Using transcripts from Earnings Calls can help you determine the stock’s sentiment and move it in the right direction. By analyzing the sentiment of analysts and other stakeholders, you can uncover critical commentary on a company’s performance. While humans are better at interpreting positive and negative sentiment, they are not always accurate. This can be a problem if there are thousands of documents that need to be analyzed.
To use these transcripts, you must understand the nuances of a given company’s culture and values. Then, you can use the data to predict the stock’s sentiment. For example, if a company is known for its social media platforms, it might be a good idea to pay attention to the social media presence of its CEO. The social media buzz about a company’s CEO should also be closely monitored.
For a full analysis of the data, an NLP solution should be used. This solution will synthesize the transcripts and extract signals related to sentiment and targeted events. By comparing the sentiment of different companies, analysts can navigate by key language drivers and view trends over time. This method is particularly useful for the quantitative analysis of earnings calls, as it can reveal themes and commentary that are missed by conventional methods.
An earnings transcript is a detailed record of the company’s quarterly earnings call with Wall Street analysts. It contains the executives’ comments and questions. The unstructured transcripts are difficult to analyze because they are not structured. However, this format makes it possible to extract important insights from these transcripts. This can help you understand the company’s sentiment as well as its competitors’. With this method, you will be able to identify trends in a company’s performance.
With the help of a text analytics solution, you can extract important information from earnings call transcripts. A text analytics solution can automatically synthesize the transcripts and extract the signals related to sentiment and targeted events. Moreover, it can be used to navigate through the earnings call transcripts and see the trends across multiple companies. This method can also uncover hidden themes that are missed by traditional analysis. Hence, it is essential to use a solution that will help you to understand the sentiment of a company.
The use of natural language processing (NLP) is a powerful tool for financial analysis. In this way, you can analyze the sentiment of a company by analysing its earnings call transcripts. This data is valuable for the investors because it can reveal the mood of the analysts and the general public. If you want to analyze a company’s sentiment, you should analyze its transcripts and extract the sentiment of its executives.
Using text analytics, earnings call transcripts and sentiment can be analyzed with a machine-readable transcript. By leveraging the technology of natural language processing (NLP), you can automatically analyze text in earnings calls. You can even extract the sentiment of executives using the stock’s sentiment report. This is one of the ways NLP can help financial analysts. The technology can extract information from a company’s earnings call and provide valuable insights on the company’s outlook.
The dataset used to analyze earnings call transcripts includes the 188 transcripts of earnings calls and related stock prices and sector index values from 2016 to 2020. It also includes the daily volume of trades and the company’s overall sentiment. These transcripts have been analyzed using NLP and a text analytics solution. These solutions can help you find the most relevant messages to analyze. The datasets used for the study were created by combining the text of two sources.