Micro Data analytics: a test for analytical procedures

Purpose: Despite the potential of Big Data analytics, the analysis of Micro Data represents the main way of forecasting the expected values of recorded amounts and/or ratios for small auditing firms and certified public accountants dealing with analytical procedures. This study aims to examine how effective Micro Data analytics are by testing the forecast accuracy of the ratio of the allowance for doubtful accounts to the trade accounts receivable and the natural logarithm of the net sales of goods and services, the first exposed to a greater uncertainty than the second. Design/methodology/approach: Micro Data are low in volume, variety, velocity and variability, but high in veracity. Given the over-fitting problems affecting Micro Data analytics, the in-sample and out-of-sample forecasts were made for both tests. Multiple regression and neural network models were performed using a sample of 35 Italian industrial listed companies. Findings: The accuracy level of the forecasting models was found in terms of mean absolute percentage error and other accuracy measures. The neural network model provided more accurate forecasts than multiple regression in both tests, showing a higher accuracy level for the amounts exposed to less uncertainty. Moreover, no generalized conclusions on predictors included in the models could be drawn. Practical implications: The examination of forecast accuracy helps auditors to evaluate whether analytical procedures can be successfully applied to detect misstatements when Micro Data are used and which model gives the most accurate forecasts. Originality/value: This is the first study to measure the forecast accuracy of the multiple regression and neural network models performed using a Micro Data set. Forecast accuracy is crucial for evaluating the effectiveness of analytical procedures.