# Python vs Jupyer vs Spyder vs IDLE…

There are so many “words” around Python. There is clearly an entire ecosystem here. Here I break it down un-comprehensively.

Python is the language. You run Python on one of the following:

• Terminal / command prompt: This is the black screen with white/green text used by computer geniuses in movies. Very basic, you can’t click anything.
• Text editor: Not as “bare” as Terminal – has basic functionality. Examples include Atom, Vim, Notepad (yes you can run Python on the thing you use to jot down reminders). Technically Jupyter is not a text editor – it’s a web application – but it behaves like a text editor.
• Integrated Development Environment (IDE): comes with bells and whistles, which make working on Python easier. Using an IDE is like using Word instead of Notepad to write an essay. Examples include Spyder, PyCharm, IDLE.

Anaconda is a bundle which includes Python as well as Spyder (IDE) and a button which opens up Jupyter on your web browser. I use Anaconda to use Spyder to use Python, if that makes sense.

Which one is the best one to use? It depends on what you want and I have not tested them all, but I like Spyder for data science and analysis.

# Tracking Fed sentiment

Members of the Federal Open Market Committee, the body which decides the Fed’s interest rate policy, have their words closely scrutinized for hints about what the next policy change could be. Aside from the official policy-setting meetings, FOMC members give speeches throughout the year.

Here I use natural-language processing (NLP) to assign a score to each of those speeches, as well as official FOMC statements.

As expected, the index shows that recent Fed speeches have been relatively negative in their tone.

Method:

The formula I use to calculate a speech’s score is based off the number of positive words and negative words in that speech/statement.

$Score = \frac{Count_{positive}-Count_{negative}}{Count_{positive}+Count_{negative}}$

A score of +1 means that a speech had only positive words like “efficient”, “strong” and “resilient”, while -1 means it had only negative words like “repercussions”, “stagnate”, and “worsening”. The dictionary I use to determine whether a word is positive is based off (I have modified it) a 2017 paper published by the Federal Reserve Board1.

One also has to account for negation. A statement like “growth is not strong” has a positive word in it (“strong”) which should actually be counted as a negative word. As such, if a positive word is within three words of a negation word like “not” or “never”, then it is treated as a negative word. On the other hand, a negative word near a negation word (“growth is not poor”) is simply not counted, rather than treated as a positive word.

I downloaded the speeches and statements, did the NLP analysis, and produced the charts on Python.

Relation with yields:

Here I chart the Fed sentiment index against the US 2y yields, as well as the sentiment scores of the official meeting statements. The index moved higher from late 2016 to early 2018 as the Fed started hiking policy.

However in early 2018 the sentiment index indicated that the Fed had turned less positive, but yields continued moving higher as the hiking cycle continued. It is also important to note that sometimes a shift in FOMC thinking/language drives market price-action, and sometimes it is the other way round, so one cannot expect the index to always presage higher or lower yields.

As we go into the September FOMC meeting, where some people are expecting the Fed to announce yield curve control, keeping an objective eye on Fed sentiment will become even more important.

Abbas Keshvani

References:

1Correa, Ricardo, Keshav Garud, Juan M. Londono, and Nathan Mislang (2017) – Sentiment in Central Banks’ Financial Stability Reports. International Finance Discussion Papers 1203.

# What markets are focused on, part II

Following my recent post about the most referenced topic in FX commentary (in my case, excellent daily commentary from BNZ), I received a number of questions from readers about whether topic X was being talked about more or less.

So I visualized the data differently for all those interested – this time as time series. Each chart show the number of references made to a particular topic on a monthly basis.

References to the trade war, Fed and Trump increased in May. Meanwhile references to Covid-19 have been consistently sliding lower every month since March.

See last post for methodology. Everything done on Python.

Abbas Keshvani

# What markets are focused on

An updated version of this chart for June 2020 was shared with subscribers of TLR Wire, the esteemed economics newsletter managed by Philippa Dunne and Doug Henwood.

The financial sector produces a lot of commentary on the things affecting markets. A lot of this year’s commentary has been focused on Covid-19, but before that there was a lot of literature being produced on the US-China trade war and Brexit.

Here I chart, for every month, the most talked about issue in financial literature. I did this by pulling out hundreds of daily FX commentary pieces from BNZ (who do a solid job on recapping the previous day’s events) and analyzing the most used words (excluding the generic ones like “the” and “markets” and “economy”).

Naturally the total number of references to Covid-19 for a given month is not just the number of times “Covid-19” is printed, but also “coronavirus” and “virus”. A similar methodology is adopted for the US-China trade war.

While Covid-19 remained in the top 5 of topics for May, we can see the focus is starting to balance out, with the the Fed getting the most number of references as we approach the June meeting (which will have the Fed’s quarterly economic projections (which they skipped in March). There was also a pick-up in references to “Trump” and “trade” this month, suggesting that we aren’t quite done with the US-China theme.

Data mining, text-analysis and chart all done on Python.

Abbas Keshvani

# The Fed’s balance sheet

The Federal Reserve (or “Fed”) is the central bank of the United States, in charge of setting interest rates, regulating banks, maintaining the stability of the financial system, and providing financial services such as swap lines (which temporarily provide foreign central banks with dollars).

The Fed has its own balance sheet, which means its owns assets such as US government bonds (“Treasuries”) and has liabilities such as reserves (cash which financial institutions keep with the Fed) and currency (which technically counts as a liability because the Fed “owes” you things for the dollars you hold – historically it was gold, but now it is other assets such as bonds).

• In the aftermath of the Great Recession from 2008, the Fed undertook Quantitative Easing (QE), which means it created new money to buy bonds and loans. This increased its balance sheet from roughly $1 trillion in 2008 to$4.5 trillion in 2014.
• From 2014 to 2018, the Fed stopped buying additional bonds and loans under QE, and its balance sheet stabilized.
• From 2018 to 2019, the Fed started to sell some of its assets, but this only reduced the balance sheet to around $3.8 trillion. • Around the Covid-19 outbreak, the Fed started buying assets again and also temporarily provided dollars to other central banks. This has ballooned the Fed’s balance sheet to around$6.6 trillion today.

Graph produced on Python, data from Federal Reserve.

Abbas Keshvani