Data science has shown to be a highly helpful tool for a variety of purposes over the years, from safeguarding sensitive data to processing large volumes of data more efficiently to assisting in data-driven decision-making.
But what about using data science to stock market forecasting?
When we evaluate the various applications of data science outlined above, many of them are far less sophisticated than using data science to analyze the stock market.
For example, when you like a song on Spotify, data science can offer recommendations for related music you might enjoy. That’s a simple task.
Much more challenging is for Spotify to select the exact music at the exact time that maximizes the song’s emotional impact on you at that precise moment. This is similar to the process of selecting the best stock to invest in.
So why is it so complex?
There are numerous factors that contribute to its complexity. The stock market, for starters, is naturally volatile and unpredictable. Even when employing machine learning to assess the previous history of a stock’s price, the model’s predictions for the future aren’t certain to come true.
Second, a stock’s price is influenced by a vast variety of factors. Interest rates, weather occurrences, corporate scandals, government supervision, and even management behavior can cause a stock’s price to rise or fall.
Finally, making forecasts is not the same as long-term forecasting. Based on the previous week’s performance it’s a lot easier to forecast what a stock’s price will do tomorrow. Predicting what a stock’s price will do a year from now or five years from now is far more challenging.
To put it another way, there are simply too many variables at play to make precise stock market predictions or long-term forecasts. It’s also not for a want of trying! For years, people have been trying to beat the stock market, including those who have been utilizing data science to generate better predictions since the 1980s.