How can I apply sensitivity analysis to my investment decisions?

Use sensitivity analysis to estimate the effects of different variables on investment returns. This form of analysis is designed for project management and profitability forecasts, but you could use it for any type of uncertain projection. The practical benefit of using sensitivity analysis for your investment decisions would be to assess risks and potential error.

Perhaps the most common investment application of sensitivity analysis involves adjusting the discount rate or other streams of cash flows. This allows you to re-evaluate risks based on specific adjustments.

Taken one step further, sensitivity analysis offers an insight into how your investment strategy is structured. You can use it to compare investment models by demonstrating how profitability depends on underlying model data or other assumptions.

Sensitivity analysis does not produce any specific prescriptions or generate any trading signals. It is left up to the individual investor or project manager to decide how best to utilize the generated results.

Sensitivity analysis is a calculation procedure that predicts the effects of changes of input data. Investment decisions are wracked with uncertainty and risk. Most investment models have explicit and implicit assumptions about the behaviors of models and the reliability and consistency of input data.

If these underlying assumptions and data prove incorrect, the model loses its effectiveness. By applying sensitivity analysis, you can examine input values, such as costs of capital, income and the value of investments.

The fundamental purpose of sensitivity analysis is twofold: insight into the impact of critical model-based parameters and the sensitivity of model-produced profitability to those parameters.

To perform sensitivity analysis for your investment models, first identify a set of criteria by which to evaluate the investments' success. These criteria must be quantitative. Normally, this can be set as rate of return (ROR).

Next, define a set of input values that are important to the model. In other words, find out which independent variables are most important in generating ROR. These can include discount rates, asset prices or your personal income.

Next, determine the range over which these values can move. Longer-term investments have larger ranges than shorter-term investments.

Identify the minimum and maximum values that your input variables (and other criteria as necessary) can take while the investment model remains profitable (generating a positive ROR).

Lastly, analyze and interpret the results of moving factors. This process can be simple or complex based on the types of input variables and their effect on ROR.

Investments are complex and multifarious. Investment evaluations might depend on asset prices, exercise or strike prices, rates of return, risk-free rates of return, dividend yields, accounting ratios and countless other factors.

Sensitivity analysis only generates results based on movements for critical independent variables. Any variables not singled out – for which there are many for any given investment decision – are assumed to be constant.

Independent variables seldom move independently. Independent variables and nonmeasured variables tend to change at the same time.