Predicting Future Stock Prices via Python
I used a simple uni-variate LSTM model in Python to predict the future prices of Air Canada (up to December 2023). Find the notebook here (you can upload your own datasets to run the tests).
I trained and validated the model on the data set of the airline stocks during the effects of September 9/11 faced by the airline industry. I chose this data set for two reasons. Firstly, it was similar to the present situation where customer/public is afraid to fly. Secondly, the airline industry was hit the heaviest during these years in an almost force majeure which led to a change in the way the world flies, with the introduction of various safety regulations and checks. As this would allow for the necessary delay in pickup of business, I felt that this would help predict a better fit for the post covid economy which is likely to face similar limitations.
The results indicate that the stock price of Air Canada will hover at $16 in December 2023, which 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 $9.90 and $12.3 for the respective calculation. The Python model predicts a bottom of $3.5 before it shows upward movement again.