臺灣博碩士論文加值系統

近年來有許多研究將機器學習應用於財務方面的股價走勢與風險預 測。透過分析股票價格、財報的文字資訊、財經新聞或者更即時的推 特推文,都有不同的應用方式可以做出一定程度的投資風險評估與股 價走勢預測。在這篇論文中,我們著重在財務報表中的文字資訊,並 利用文字資訊於財務風險評估的問題上。我們以財報中的文字資訊預 測上市公司的風險程度,在此論文中我們選用股價波動度作為衡量財 務風險的評量方法。在文字的處理上,我們首先利用財金領域的情緒 字典改善原有的文字模型,情緒分析的研究指出情緒字能更有效率地 反應文章中的意見或是對於事件的看法,因而能有效地降低文字資訊 的雜訊並且提升財報文字資訊預測時的準確率。其次,我們嘗試以權 重的方式將股價與投資報酬率等數值資訊帶入機器學習模型中,在學 習模型時我們根據公司財報中的數值資訊,給予不同公司財報中的文 字資訊權重,並且透過不同權重設定的支持向量機將財報中的文字資 訊結合。根據我們的實驗結果顯示,財務情緒字典能有效地代表財報 中的文字資訊,同時,財務情緒字與公司的風險高度相關。在財務情 緒字以權重的方式將股價與投資報酬率結合的實驗結果中,數值資訊 顯著地提升了風險預測的準確率。

In recent years, there have been some studies using machine learning techniques to predict stock tendency and investment risks in finance. There have also been some applications that analyze the textual information in fi- nancial reports, financial news, or even twitters on social network to provide useful information for stock investors. In this paper, we focus on the problem that uses the textual information in financial reports and numerical informa- tion of companies to predict the financial risk. We use the textual information in financial report of companies to predict the financial risk in the following year. We utilize stock volatility to measure financial risk. In the first part of the thesis, we use a finance-specific sentiment lexicon to improve the pre- diction models that are trained only textual information of financial reports. Then we also provide a sentiment analysis to the results. In the second part of the thesis, we attempt to combine the textual information and the numeri- cal information, such as stock returns to further improve the performance of the prediction models. In specific, in the proposed approach each company instance associated with its financial textual information will be weighted by its stock returns by using the cost-sensitive learning techniques. Our experi- mental results show that, finance-specific sentiment lexicon models conduct comparable performance to those on the original texts, which confirms the importance of financial sentiment words on risk prediction. More impor- tantly, the learned models suggest strong correlations between financial sen- timent words and risk of companies. In addition, our cost-sensitive results significantly improve the cost-insensitive results. As a result, these findings identify the impact of sentiment words in financial reports, and the numerical information can be utilized as the cost weights of learning techniques.

致謝 3
中文摘要 4
Abstract 5
1 Introduction 1
2 Related Work 5
2.1 FinancialRiskPrediction.......................... 5
2.2 SentimentAnalysis ............................. 6
2.3 Cost-SensitiveClassification ........................ 6
3 Methodology 9
3.1 DefinitionofFinancialTerms........................ 9
3.1.1 DailyStockReturns ........................ 9
3.1.2 StockReturnVolatility....................... 10
3.2 Finance-SpecificSentimentLexicon .................... 10
3.3 ProblemFormulation ............................ 12
3.3.1 RegressionTask .......................... 12
3.3.2 RankingTask............................ 12
3.3.3 Cost-SensitiveTask......................... 13
4 Experimental Results 17
4.1 ExperimentalSettings............................ 17
4.1.1 Dataset ............................... 18
4.1.2 ExtractedFeatures ......................... 18
4.1.3 EvaluationMetrics ......................... 20
4.1.4 ParameterSettings ......................... 20
4.2 Finance-Specific Sentiment Lexicon Based Model . . . . . . . . . . . . . 21
4.3 Cost-SensitiveBasedModel ........................ 23
5 Conclusions 27
Bibliography 29

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