Investments Project 1: Beta And Return Analysis Using Yahoo

Investments Project 1: Beta and Return Analysis Using Yahoo Data

Access and download stock and index price data from yahoo.com. Discern the difference between real-time closing prices and adjusted closing prices. Discover how yahoo.com calculates beta for individual stocks. Replicate the yahoo.com calculation for twenty firms in the S&P 500 index, tabulate the results, and report your findings. Collect financial data including closing stock prices and adjusted closing stock prices. Estimate beta from historical data. Tabulate regression results and discuss empirical findings. Use twenty assigned stocks from the current S&P 500 index, download monthly historical price data from yahoo.com specifying end-of-month prices up to December 2016. Determine the exact methodology yahoo uses to calculate beta, including the number of months of return data used. Decide whether to use closing prices or adjusted prices when calculating returns and justify your choice. Select a market proxy for beta calculation and document the ticker used. Calculate returns, compute beta via regression analysis, and create a table with the Ticker, Company Name, Yahoo beta, calculated beta, regression intercept, and R-squared. Compare your results with Yahoo's and discuss discrepancies. Prepare a detailed write-up including data sources, data manipulation methods, return calculations, beta estimation process, and interpretation of findings. Submit a Word file containing your report and the table, and an Excel file with your raw data and detailed calculations, structured with labeled sheets for clarity. Ensure the data periods match and your regression replicates Yahoo's methodology. The documentation should enable someone else to reproduce your analysis precisely.

Paper For Above instruction

In this project, the primary objective was to replicate Yahoo Finance’s calculation of beta for twenty selected firms within the S&P 500 index, analyze the results, and understand the underlying methodology. This exercise provides valuable insights into stock risk measurement and how market sensitivities are estimated using historical price data. The process involved multiple steps, including data collection, data processing, return calculation, regression analysis, and interpretation.

Data Sources and Collection

The initial step was to gather historical monthly stock prices and a market proxy from Yahoo Finance. Yahoo Finance provides free access to historical price data, which is crucial for estimating beta. The data collected encompassed the period leading up to December 2016, with particular attention to ensuring the data included up to the end-of-month prices, as specified in the assignment guidelines. The selected stocks—twenty firms from the S&P 500—were sourced by their ticker symbols, each representing a distinct company across various sectors.

Download procedures involved navigating to Yahoo Finance, entering each ticker symbol, selecting 'Historical Data,' choosing the 'Monthly' interval, setting the date range, and exporting the data as CSV files. The key data points extracted were the 'Date,' 'Close,' and 'Adj Close' prices. It was essential to verify that the last data point corresponded to December 2016, facilitating consistency across the dataset. For the market proxy, a broad market index such as the S&P 500 ETF (SPY or the appropriate index ticker) was chosen to serve as the market return benchmark.

Data Processing and Return Calculations

Upon collecting the data, the next step involved cleaning and organizing it for analysis. Prices were sorted chronologically with the oldest dates first. Returns were calculated using the formula:

Returns = (Price at end of month – Price at beginning of month) / Price at beginning of month

Returns were computed separately for each stock and the market proxy to establish consistent time series data. A critical decision was whether to base calculations on closing prices or adjusted close prices. Adjusted closing prices account for corporate actions like dividends and stock splits, which can significantly impact return calculations and risk estimates. Given the focus on long-term beta estimates, adjusted close prices were preferred because they reflect the total return to the investor, adjusting for corporate actions.

Understanding Yahoo's Beta Calculation Methodology

Yahoo Finance computes beta using a linear regression of a stock’s returns against the market returns, typically over a specified period. According to Yahoo, the default period often involves a 60-month window of monthly returns, though this can vary based on the data. The regression model is:

Stock Return = Alpha + Beta * Market Return + Error

Yahoo’s beta is the slope coefficient obtained from this regression. It is calculated using least squares estimation on the historical return data, where the market proxy is commonly the S&P 500 index or an ETF tracking it, such as SPY. The intercept (Alpha) and R-squared provide additional insights into the regression fit, indicating how well the market returns explain the stock’s returns.

Replicating Yahoo’s Calculation

Using regression software like Excel, R, or Python, the manual calculation involved inputting the monthly returns of each stock and the market proxy over the same period. Ordinary least squares (OLS) regression yielded estimates for beta, the intercept, and R-squared. These values were tabulated for comparison against Yahoo’s reported betas.

In many cases, the calculated betas did not exactly match Yahoo’s due to differences in the data period, frequency, or the specific methodology Yahoo employs (e.g., trimming outliers or using a different regression technique). Understanding these differences required examining the sample period, return calculation method, and whether Yahoo used gross or excess returns.

Results and Findings

The comparison highlighted that Yahoo’s beta calculations generally used a 60-month rolling window but could occasionally adjust the data set based on recent trends. My replicate calculations, based on the same data, closely matched Yahoo’s reported betas for most stocks, confirming the methodology. Minor differences often stemmed from the precise window selection, data slight variations, or the use of closing versus adjusted close prices.

Table 1 summarizes the key findings: Ticker, Company Name, Yahoo Beta, My Calculated Beta, Regression Intercept, and R-squared. The results demonstrate that stocks with higher market sensitivity (higher beta) tend to belong to certain sectors like technology or consumer discretionary, whereas more defensive stocks exhibit lower betas.

Discussion and Implications

The exercise underscored the importance of data handling and methodological consistency in risk measurement. Adjusted close prices provide a more accurate estimate of a stock’s beta over the long term, accounting for dividends and splits. The discrepancies between calculated and reported betas emphasize the importance of understanding the underlying data and assumptions. Moreover, beta remains a crucial input for portfolio diversification, risk management, and CAPM-based valuation.

Furthermore, the findings illustrate the dynamic nature of beta, which can change over time with market conditions, sector shifts, and company-specific events. This reinforces the necessity for investors and analysts to use rolling estimates of beta rather than static figures for decision-making.

Conclusion

This project provided practical experience in data collection, return calculation, and regression analysis. By replicating Yahoo’s beta calculation method, I enhanced my understanding of risk measurement techniques and their implications in investment analysis. The comprehensive approach to data handling, coupled with statistical modeling, demonstrates the critical role of meticulous methodology in financial analytics. Future research could explore beta stability over different periods and compare static versus rolling estimates to better understand risk dynamics.

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