Using a Dictionary-Based Approach to Calculate Hawkish-Dovish Scores
by Xian, post on Thu 29 August 2024The dictionary-based approach to sentiment analysis is widely used due to its simplicity and transparency. This post explores how this approach can be applied to Fed communications to calculate hawkish-dovish scores, providing insights into the sentiment conveyed in these statements.
Textual Data Analysis of Fed Communications
by Xian, post on Mon 12 August 2024Textual analysis serves as a powerful tool for examining large volumes of text. By applying both qualitative and quantitative techniques, it provides insights into the evolution of Fed communications. This post explores how textual analysis uncovers the nuances and trends in the Fed's communications, offering a comprehensive understanding of their intentions and actions.
Web Scraping Techniques for Fed Communications
by Xian, post on Wed 07 August 2024This post explains web scraping techniques for Fed communications using Python libraries like Requests, BeautifulSoup, and pdfminer. It covers fetching JSON data, extracting text from HTML and PDF formats, and emphasizes ethical scraping practices and handling different file formats.
Free Financial and Economic Data Download Source: FRED Using Python
by Xian, post on Fri 02 August 2024This post guides through accessing financial and economic data from the Federal Reserve Economic Data (FRED) using Python. The post explains how to obtain an API key, install necessary libraries (fredapi and pandas), and fetch data from FRED using Python.
Analyzing Fed Communications Using NLP
by Xian, post on Thu 01 August 2024This blog series explores how Fed communications affect financial markets using NLP techniques. It quantifies hawkish or dovish sentiments with dictionary-based approach and advanced models like BERT and LLaMa. Future posts will cover data acquisition, NLP preprocessing, sentiment models, and their market impact.