On the Robustness of ARIMA-Based Benchmarks for Corporate Financial Planning Quality

Corporate financial planning relies on thousands of financial forecasts generated by human forecasters with varying performance (forecast errors). Previous work proposes ARIMA prediction as a competitive benchmark for manual forecasts. However, ARIMA can also produce large errors, and a company needs to understand sensitivity of ARIMA-outcome to time series characteristics before ARIMA-benchmarks can be established. Using forecast data provided by a global corporation, we present sensitivity analysis of ARIMA on shifts in fitting periods including the financial crises. Results show that ARIMA leads to rather robust performance, on average dominating human forecasters, with some huge errors not made by forecasters. We conclude that ARIMA can be applied to generate benchmarks in financial planning, which can then be refined to reflect novel expectations.