Role of generative AI in stock market prediction

 

Before making predictions about stock price movements, an expert will consider various economic indicators, market sentiment, past performance and trading volume. An effective generative AI tool must take into account all these important factors before recommending a stock for investment. These AI-based predictive models are built on historical data, but should also be equipped to conduct sentiment analysis based on current market conditions – one of the most essential elements in share market trading.

These GenAI models learn from historical data fed into them to recognise the complex patterns such as market trends, and other factors to establish their correlation with the price movements. They also perform sentiment analysis by extracting important information from news articles and social media posts for better forecast. The models, thus trained on the training data (historical data) and enhanced with sentiment analysis, can show whether a stock will move upwards or will go down for the future time period (forecast period) with the expected fluctuations.

Although, it all may sound so simple, but one could only wish for such a perfect AI-based tool that can build a model for accurate predictions. A predictive model, however well trained, can never guarantee if the forecast value will even be nearly close to the actual movement.

In stock market a lot of things can go wrong. Market may crash due to some sudden catastrophe such as war, natural calamity, etc. However, these are extremes known as black swan events, and even if we discount them, there are still many other factors that an AI tool may not be able to keep track of while training a predictive model.

Market experience and human intuition are the two main factors that no AI tool has been bestowed with so far, which only humans are gifted with. Even if these GenAI tools, specialized in stock market, were to be developed using the expertise of experienced and skilled stock brokers and investment bankers, it is unlikely that the models can be fully relied upon. At the most, the forecast values obtained using these GenAI tools can be used for reference and not for making investments blindly.

Various companies had claimed to have developed such AI tools that, according to their version of stories, could make accurate predictions for stock prices, but they all vanished as quickly as they came. Their predictive models could not stand the test of time. Does it mean GenAI tools should not be used for stock market? Well, that depends on an investor. Although, with time, these AI tools will definitely get better, but will they be able to reach 99.99% accuracy in their predictions or at least an acceptable accuracy level by the market standard. That’s a big question!

Investment in stock market is not a one-time affair. Serious investors continuously look for opportunities to make more money by investing in the right kind of stocks. This means, the predictive models must be trained on a daily basis considering the volatile nature of share market. Despite following the iterative refinement process, will automating the training of predictive models work in a long run with the accuracy needed for right investments? Another big question!