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  • 學位論文

整合專利、財務及ESG指標建立企業績效預測模型之研究

Firm Performance Prediction Using Patent, Finance Index and ESG Index

指導教授 : 賴佳瑜
本文將於2026/08/10開放下載。若您希望在開放下載時收到通知,可將文章加入收藏

摘要


在全球進入大數據時代的背景下,進行資料統計與數據分析已成為近期趨勢,例如:預測企業未來之財務走向與變化,大多數皆是以單一面向變數去分析對企業財務之影響,意義在於確認自變數與應變數之間的關係。除了財務報表的量化數據之外,在許多研究中可以發現,公開披露的文件(專利、年度報表)已成為現今預測公司績效的重點,有趣的是,近年來有關永續發展的研究,也漸漸活絡。關於影響企業財務之因素眾多,故本論文為結合多面向變數探討與財務間之關聯,透過企業持有之專利、年報情緒、財務報表之特徵等,本論文還加入了環境、社會及公司之治理原則(ESG),採用機器學習分類器XGBoost、LightGBM與Random Forest作系統化的評估,建立預測企業績效之模型。實驗結果顯示,專利與ESG特徵對於每股盈餘(EPS)及托賓Q法具有顯著影響,年報情緒對於EPS的預估準確率也有顯著影響,且財務之特徵也具備一定的影響,因此可以判斷企業改善專利、年報情緒、財務特徵和ESG指數,這幾項因素是有助於公司未來的發展。

並列摘要


In the era of big data, data analytics has become a recent trend. For instance, most prior studies on predicting the future financial direction and enterprises change focus on using a single variable-oriented analysis to examine the impact of corporate finance. In addition to the quantitative data on financial statements, many studies also attempt to adopt publicly disclosed documents (patents, annual statements, and especially sustainable development reports) to forecast company performance. The aim of this paper is to propose a predictive model for firm future performance by extracting financial indicators and multi-faceted features based on patents, annual reports, financial statements, and in particular, environmental, social, and corporate governance (ESG) index. We also apply machine learning classifiers, including XGBoost, LightGBM and Random Forest to establish a model for predicting enterprise performance. Experimental results show that patent and ESG characteristics have a significant impact on earnings per share (EPS) and Tobin Q method, the annual report sentiment for EPS estimate accuracy also has a significant impact, and financial characteristics also have a certain impact on firm performance. Our study can help enterprises improve patents, annual report sentiment, financial characteristics and ESG, where these factors are conducive to the future development of the company.

參考文獻


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