Human vs. ChatGPT: Analyzing Market Data Efficiently
Analyzing market data can be a daunting task, especially when faced with the intricacies of calculations and interpretations. One of the primary challenges is crunching the numbers yourself. But what if you could leverage the capabilities of ChatGPT to streamline this process? Here’s a glimpse into how a straightforward approach can make a difference.
For anyone diving into data analysis, mastery over the necessary formulas and proficiency in using analytical software are paramount. While some relish the process of dissecting data manually, others might prefer a more automated approach with less effort from oneself. This brings us to the question: Can ChatGPT be a reliable assistant for such tasks?
To answer this, I embarked on a simple experiment that anyone could replicate, aiming to gauge ChatGPT’s prowess in this domain.
The Challenge
My objective is straightforward: Entrust ChatGPT with the task of analyzing specific market data.
I opted for the historical monthly S&P 500 (^GSPC) data, focusing on adjusted close prices (accounting for splits, dividend, and/ or capital gain distributions) and trading volume. The data spanned from Jan 01, 1985 to Oct 10, 2023, and was sourced directly from the Yahoo! Finance website (Link).
In general, I imposed a few constraints for my experiment:
- The data would remain unaltered, and no external aids would be used.
- I chose the ChatGPT-4 model, equipped with the “Advanced data analysis” add-on, without any further customizations and optimization of the model. This was to gauge the raw efficiency of the model in a real-world scenario of an amateur investor.
- ChatGPT’s role was to deliver results directly, without guiding me through the process or offering code snippets.
To validate the accuracy of ChatGPT’s analysis, I cross-referenced its findings with my own calculation.
The Results
Here’s a breakdown of my interaction with ChatGPT, along with some insights into my thought process.
Prompt 1: Setting the Stage
I began by outlining the context for ChatGPT.
Prompt 2: Data Input
I pasted the data directly into the chat interface.
ChatGPT’s initial response was commendable, offering a solid foundation for those unfamiliar with market data analysis.
Prompt 3: Absolute Return Calculation
Here, I encountered a hiccup. ChatGPT didn’t reference the earliest data point from Jan 01, 1985. This highlighted for me the need for more precision in my queries.
Another challenge arose when copying data from MS Excel, as the tab-separated format didn’t seem to mesh well with ChatGPT’s interface.
I re-entered the data and inquired about the absolute return again.
Despite ignoring the failed calculations initially, the result of approximately -69% was off-mark.
I probed further to understand ChatGPT’s chosen values for the calculation.
This experience underscored the limitations of merely pasting a large dataset into the chat.
However, ChatGPT’s accurate formula for absolute return showcased its potential as a really fancy calculator.
Prompt 4: Fancy Calculator
ChatGPT’s calculations were spot on! Yet, my curiosity wasn’t satiated. I wondered about its capability to handle more intricate analyses.
Prompt 5: Volatility Analysis
I reintroduced the dataset and requested ChatGPT to compute the volatility (standard deviation) of the adjusted prices.
Unfortunately, the result was inaccurate, reaffirming the challenges of my initial approach with ChatGPT.
Nevertheless, I was intrigued to know ChatGPT’s investment stance based on the provided data.
Its response was enlightening, offering valuable insights, particularly beneficial for budding investors.
Lastly, I posed a hypothetical: Would ChatGPT’s investment perspective change if it was aware that the data pertained to the S&P 500 index?
Here’s its answer:
In Conclusion
While ChatGPT showcased its analytical prowess in certain areas, it also highlighted the importance of clear, precise prompts and the potential challenges of data input.
It’s a powerful tool, but like all tools, its efficacy is determined by how it’s wielded.
Have you ever tried to analyze data with ChatGPT? If so, what was your experience?
I’d appreciate if you would share your story here.
