Earnings management is a means by which managers manipulate earnings to conceal the true performance of a company. The characteristics of the board of directors can also influence firm performance. This study applies data envelopment analysis (DEA) and the Tobin regression model to investigate the influence of earnings management and board characteristics on company efficiency. The data sample includes 396 Taiwanese electronics and biotechnology companies from 2009 to 2017. The results indicate that earnings management has an insignificant influence on company efficiency with mixed results on the interactions between earnings management and board characteristics. When companies practiced earnings management, director experiences, a higher proportion of female directors, and a higher number of board meetings increased company efficiency. In contrast, a higher number of independent directors and a higher attendance rate of the directors at the board meeting decreased company efficiency. The results of this study suggest that board diversity, more female directors, and meetings could still improve firm performance despite companies’ engagement in earnings management.

Compared to widely examined topics in the related literature, such as financial crises/difficulties in accurate prediction, studies on corporate performance forecasting are quite scarce. To fill the research gap, this study introduces an advanced decision making framework that incorporates context-dependent data envelopment analysis (CD-DEA), fuzzy robust principal component analysis (FRPCA), latent Dirichlet allocation (LDA), and stochastic gradient twin support vector machine (SGTSVM) for corporate performance forecasting. Ratio analysis with the merits of easy-to-use and intuitiveness plays an essential role in performance analysis, but it typically has one input variable and one output variable, which is unable to appropriately depict the inherent status of a corporate’s operations. To combat this, we consider CD-DEA as it can handle multiple input and multiple output variables simultaneously and yields an attainable target to analyze decision making units (DMUs) when the data present great variations. To strengthen the discriminant ability of CD-DEA, we also conduct FRPCA, and because numerical messages based on historical principles normally cannot transmit future corporate messages, we execute LDA to decompose the accounting narratives into many topics and preserve those topics that are relevant to corporate operations. Sequentially, the process matches the preserved topics with a sentimental dictionary to exploit the hidden sentiments in each topic. The analyzed data are then fed into SGTSVM to construct the forecasting model. The result herein reveals that the introduced decision making framework is a promising alternative for performance forecasting.