Project Overview In this project, we performed an in-depth analysis of two stocks, Equity Residential (EQR) and Sabra Health Care REIT (SBRA), focusing on their historical performance, risk profiles, and correlation between their movements. The goal was to understand their past behaviors to help inform potential investment decisions. However, as is common knowledge in finance, past performance is not indicative of future results. Key Metrics Analyzed We used the following metrics in our analysis:
- Average Daily Simple Returns: This is the mean of the daily price changes, indicating the average return one might expect in a single day based on historical data.
- Annualized Daily Log Returns: This metric measures the compound rate of return over the year, which is generally considered more accurate than simple returns for periods longer than a single day.
- Variance of Daily Log Returns: Variance provides a measure of how much the returns of a stock vary around the mean return. It is commonly used as a measure of investment risk.
- Daily Standard Deviation: This is the square root of variance and is another commonly used measure of investment risk or volatility.
- Correlation of Daily Log Returns: We computed the correlation between the daily log returns of EQR and SBRA, which indicates how closely the two stocks have historically moved in relation to each other. Results Our analysis showed that SBRA historically has had higher returns but also higher volatility compared to EQR. On the other hand, EQR showed lower returns but also lower volatility. The correlation between the two stocks was found to be moderately strong, suggesting they tend to move in the same direction. Disclaimer While this project provides an in-depth analysis of past stock data, it does not predict future performance. The stock market is influenced by a variety of factors, many of which are unpredictable. Therefore, this analysis should be used as a starting point for making investment decisions, not as a definitive guide. It's always recommended to conduct further research or consult with a financial advisor before investing. Tools Used The project heavily used Python's numerical computing library, NumPy, for calculations.