- Tell us a little about yourself.
I was born and raised in Dallas, Texas. I know what you’re thinking…and no, I did not ride a horse to school [laughs]. I am also a recent graduate from Washington University in St Louis with a B.A. in statistics and minor in astrophysics and drama. After doing freelance design work in the St. Louis theatre community for a year, I joined Prattle as an intern in the summer of 2015.
- Before working at Prattle, how much did you know about finance, economics, and monetary policy?
I had taken basic micro and macroeconomics courses in college and had studied stocks a little on my own, but I had just a rudimentary understanding of finance and economics. I also knew absolutely nothing about monetary policy.
- How were you able to predict the Central Bank of Turkey’s rate cut…when almost no one else did?
Prattle’s data really helps put central bank communications in perspective. At Prattle, I was constantly reading communications from many different central banks. In February and early March 2016, speakers from Turkey’s monetary policy committee gave speeches hinting at a potential rate cut if they continued to see success in their rate corridor implemented last August. As the speeches were quite long and tedious, I initially skimmed over those sentences. However, by mid-March, I noticed a sharp decline in their sentiment data. This inspired me to go back and re-read the communications in depth, find those assertions, and begin forming a case for a rate cut.
Read post: “Turkey’s Rate Cut No Surprise to Prattle”
- What does Prattle’s central bank sentiment data mean to you?
Prattle’s central bank sentiment data to me means clarity. Every day, central banks produce mountains of text. Reading through all of these communications is tedious, and often major points slip through the cracks. Whether it’s verifying your interpretation or alerting you to a conclusion you may have missed, Prattle’s data brings meaning to these communications. In a world dominated by the communications and data from these institutions, being able to quantify sentiment allows for unexpected insights.
About Alexander Booth
Alexander Booth is a recent graduate of Washington University of St. Louis and holds a B.A. in Statistics. He currently works on the machine learning team for the industrial supply company McMaster-Carr. Prior to his current position, he worked as a Research Associate for Prattle, where he led the quality assurance team and developed algorithms to further improve Prattle’s central bank sentiment scores.
The Signal Team