Tracking the RBI

At the August meeting of the RBI, the Indian central bank kept the repo rate, its benchmark interest rate, unchanged. Around half of economists had expected a policy rate cut (India is in a pandemic after all!). But inflation in the country has exceeded the upper-bound of the RBI’s target range, and a lot of economists correctly forecasted that the central bank would not cut.

Between speeches, statements, and economic data, there is a lot to track for the RBI. So I have produced an RBI sentiment index in an attempt to objectively quantify and automate the tracking of:

Monetary policy statements (around 4-6 per year)
Speeches (over 20 last year)
CPI prints (12 per year)

Interpretation: The index is a moving average of the last 10 statements/speeches/CPI prints and can oscillate between -1 and +1. A score of +1 means that the RBI sounds optimistic about the economy and inflation is high, so one can reasonably expect the policy rate to be increased. -1 means the RBI sounds pessimistic and that inflation is low, so we can expect rate cuts.

As you can see the index has come off quite a bit since March, mostly due to pessimistic rhetoric as Covid-19 has taken a toll on India’s economy. The index would have fallen further if not for high inflation prints recently. But that is the point: the index should reflect the constraints imposed by inflation (i.e. you can be pessimistic about the economy but unable to cut due to high inflation).

An interesting overlay is between this index and bond yields. Low bond yields indicate that the market expect the central bank to cut rates, but as you can see, the RBI might be thinking differently right now. Even if you look past high inflation, some of the recent speeches and the August meeting statement show an improvement in RBI sentiment.

Details of the index:

NLP to automate scoring of rhetoric: I have used Python to automate the process of scraping all speeches and scoring them on a scale of -1 (pessimistic about the economy) to +1 (optimistic). While I was at it, I also did the same process for all RBI statements. The methodology is explained in more detail here.

Adding inflation data to the mix: I was worried that RBI speeches and statements were not adequately discussing inflationary pressure. This is particularly problematic for a country like India, which dealt with double digit inflation as recently as 2013 and where the CPI index has swung from 2.1% in January 2019 to 6.9% in July 2020. Compare that to the US, where core PCE inflation has mostly observed a humble range of 1-2%.

Everything done on Python.

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 (around eight per year), FOMC members give speeches throughout the year (78 in 2019).

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.

Everything done on Python.

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.

NLP panel

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”).

What markets are focused on

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).

Fed BS

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

Expect less oil supply

In my last post, I talked about how shale supply has depressed oil prices by increasing its supply. Recall this graph which shows that the increased supply is primarily from America (the top pink line):

The glut is mostly due to America producing more oil — America producing more oil

Since lower prices are mainly enabled by American shale supply, a decrease in shale supply will have a major impact on prices. And it does look like shale supply might wind down. The next graph shows how American oil production responds (eventually) to the number of oil rigs in America.

Just to clarify, “rigs” here refers to rotary rigs – the machines that drill for new oil wells. The actual extraction is done by wells, not rigs. But US oil supply shows a remarkable (lagged) covariance with rig count. From the 1990 to 2000, the number of rigs decreased, and oil supply followed it down. Then, when the number of rigs jumped in 2007, oil supply also rose with it.

Note that the number of American rigs has plummeted since the start of 2015. It is no coincidence that oil prices hit a record low in January 2015. At these paltry prices, oil companies have less of an impetus to dig for more oil.

The number of oil rings in America has halved since January 2015

The break-even price for shale oil varies according to the basin (reservoir) it comes from. A barrel from Bakken-Parshall-Sanish (proven reserves: 1 billion barrels) costs $60, while a barrel from Utica-Condensate (4.5 billion barrels) costs $95. The reserve-weighted average price is $76.50. These figures were calculated by Wood McKenzie, an oil consulting firm, and can be viewed in detail here.

As the number of rigs has halved to 800, the United States will not be able to keep up its record supply. Keep in mind that wells are running dry all the time, so less rigging will eventually mean less oil. Perhaps shale supply will decrease, with consequences for oil prices. To put things in perspective, the last time America had only 800 rigs (end January 2011), oil was at $97 a barrel.

Oil probably will not return to $100 a barrel. If it does, shale oil will become profitable again (the threshold is $76), American shale rigs will come online again, supply will increase and prices will come down again. So oil will have to find a new equilibrium price to be stable. A reasonable level to expect for this equilibrium is around $70, the break-even price for shale.

There will probably be a lag in the reduction of American supply: Note how oil supply does not immediately respond to the number of rigs. But things move faster when expectations are at play. On the 6th of April, traders realized Iranian rigs were not going to come online as fast as they thought. Oil prices rose 5% in one night. American supply does not have to come down for prices to drop: traders simply have to realize prices will come down.

Data from US Energy Information Agency and Baker Hughes, an oil rig services provider. Graphs plotted on R.

Abbas Keshvani

World Wide Wage

South Koreans earn, on average, $33,140 per year (PPP), making them almost as rich as Britons. However, Koreans also work 30% more hours than Britons, making their per-hour wage considerably less than a British wage. In fact, the Korean wage of $15 per hour (PPP) is comparable to that of a Czech or Slovakian.

Here is a map of the working hours of mostly OECD countries.

As you can see, people in developing countries have to work longer hours. The exception is South Korea, which works pretty hard for a rich country – harder than Brazil and Poland do. If you divide per-capita income by working hours, you get a person’s hourly wage:

The top 10 earners by wage are mostly northern Europeans – Norway, Germany, Denmark, Sweden, Switzerland, Netherlands – and small financial centres Singapore and Luxembourg. As the first to industrialize, Europeans found they were able to mechanize ploughing, assembling and number-crunching – boosting incomes, while simultaneously decreasing working hours.

The bottom earners are developing countries – such as Mexico, Brazil and Poland. Again, Korea stands out as a rich country with low wages. This could be because Korea exported their way into prosperity by winning over Western consumers from the likes of General Motors and General Electric. They did this by combining industrialization with low wages, which are therefore responsible for the ascent of their economies.

Data from World Bank, OECD, and BBC. Maps created on R.

Abbas Keshvani

If Scotland becomes a country

On the 18th of September 2014, Scottish people will vote on secession from the United Kingdom, potentially ending a union that has existed since 1707. If Scots vote “Yes” to end the union, the United Kingdom will consist of England, Wales and Northern Ireland, while the newly created country of Scotland may look like this:

Basically, Scotland would look a lot like Finland. The two countries have similar populations, GDP, and even their respective largest cities are about the same size.

Abbas Keshvani

Indian Elections 2014 – a Summary

India conducted general elections between 7th April and 12th May , which elected a Member of Parliament to represent each of the 543 constituencies that make up the country.

The opposition BJP won 31% of the votes, which yielded them 282 out of 543 seats in parliament, or 52% of all seats. The BJP allied with smaller parties, such as the Telugu Desam Party, to form the National Democratic Alliance (NDA). Altogether, the NDA won 39% of the votes and 336 seats (62%).

Turnout was pretty good: 541 million Indians, or 66% of the total vote bank, participated in the polls.

Google and Bing both performed excellent analytics on the election results, but I thought Bing’s was easier to use since their visual is a clean and simple India-only map. They actually out-simpled Google this time.

You are more likely to vote BJP if you speak Hindi — Bing: A constituency is more likely to elect BJP (orange) if its people speak Hindi

Interestingly, the BJP’s victories seem to come largely from Hindi speakers, traditionally concentrated in the north and west parts of India. Plenty of non-Hindi speakers voted for the BJP too, such as in Gujarat and Maharashtra, but votes in south and east of the country generally went to a more diverse pantheon of parties.

Abbas Keshvani

Crime map for the City of London

In my experience, central London is generally a safe place, but I was robbed there two years ago. A friend and I got lost on our way to a pancake house (serving, not made of), so I took my new iPhone out to consult a map. In a flash, a bicyclist zoomed past and plucked my phone out of my hands. Needless to say, I lost my appetite for pancakes that day.

But I am far from alone. Here, I have plotted 506 instances of theft, violence, arson, drug trade, and anti-social behaviour onto a map of London. The data I am using only lists crimes in the City of London, a small area within central London which hosts the global HQs of many banks and law firms, for the month of February 2014.

Crime in the City of London - February 2014 — Crime in the City of London – February 2014

Each point on this map is not a single instance of crime – recall that the data lists over 500 instances of crime. So, each point corresponds to multiple instances of crime which happened at a particular spot. So, it is probably best to split the map into hexagons (no particular reason for my choice of shape) which are colour coded to explain how dense the crime in that area is.

Heatmap of crime in Central London - Feb 2014 — Heatmap of crime in Central London – Feb 2014

A particular hotspot for crime appears to be the area around the Gherkin, or 30 St Mary’s Axe, Britain’s most expensive office building.

Data from data.police.uk; Graphics produced on R using ggplot2 package; Map from Google maps.

Abbas Keshvani

CO2 Emissions per Dollar of GDP

For all the flak China receives about its greenhouse gas emissions, the average Chinese produces less than a third the amount of CO2 than his American counterpart. It just so happens that there are 1.3 billion Chinese, and 0.3 billion Americans, so China ends up producing more CO2.

Carbon dioxide and other greenhouse gases, such as methane and carbon monoxide, are produced from burning petrol, growing rice, and raising cattle . These greenhouse gases let in sun rays, but do not let out the heat that the rays generate on earth. This results in a greenhouse effect, where global temperatures are purported to be rising as a result of human activities.

The below map shows the per-capita emissions of greenhouse gases:

As you can see, the least damage is done by people in Africa, South Asia, and Latin America. But these places also happen to be the poorest places: Because they don’t have much industry, they don’t churn out much CO2.

The below plot shows the correlation between poverty and green-ness. As you can see, each dollar of a rich person is attached to a smaller carbon cost than the dollar of a poor person. This is partially because rich people get most of their manufacturing done by poor people, but also because rich people are more environmentally conscious.

Lastly, here is a map of CO2 emissions per dollar of GDP, which shows how green different economies are:

CO2 emissions per Dollar of output are lowest in:

EU and Japan: highly regulated and environmentally conscious
sub-Saharan Africa: subsistence-based economies

…and highest in the industrializing economies of Asia.

Kudos to Brazilian output for being so green, despite the country’s middle-income status. Were these statistics to factor in the CO2 absorption from rainforests, Brazil and other equatorial countries would appear even greener.

Data from the Word Bank. Graphics produced on R.

Abbas Keshvani

University Rankings over Time

The QS Rankings are an influential score sheet of universities around the world. They are published annually by Quacquarelli Symonds (QS), a British research company based in London. The rankings for 2013 are out, and I have charted the rankings of this year’s top 10 over the last five years:

QS’s top 10 from 2008 to 2013; The label is the 2013 rank. Columbia is included because it was in the top 10 of 2008 and 2010.

Observations from this year’s ranking:

MIT (#1 in 2013) has shot up in the rankings. This is in line with the increasing demand for technical and computer science education. At Harvard, enrollment into the college’s introductory computer science course went up, from around 300 students in 2008 to almost 800 students in 2013!
Asia’s top scorer is National University of Singapore

Method:

The QS Rankings produce an aggregate score, on a scale of 0-100, for each university. The aggregate score is a sum of six weighted scores:

Academic reputation: from a global survey of 62,000 academics (40%)
Student:Faculty ratio (20%)
Citations per Faculty: How many times the university’s research is cited in other sources on Scopus, a database of research (20%)
Employer reputation: from a global survey of 28,000 employers (20%)
Int’l Faculty (Students): proportion of faculty (students) from abroad (5% each)

Note that many of the universities are apart by tiny numbers (MIT, Harvard, Cambridge, UCL, Imperial are all within 1.3 points of each other), which increases the likelihood of bias or error influencing the ranking.

In any case, it appears futile to try and compare massive multi-disciplinary institutions by a single statistic.

However, larger trends – like MIT’s and Stanford’s ascendancy – are noteworthy.

Data from QS Ranking. Graphics produced on R.

Abbas Keshvani