Long Term Analysis of Stocks using the DCF model

Inspiration

We knew that statistical modeling is used to predict short-term investments and to make short-term investment decisions. We wanted to apply this to long term investing using the discounted cash flow model.

What it does

This model takes the ticker from the user and outputs the intrinsic value of the stock along with the ideal long-term investment decision. The intrinsic value of a stock is the real value of the stock when undervaluation and overvaluation are disregarded. Intrinsic value is used to prove whether a stock is undervalued or overvalued.

How we built it

We built this project using python to scrape data and incorporate our formula into it. We used numpy, matplotlib, pandas, scikit-learn, yfinance, yahoo_fin, and tkinter

Challenges we ran into

Since we derived the formula, there were a few minor setbacks that we ran into. As a result, we had to mathematically work around these. Creating the line of best fit for the vector support regression model was also difficult since this was the first time we used this type of regression.

Accomplishments that we're proud of

With our program, a user is able to find the intrinsic value of any stock given that they have the ticker of that stock. We were able to successfully use the discounted cash flow model to offer long-term investment advice.

What we learned

We learned how important cash flow is to a company. We also learned how to use the DCF to make long-term investment decisions and calculate the intrinsic value of a stock without knowing the r^2-value of regression.

What's next for Long Term Analysis of Stocks using the DCF model

Our program can be used to make long-term investment decisions when a user needs advice. It is possible to encapsulate the many applications of the intrinsic value of a stock.

Built With

• numpy
• pandas
• python
• scikit-learn
• tkinter
• yfinance