How AI is Making Stock Price Predictions More Accurate Than Ever

Predictable is tough. Given the complexity and volatility of financial markets, stock price prediction is even tougher as it is affected by many factors not just historical values of stock price itself. News, macroeconomic variables such as interest rates and investor sentiment,  company related announcements like dividend payment, fraud etc. might have an impact of the stock price prediction. 

This blog tries to help you how to run stock price prediction with machine learning. With the rise of machine learning, we are able to have more accurate results than before. This is simply thanks to better data pattern recognition and having multivariate regression. Pattern recognition is key to have more accurate predictions as pattern detection reveals key insights about the stock price movement and once it is detected, it becomes easier to say something for the future direction. 

Multivariate regression plays also key role in that it helps us use all other explanatory variables in stock price prediction. For example, instead of predicting a stock’s future price using only historical prices, we can include variables like: 

• Trading volume  

• Interest rates  

• Company earnings per share (EPS)  

• Market sentiment score  

• Volatility index (VIX)  

Let’s walk through how we can run a sample stock price prediction in python. First, need to install all required python packages such as mlforecast, catboost, pandas, and numpy and then import all. 

mlforecast is a powerful Python library for scalable time series forecasting using machine learning models, and CatBoost is a gradient boosting algorithm that excels at handling complex patterns and categorical features. 

For this blog post, I did not use real stock price data (maybe in the next blogJ), I created a simulation for stock price data using generate_daily_series function from mlforecast.utils, which is quite handy. 

Now, it is time to add additional variables, i.e. trading volume and VIX. Trading volume is the number of trades during a specific time interval, say 1 hour, 1 day etc. VIX is a widely used indicator for financial market volatility. It is a calculation designed for 30-day expected volatility.

After arranging all variables, we are ready to configure MLForecast and catboost model. To do that, we define initial parameters such as frequency, lags, lag_transform, date_features 

After configuring the MLForecast, we can now run the model with our exogenous variables, i.e. volume and vix. Note that In newer versions of mlforecast, external features (exogenous variables) such as volume and vix are included directly in your input dataframe (the same dataframe containing your target). 

Model learning is completed, this mean we can run prediction. It is up to you to decide how many days you want to predict. Here, I picked 7, indicating that I just predict the stock price for 7 days ahead. 

Here is the fun part, plotting the result of the prediction of 7 days ahead. 

Conclusion 

As financial markets continue to grow in complexity, the role of artificial intelligence in predicting stock prices has never been more critical. By combining machine learning techniques with rich, multivariate datasets, we can move beyond simplistic models and gain deeper insight into market dynamics. 

In this blog, we demonstrated how to build a stock price prediction model using mlforecast and CatBoost, showcasing how powerful these tools are for recognizing hidden patterns and enhancing prediction accuracy. While we used simulated data here, the same approach can be seamlessly applied to real-world financial datasets. 

AI doesn’t promise perfect prediction but it does offer a significant edge. And in markets where every percentage point matters, that edge can make all the difference.