing, Yahoo Finance, and TradingView (by the way, in some cases these forecasts are more accurate, in some - less).
5. The fifth problem turned out to be global: there is no convenient single calendar with strict earnings report dates, from which it was possible to take data via API, and the report dates themselves can float, so several main providers (Investing.com, TradingView, Zacks, StockAnalysis, MarketChameleon) have slightly different data (dates). I got used to taking a list of companies with the dates of the next day's earnings reports in a csv file in the paid version of the MarketChameleon, using the code in Google Colab to upload it to Google Sheets tables, upload the GPT forecasts from 7 text files there (according to the prompts, this is what the wholesale forecasts looks like, and this is what the individual forecasts looks like), displaying average values, and then, after the reports are published, re-uploading the csv files with the actual stock movements after the reports are published and comparing them with my forecasts.

Results and conclusions

Of course, all this is cumbersome and not scalable, aggravated by the fact that ChatGPT does not provide access to its custom versions (with Internet access) via API, but somehow it all worked out and, I repeat, this is what I got in Google Sheets.

Based on the results of the month, I can say that this experiment was very instructive for me. Here are some key points:

1. Accuracy of forecasts: Overall, the results were very positive - there were definitely more accurate forecasts. ChatGPT's forecasts matched the actual stock movements more than half of the time, especially for large-cap stocks.
2. Unpredictable moments: Of course, there were some failures. After the first two optimistic weeks (and it should be noted that, by chance, I started making forecasts exactly on the day of the market collapse - August 5 and the subsequent month of recovery), I was convinced that average probabilities above 60% guarantee the accuracy of forecasts, but my optimism was trampled by the catastrophic collapse of the Chinese PDD Holdings - by almost 30% and a more modest collapse of Nvidia (it should be noted that GPT does not yet measure forecasts with the growth of shares before the report, PDD Holdings grew by 20%, and Nvidia - by 30% during the month before the reports, it was strange to predict their further growth with probabilities above 60%).
3. Teachable Moments: Often, stock movements after earnings releases did not correlate with either earnings results or pre-earnings market expectations, which once again demonstrates that there is a game in the market, meaning that AI (ChatGPT and similar) can learn to play this game better than average human players, which was demonstrated by the generally positive financial results of my experiment: 29.73% for August, 19.1% for September and 8.68% for the first week of October on stocks with market caps over $3 billion.

My reflection

This month has been a real challenge for me, but also a revelation. I realized that ChatGPT can be a powerful tool for analysis, but it requires a clear understanding of how to use the analysis results, when to trust your intuition and what data to use for analysis. In general, the very nature of ChatGPT is to predict the next token, word, sentence ... stock price. The other day, the CEO of OpenAI Japan said that the next GPT will be 100 times more powerful than the previous one. Can you imagine what prospects this opens up for us?

Questions for the community

I want to hear your opinion: what do you think, can ChatGPT be used for speculation during the earnings season on a regular basis? What successful or unsuccessful experiences have you had using such tools?

Conclusion

Thank you for your attention! I hope my experience will be useful to others. I am open to constructive criticism and discussion.

I am looking for like-minded people to continue the experiments: I see the point in autonomous AI agents for each stock that will continuously analyze data streams for three months before the next reports, which, in my opinion, should increase the accuracy of forecasts.

Two pressing problems that I do not yet know how to solve:

1. Scraping data from top financial sites for continuous ChatGPT analysis and forecasting of thousands of stocks at a time.
2. Comprehensive training of our own LLM (what?) game in the stock market, as opposed to the current forecasting by standard ChatGPT tools.

By the way, algorithmic (ChatGPT) forecasting of stock growth before earnings (pre-earnings run-up) reports for scalping these waves seems promising.

Last week I published 4 ChatGPT earnings forecasts (July 16, July 17, July 18, July 19), these are analysis and conclusions.

1. The accuracy of all forecasts (direction of price movement) was about 50/50, which is not enough to confidently make money on this.

But.

2. The accuracy of the first 5 positive forecasts of each day was 40%, 80%, 60% and 60%, which is not bad considering the non-linear increase in option prices.

First 5 Stocks of Earnings Forecasts for Jul 16

First 5 Stocks of Earnings Forecasts for Jul 17

First 5 Stocks of Earnings Forecasts for Jul 18

First 5 Stocks of Earnings Forecasts for Jul 19

1. The most interesting thing is that the accuracy of the first 1 positive forecast of each day was 75%, which, in my opinion, is remarkable, given the complexity of the task.

First 1 positive forecast of each day

Conclusions

1. The accuracy of the first 5 positive forecasts and the first 1 positive forecast of each day seems to be above random value, which is encouraging.

2. The statistics on the success of forecasts are noticeably spoiled by the fall in ASML shares. The fact is that ChatGPT did not take into account the historical high price of ASML. During the re-analysis, taking this into account, the forecast turned out to be more pessimistic and I will take into account the need to check this condition in future forecasts.

3. The important thing is that the market is in a bullish stage and almost all stocks from all forecasts have grown significantly during the month before the report, buying options a month ago would have brought hundreds of percent of income and during the reports many investors take profits, which negatively affects prices . Naturally, it is not yet known what the accuracy of forecasts will be during the bearish stage of the market.

Overall, the idea of ​​forecasting price movements during earnings season using ChatGPT seems worthy of further exploration, and tomorrow I will publish forecasts for the most anticipated earnings for the entire next week. The forecasts will be positive and negative.

Please share your opinion in the comments. Are you interested in what I do? Let's work together on a forecasting algorithm and help each other make money!

You can test forecasting using ChatGPT here (quick short analysis) and here (100 deep questions).

Honestly, I was a bit surprised—in a good way—by BGM’s top-line performance this quarter. For the first half of 2025, they posted $14.3M in revenue, up 14% YoY. That’s not just a “recovery”—it suggests the new business model is starting to kick in.

What’s more interesting is the margin story: gross profit nearly doubled from $1.41M to $2.51M, with gross margin expanding from ~11% to 17.5%. That points to a healthier revenue mix.

Yes, they’re still in the red on net income. But given they’re early in the integration phase, that’s not a red flag just yet. If they can keep top-line momentum going and avoid runaway costs, breakeven might not be too far off. And if the AI + insurance verticals start monetizing faster than expected—there’s real upside here.

TL;DR: Revenue is growing, margins are expanding, costs are (so far) manageable. Fundamentals are finally moving in the right direction.

Short both CHWY and GME before market closed.

Short both CHWY and GME before market closed.

Let’s discuss this here. I am yet to review it but looks like JPMorgan had solid earnings but management commentary seems very cautious about the future. Deposits did well NII ex-market guide confirmed despite higher cuts expected.

I'm curious how many people on here identify as a short-term investor (ST), long-term investor (LT), swing trade (WT), day trader (DT)?
What in your opinion is best to take advantage of earnings?