ETFs are baskets of securities designed to track the performance of an index. They are designed to provide exposure to broad-based indexes at a lower cost. We first analyzed why ETF should be the choice for an investment. We provide a brief history of this segment, key attributes of ETFs, and investments strategies and implementations with ETFs. The article then presents data analysis and a series of forecasting methods with data analysis techniques to evaluate the performance of each method. The data analysis and the forecast evaluation is to determine the best forecasting model for a single ETF (SPY). The different techniques considered include single exponential smoothing, Holt’s exponential smoothing, simple linear regression, multiple regression and various versions of Box-Jenkins (ARIMA) models. Based on the evaluation of a decade of past historical data, we provide a guidance for the price of our ETF (SPY) using the multiple regression technique (with an R-square of 98.4%), which produced promising results (with low forecast errors of 1% across several forecast metrics), among the different techniques evaluated. Promising results were also obtained using the Multiple regression technique on several other popularly traded ETF’s.