Embedding is a technique for converting text, images, or other forms of data into vector representations.
OpenAI's Embedding API utilizes extensive text training to convert any text into multi-dimensional vectors, making similar text closer together in vector space. These vectors can be used for searching, clustering, recommendations, anomaly detection, diversity measurement, and classification.
MacroMicro utilizes OpenAI's Embedding API to calculate the consistency level of each Federal Reserve statement
by converting them into vectors. The consistency level algorithm is 1 - vector distance, where distance is
calculated using cosine similarity. The x-axis and y-axis represent the dates of the statements, and
the color represents the level of consistency between two statements, with red (higher value) indicating
By analyzing the consistency heatmap, it is possible to determine whether the
Federal Reserve's stance has changed. For example, during the period from March 2020 to the end of 2021,
the consistency level between statements was high, coinciding with a period of loosened monetary policy
in response to the COVID-19 pandemic. However, after the Federal Reserve began its interest rate hiking cycle
in 2022, the consistency between statements during the pandemic period noticeably declined.