Time Series Econometrics for Forecasting Stock Prices
Forecasting stock prices is one of the most challenging and sought-after goals in financial economics. While no model can perfectly predict the future (largely due to market efficiency and random walk theory), time series econometrics provides the rigorous framework necessary to model stock price dynamics, test for significant relationships, and generate informed forecasts.
Here is an overview of the key concepts and models used in applying time series analysis to stock price data.
Why Standard Regression Fails in Finance
The primary issue when analyzing financial data, such as daily or monthly stock prices, is the inherent violation of the core assumptions of Ordinary Least Squares (OLS) regression.
- Non-Stationarity: Stock prices often exhibit a trend over time, meaning their mean and variance are not constant. Non-stationary data leads to spurious regressions, where a high suggests a relationship that is statistically meaningless.
- Autocorrelation: The current price of a stock is highly