以文字探勘技術分析致股東報告書與績效間之關聯性

Abstract

本研究以 2018 年問世之自然語言處理方法中的 BERT (Bidirectional Encoder Representation from Transformers) 模型為基礎，將其所衍生出的機器學習方法用以剖析臺灣上市櫃科技產業致股東報告書之資訊價值，並探討其文字內容與財務績效間之關聯性，看能否藉由致股東報告書內容及語調中分析出其與同產業績效平均值的高低關係及其對於企業本身未來績效成長的影響力。 研究中所使用之機器學習方法有兩種，一為將致股東報告書經前處理後轉換成 BERT CLS 向量及經 LDA 後向量，再把兩向量接起來，並以此一新向量形式放入傳統機器學習模型訓練與預測；另一為將致股東報告書經前處理後直接放入 BERT 模型中進行微調並產出預測結果。 實證結果發現，以營業收入淨額作為定義財務績效分類之依據，且與致股東報告書一同進行訓練的情況下，模型預測準確度十分理想，故推論出致股東報告書與財務績效間確實存在關聯性，且致股東報告書內容及語調可用於預測其現在或未來績效是否會高於同產業平均及與自身相比成長與否。

This study analyzed the information value of Report to Shareholders covering all technology companies listed in Taiwan with machine learning methods primarily based on BERT, and explored the impact of text content and tone of Reports to Shareholders on a company’s financial performance, including the company’s financial position when compared with the average performance in the same industry and the future performance growth of the company. In addition, two machine learning methods were utilized in the study. One of the method converted Reports to Shareholders into BERT CLS embedding and vector learned by LDA model, combined the two into a new vector form, and fed the new vector into traditional machine learning models for training and prediction; the other method generated prediction results by directly employing BERT to analyze pre-processed Reports to Shareholders and fine-tuning BERT parameters. The empirical result showed that when using the net operating revenue to define the classification of the financial performance, the accuracy of the model results was great. It could be inferred that there is indeed a correlation between Reports to Shareholders and a company’s financial performance. The content and tone of Reports to Shareholders could thus be used to predict whether a company’s performance will be higher than the industry average and whether it will outgrow its present net operating revenue.