Original article was published on Deep Learning on Medium
Predicting Future Stock Prices via Python
I used a simple univariate LSTM model in Python to predict the future prices of Air Canada (up to December 2023).
I trained the model on the dataset of the airline stocks during the effects of September 9/11 faced by the airline industry. I chose this dataset for two reasons. Firstly, it was similar to the presnent situation where customers/public is afraid to fly. Secondly, the airline industry was hit the heaviest during these years and that it was not a general recession. I felt that this would help predict a better fit considering the present industry choice and the situation on hand.
The results indicate that the stock price of Air Canada will hover at $16 in December 2023, that is after stabilization and recovery of the economy.
Additionally, in order to gauge the present prices of the stocks of Air Canada, I used a basic DCF method (WACC — 5.7%, EV/EBITDA — 6x) and graham’s method (TTM EPS, adjusted to reflect present AAA bond yields, with a 4% growth rate) to calculate the intrinsic value of the Air Canada stocks.
The intrinsic value stood at $9.30 and $12.3 for the respective calculation. The Python model predicts a bottom of $3.5 before it shows upward movement again.