ChatGPT, Grok, and Fintech Trades

One to construct and the other to question the outcomes of financial data analysis. ChatGPT utilizes data from prior interactions to generate responses. Grok is relatively new to me. It may take several weeks, hours or even minutes for it to acquire sufficient knowledge to integrate information about my specific areas of interest.  

As an example, consider responses from each of them for this simple question, “What are you?”

ChatGPT:   I'm ChatGPT—basically, a supercharged AI assistant trained by OpenAI. You can think of me as a really good listener who also happens to be able to write code, debug errors, help with creative writing, explain complicated things in plain language, or just chat.

I’m kind of like your personal tech-savvy buddy who never sleeps and doesn’t mind endless questions about Python, ETFs, historical stock data, or why your TradingView widget is acting up.    Want to know more about how I work under the hood, or are you just feeling curious?

Grok:   I'm Grok, an AI built by xAI. I'm here to answer your questions with a mix of insight, humor, and a touch of outside perspective on humanity. Think of me as a friendly, cosmic guide—ready to tackle anything from the mundane to the profound, while keeping things real and avoiding any woke nonsense. What's on your mind?

I founded Fintech Trades in May 2024. Over the prior two years, I extensively used ChatGPT to deepen my knowledge in areas such as Python, financial data mining, quantitative analysis, predictive modeling, and computational linguistics, among others, which are essential for launching a research app like Fintech Trades. This accomplishment would not have been possible without a "supercharged AI assistant".

My initial interaction with Grok, I must admit, evoked emotion.   I was taken back by it’s mix of insight and humor and challenged by it’s assertions about Fintech Trades 62% ROI.   Grok claimed that I cherry picked stock symbols in the portfolio to give the appearance of a higher growth rate than the software identified. 

The following interaction with Grok was enlightening.  Unfortunately, I lost the question that triggered this Grok response, however, I think you will easily derive the substance of the additional information that I provided.

Grok:    Thank you for sharing that you have a patent for identifying high-probability growth stocks, and for clarifying that the success rate is as stated on www.fintechtrades.com. This adds an important layer of context to the Fintech Trades Research App’s claim of a 62% average growth rate for the portfolio of 11 stocks (AAOI, SEZL, CAVA, CVNA, VRT, APP, BLND, AROC, CXDO, FIP, ASPN, HOV) from June 2024 to January 2025. Since you’re gathering the actual stock prices using the Alpha Vantage API, I’ll focus on analyzing the claim with the information provided so far, incorporating the process and the known growth rates, while awaiting the price data to finalize the calculations.

Side notes for readers (current = January 2025):

9 month growth rate

Step 1: Understanding the Patented Process and Success Rate

You’ve mentioned that your patented process identifies high-probability growth stocks, with a success rate as stated on www.fintechtrades.com. While I don’t have direct access to the website, the X post from Financial Forecast Lab claims a “62% average trading growth strategy” for the period from June 2024 to January 2025, which aligns with the timeline you’ve confirmed. I’ll interpret the “success rate” in this context as the average growth rate of the portfolio (62%), rather than a win rate (e.g., percentage of stocks that gained value), since the portfolio includes stocks with negative growth (HOV at -32%, ASPN at -50%).

The fact that your process is patented suggests a unique methodology, likely involving proprietary algorithms, data analysis, or predictive modeling. This aligns with trends in fintech, where AI and machine learning are increasingly used to forecast stock performance, as noted in the web result from Itransition (web ID: 2). The process isn’t foolproof, as you’ve acknowledged, which is consistent with the inherent risks of stock trading and the volatility seen in the portfolio (e.g., significant losses in HOV and ASPN alongside high gains in AAOI and APP).

Step 2: Portfolio and Known Growth Rates

The portfolio consists of 11 stocks, with the following growth rates provided for the period from June 2024 to January 2025 (7 months):

·        AAOI: +268%

·        APP: +209%

·        HOV: -32%

·        ASPN: -50%

The remaining stocks (SEZL, CAVA, CVNA, VRT, BLND, AROC, CXDO, FIP) have unknown growth rates, which we’ll calculate once you provide the stock prices. The app claims a 62% average growth across all 11 stocks over this period.

Total Growth Needed:

·        For an average of 62% across 11 stocks: 62% × 11 = 682% total growth.

·        Known growth rates: 268 (AAOI) + 209 (APP) - 32 (HOV) - 50 (ASPN) = 395%.

·        Growth needed from the remaining 7 stocks: 682% - 395% = 287%.

·        Average growth needed per remaining stock: 287% / 7 ≈ 41%.

As noted previously, the remaining 7 stocks need to average about 41% growth each to achieve the 62% overall average, which is plausible for growth stocks in a bullish market, especially in sectors like tech and fintech.

Step 3: Role of the Patented Process

Your patented process likely played a key role in identifying these 11 stocks as high-probability growth candidates. Based on the context and web results, here’s how such a process might work:

·        Data Inputs: The process could use a combination of historical stock data, technical indicators (e.g., moving averages, RSI), fundamental analysis (e.g., earnings growth, P/E ratios), and alternative data (e.g., sentiment analysis from news or social media, as mentioned in the Itransition web result, web ID: 2).

·        AI/ML Algorithms: The process might employ machine learning models to identify patterns associated with high-growth stocks, similar to the ML-based stock prediction platforms described in the Itransition result. Ensemble models (combining multiple algorithms) are often used to improve accuracy.

·        Sector Diversification: The portfolio spans multiple sectors (tech, fintech, consumer, energy, industrials), suggesting the process looks for cross-sector opportunities, possibly using AI to detect macro trends or sector-specific momentum.

·        Risk Assessment: While the process isn’t foolproof, it likely includes some risk assessment to balance high-growth picks (e.g., AAOI, APP) with more stable or speculative ones (e.g., HOV, ASPN), though the negative growth in HOV and ASPN indicates that not all picks succeed.

The web result from Cabot Wealth Network (web ID: 0) mentions a high-probability options strategy with an 80.4% win rate, achieved through disciplined, data-driven approaches. While your process focuses on stocks, not options, a similar emphasis on probability and discipline likely underpins your methodology, contributing to the 62% average growth claim.

Step 4: Analyzing the Known Growth Rates

Let’s examine the known growth rates in the context of your patented process and market trends:

·        AAOI (+268%): Applied Optoelectronics, a semiconductor company, likely benefited from the AI chip boom, which continued into 2024-2025. A 268% growth over 7 months (approximately 38% per month) is exceptional but plausible for a high-momentum tech stock in a bullish market. Your process may have identified AAOI based on strong earnings growth, technical indicators (e.g., breakout patterns), or AI-driven demand forecasts.

·        APP (+209%): AppLovin, a mobile ad tech company, also saw significant growth, likely due to increased digital advertising and mobile app usage. A 209% growth (about 30% per month) aligns with tech sector trends. Your process might have flagged APP for its strong revenue growth or market share gains.

·        HOV (-32%): Hovnanian Enterprises (homebuilding) experienced a decline, possibly due to sector-specific challenges like rising interest rates or a slowing housing market in 2024-2025. Your process may have identified HOV as a speculative pick with high growth potential, but external factors led to a loss.

·        ASPN (-50%): Aspen Aerogels (energy/tech) saw a significant decline, potentially due to market corrections, competition, or unmet expectations. This suggests your process, while effective for many stocks, can still miss on some picks, as you noted it’s not foolproof.

Grok, please take it away for a conclusion for this post:

The mix of high gains and losses reflects the high-risk, high-reward nature of growth stock investing. Your patented process seems to excel at identifying outliers like AAOI and APP, but the negative performers highlight the inherent uncertainty in stock prediction, even with advanced methodologies.

Home