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.

中文翻译：

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微数据分析：分析程序的测试

目的

尽管大数据分析具有潜力，但对处理分析程序的小型审计公司和注册会计师来说，微数据分析代表了预测记录金额和/或比率的预期值的主要方式。本研究旨在通过测试呆账准备金与贸易应收账款的比率的预测准确性以及商品和服务净销售额的自然对数（第一个面临更大的不确定性）来检验微数据分析的有效性比第二个。

设计/方法/方法

微数据的数量、种类、速度和可变性都很低，但准确性很高。考虑到影响微数据分析的过拟合问题，对这两个测试都进行了样本内和样本外的预测。使用 35 家意大利工业上市公司的样本进行多元回归和神经网络模型。

发现

预测模型的准确度水平是根据平均绝对百分比误差和其他准确度度量来确定的。在这两个测试中，神经网络模型提供了比多元回归更准确的预测，表明暴露于较少不确定性的金额的准确度水平更高。此外，无法就模型中包含的预测变量得出一般性结论。

实际影响

对预测准确性的检查有助于审计师评估在使用微观数据时是否可以成功地应用分析程序来检测错报，以及哪种模型可以提供最准确的预测。

原创性/价值

这是第一项测量使用微数据集执行的多元回归和神经网络模型的预测准确性的研究。预测准确性对于评估分析程序的有效性至关重要。