Complexity of the Stock Market
Various macro indicators, quarterly earnings reports for companies, and not to forget sectorial trends, along with any political happening around that time, have a significant impact on stock prices. This is because global stock markets have many billions of dollars in daily trading volume on average, although minor changes in these constants can result in vast fluctuations. In 2022, global GDP growth approached 3%, and this transition has directly affected the trends of some important world stock markets. Long-term and short-term unemployment ratios between the Federal Reserve inflation rates are also central. In the first quarter of 2023, when U.S. unemployment was roughly at 3.5%, and inflation stood close to 4%, adjustments to these levers created ripples across Wall Street on this occasion within stock market activity lore U.S. stocks minutely. We all remember that turbulent time! Although ChatGPT can use these data and give relevant information to the users, it cannot be used for predicting future values of its properties applied to the stock market attacks.
This is the stock market complexity as demonstrated with a ripple effect of events. Stocks in the energy sector might be influenced by political tensions on a worldwide scale as it may lead oil prices to track higher. The Russia-Ukraine conflict was a major reason for the surge in international oil prices from 25.69 dollars per barrel at one point near the end of December, followed by Brent crude oil exceeding 120$ dollar per barrel and had a direct impact on global energy companies’ stock price that drove many to seven-year highs as early as January-2022. Even though ChatGPT is able to understand these historical patterns, it needs more financial data analysis skills to deal with a future set of complex variables.
Limitations of Language Models
ChatGPT is a natural language processing model, and this generation-based model also includes language understanding and questions, but not only with your financial data. It can provide context of what the market has done historically, but it does not quantitatively answer financial questions. Unfortunately, due to my model limitations, I cannot investigate as detailed above on S&P 500 Index volatility in the US, which was approximately at an average of 20% during the year 2023 using proper financial models.
ChatGPT can not handle real-time market data. The stock market fluctuates in real-time, and high-frequency trading systems with millisecond data processing speed can immediately seize the opportunities provided by ChatGPT. ChatGPT is not a financial analyst and does lack the ability to look at historical data/records in relation to performing some analysis like price-to-earnings ratio (PE Ratio) or dividend yield for the future trends of company stock.
Statistical Methods for Stock Predictions
Two commonly used analytical methods in stock predictionsTechnical analysis and Fundamental analysis. This form of analysis consists of determining trends and patterns based on historical price data (including open, high, low, and close) using different indicators performed by specialized software. Many major investors look at the moving averages (MA) of the S&P 500 Index, and a crossover between the key for many is when its 50-day MA moves above or below its longer-term, typically more important, 200-day. Unlike technical analysis, which uses stock price data alone for predicting prices in the future, fundamental analysis is based on a company’s financial reports. However, Apple Inc.’s annual revenue was $394 billion in 2022, with a net profit of $94.6 billion, and this financial statement helps to see the forecast for its stock better.
While these analytical methods can offer direction to investors, the capital market is highly volatile. With 50 years of historical data, you can see that the mean annual return is about 10%, BUT it has an annual volatility of up to 15%. The ability to efficiently predict what is going to happen in the markets of the future, using even the most sophisticated statistical models, is very limited. ChatGPT cannot make these predictions because of its constraints on both technical and fundamental analysis.
AI Applications in the Stock Market
It has been getting more and more important in the stock market, especially in quantitative trading or algorithmic trading. Quantitative funds deploy AI models to crunch massive amounts of market data and trade within seconds. By way of example, Renaissance Technologies is a quant hedge fund that boasts hundreds of billions in AUM. The company leverages AI models to extract value from the market data, engineer trading strategies and average 30+% annual returns. But such AI technology is based on intricate machine learning and deep learning models dealing with millions of data points something that ChatGPT doesn’t have.
HFT systems are AI-based solutions that trade in the market at a certain pace. In 2022 alone, high-frequency trading at Citadel Securities earned billions of dollars worth of profits; their typical time to execute a trade was under one millisecond. AI-powered systems can process stock order books, market volatility and other data in real-time. In contrast, ChatGPT is not designed to deal with live financial data streams while Kit SNNs are very specifically shaped for this purpose.
