Successful Applications of Portfolio Software
Portfolio software is a Swiss Army knife for serious investors looking to increase their returns and minimize their risks. There are many highly effective uses of this type of tool, and also some pitfalls that you simply avoid. Read through to learn more.
Portfolio software is ordinarily utilized to analyze a portfolio of securities against a market standard, whether it’s an overall market index including the S&P 500, FTSE 100, or DAX, or even a more particular benchmark say for example a Brazil real estate loan index. This benchmarking methodology is done to determine how an investment portfolio performed compared to the market in the past. Analysts will use the outcome to project expected returns in to the future under the assumption there won’t be any structural investment or marketplace changes. Over a reasonable length of time, long horizons such an analysis can often be useful.
Another usage of this type of application is for “what if” decisions regarding which securities could add higher returns or lower risk in the portfolio. This is a form of scenario analysis which includes two primary approaches, including optimization simulations (get the optimum quantity of shares of X, Y, and Z) and user-defined scenarios (what happens if I add one thousand shares of stock A to my portfolio). This analysis requires lots of historical return time series data, or at a minimum beta, volatility, or correlation matrix data as inputs to supply the calculations.
One final use of portfolio software programs are to calculate risk and return figures such as Value at Risk (VaR), and extracted ratios like the Sharpe or Treynor ratios. VaR is usually a way of measuring potential portfolio losses in the foreseeable future, stated in currency value for a particular confidence interval (i.e. $1.5 million with 95% probability). Sharpe, Treynor, Jensen Alpha, as well as other ratios are widely-used to compare different portfolios against the other and determine if the portfolio manager is underperforming against her peers.
When you use this type of program it is important to understand the negatives and constraints as well as the benefits above. One limitation is simply the inability for any tool or technique to predict tomorrow with a modicum of accuracy. Regardless of the underlying model used, no technique can predict the portfolio’s future returns with one hundred percent success, although it has been well proven that such tools have some beneficial predictive value. Another restriction is computing speed and data cost; the bigger and more complicated a portfolio becomes, the more varied the possible risk-return permutations are. This increases demands for data, storage space, and computing power at an accelerating rate, which adds expense and complexity. It is normally better to utilize less sophisticated models combined with aggregation or proxy methods like PCA where the size and cross section of investments inside a portfolio permit this.
Even considering these limitations, portfolio software can be very useful for smaller investors to help reduce their investment risk and enhancing returns.
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