Forecasting the Future: Predicting Stock Prices with ARIMA
Predicting the stock market is basically the Holy Grail of finance. Everyone wants to know what’s going to happen tomorrow. In Chapter 2, we look at a classic tool for this: the ARIMA model.
ARIMA stands for Autoregressive Integrated Moving Average. It sounds complicated, but here’s the gist: it looks at past values and past errors to guess the next value in a sequence.
When to use ARIMA
The book points out that ARIMA is your best friend when:
- You don’t have other “dependent” variables (like sales or profits) to help you predict.
- You have a good chunk of historical data.
- The data is autocorrelated (meaning today’s price is somewhat related to yesterday’s).
The Case Study: MRF Stock
The authors used MRF stock data from 2023 to 2024 to test this out. Here’s how they did it:
- Check for Autocorrelation: They looked at “lags”—how much the price today correlates with the price 1 day ago, 2 days ago, etc. It turns out Lag 1 had the strongest connection.
- Testing Different Models: They didn’t just pick one ARIMA version and hope for the best. They tested models like ARIMA(1,1,1), ARIMA(1,0,2), and ARIMA(0,0,3).
- The AIC Score: To find the winner, they used the Akaike Information Criterion (AIC). Think of it like a golf score—lower is better.
The Winner
The ARIMA(1,1,1) model came out on top with the lowest AIC and the most significant p-values. It wasn’t perfect, but it provided a solid statistical baseline for predicting price movements.
But here’s the problem: ARIMA is great for short-term forecasts, but the further out you go, the shakier it gets. It’s a tool, not a crystal ball.
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