⚙️ Tool : Python
💻 Frameworks : Streamlit
🗂️ Source Dataset : SP500

This project showcases a web application that is designed to perform CAPM calculations for different stocks. The application uses Python programming language and its libraries such as Pandas, NumPy, Streamlit and Plotly, to gather stock data from Yahoo Finance and perform calculations to determine expected returns.

• CAPM is a model that describes the relationship between the expected return and risk of securities.
• CAPM indicates that the expected return on a security is equal to the risk-free return plus a risk premium.

Figure 1 — CAPM Formula

• A risk free asset could be a US Government 10 year Treasury bill.
• Investors who are extremely risk averse would prefer to buy the risk free asset to protect their money and earn a low return.
• If investors are interested in gaining more return, they have to bear more risk compared to the risk free asset.

• Market portfolio includes all securities in the market. A good representation of th emarket portfolio is the S&P 500.
• Market portfolio return is the average return of the overall return of the SP500.

It is a measure of a stock's risk (volatility of returns) reflected by measuring the fluctuation of its price changes relative to the overall market.

ß = 0: No Market Sensitivity
ß < 1: Low Market Sensitivity
ß = 1: Same as Market (Neutral)
ß > 1: High Market Sensitivity
ß < 0: Negative Market Sensitivity

Project Objective:

• To develop a wep application for CAPM financial model analysis.
• To evaluate the risk associated with different investment options by using CAPM to calculate the expected rate of return for each investment.

We developed a web application using Python with Streamlit framework to quickly create interactive and data-driven web application. Streamlit is known for its simplicity and ease of use, making it a best choice for us looking to deploy data applications without extensive web development expertise. Here's the methodology to develop the web application using Streamlit:

1. Application Code:

• Create CAPM_Returns.py and capm_funtions.py to write the code for our web application.
• Import the necessary libraries, including streamlit, pandas, yfinance, datetime and pandas-datareader.
• Define the layout and functionality of our application, including text inputs.

2. Data Integration:

• Get input from user and download data for SP500.
• Create functions to normalize prices based on the initial price, calculate daily returns and beta value.

3. Run Application:

• Open cmd terminal, navigate to our project directory, and run the application using streamlit run CAPM_Returns.py

Click to view full code of CAPM Return

Click to view full code of CAPM Functions

Figure 2 — Dataframe head and dataframe tail with 4 selected stocks (Tesla, Apple, Amazon & Google).

Figure 3 — Price of all stocks before and after normalizing.

Figure 4 — Calculated Beta value and CAPM return value.