MVPORT: Stata module for Collection, Optimization and Backtest of Financial Portfolios

This package is a set of 12 commands designed to a) collect online financial data, b) optimize portfolios, c) simulate portfolios, and d) backtest portfolios. The 12 commands are the following: 1) returnsyh - gets prices and trade volume of a list of stocks from Yahoo Finance, computes simple and continuously compounded returns, and integrates the data in one dataset 2) meanrets - calculates the expected simple returns of a historical series of continuously compounded returns or simple returns. Expected returns can also be calculated using weights from other variable or using the exponentially weighted moving average (EWMA) method. 3) varrets - calculates the variance-covariance matrix of a series of continuously compounded returns. The variance-covariance matrix can also be estimated using weights from a variable or using the exponentially weighted moving average (EWMA) method. 4) gmvport - calculates the global minimum variance portfolio of a set of continuously compounded returns following Portfolio Theory. 5) mvport - calculates the minimum variance portfolio given a required return following Portfolio Theory. 6) ovport - calculates the optimal variance portfolio that lies in the Capital Market Line. 7) efrontier - generates and graph two efficient frontiers: with and without allowing for short sales. 8) cmline - generates and graph the efficient frontier along with the capital market line when adding a risk-free asset. 9) simport - simulates random portfolios inside the portfolio frontier 10) holdingrets – calculates the holding period return of one or more price variables of a sorted time series dataset 11) backtest - performs a backtest of a financial portfolio. It calculates the holding period return of a specific portfolio 12) cbacktest – performs a cumulative backtest of a financial portfolio. For each time period, a holding return is calculated. It also graphs the cumulative holding period return of the portfolio for all periods. The optimization commands gmvport, ovport, efrontier, and cmline allow for weight constraints such as minimum and/or maximum weights for each asset. They also allow to use any variance-covariance matrix and any vector of expected asset returns. The variance-covariance matrix and the vector of expected returns can be calculated using the EWMA method. In addition, these commands performs risk decomposition for each portfolio asset using Euler’s theorem of decomposition for homogenous functions.