Sentiment Analysis of 10-K Filings: An Approach to Automatic Processing of the Information Hidden in Accounting Narratives

Abstract

In 10-K reports, there are numerous amounts of financials and accounting narratives available upon which investment decisions can be made. However, while financials relatively straightforward can be used to explain the performance of the firm, it is a more difficult task to use accounting narratives. Accounting narratives can contain information relevant to investors, such as expectations about the future and risk measures, which are not captured by the financials. However, given the scope of the 10-K report, it is a daunting task to find this information. This thesis seeks to provide a step in the direction of a more easy and automatic processing of the information hidden in the accounting narratives. The focus is on sentiment analysis since this provides a crude measure of whether the information contained in accounting narratives in the 10-K reports is favorable or unfavorable. The study is centered around a comparison of two sentiment analysis methods: The Bag of Words model and the Recursive Neural Tensor Network. In order to assess which model is superior in a financial setting, their extracted sentiment of the narratives in 10-K reports will be evaluated by their ability to explain stock returns. The approach of these models is to classify sentiment on a word and sentence level respectively, and they therefore represent a simple and a more sophisticated approach to textual analysis. The initial results showed that while the adjusted 푅 2 was remarkably low there was a statistically significant relationship between the models’ sentiment score and the stock returns. However, after testing the validity of the results by adjusting the returns for systematic risk and including control variables, only the sentiment score of the Bag of Words model was significant in explaining the stock returns over the 10-K filing date. It is, therefore, concluded that further development of the Recursive Neural Tensor Network in order for it to be applicable to the financial domain is beneficial for the field of accounting research.