Python-powered analysis of market trends

Every month has a different market flavor, from Chinese growth to easing of Federal Reserve policy. To illustrate this, I conducted natural language processing on daily market commentary. The result is a recap, for the last two years, of the various themes that the market has focused on.

Since 2023, we have seen the market fret about Chinese growth (summer 2023); over-anticipate Fed easing before paring back expectations (winter 23/24); focus on Japan as the Bank of Japan finally started hiking (spring 2024); analyze actual Fed easing (fall 2024); salivate on potential Chinese easing (winter 2024); and stress about tariffs (year-to-date).

We are now in the fourth month of tariffs being the main macro focus, and April thus far has seen a spike in the word “negotiation”.

Acknowledgements: The research I am analyzing is by BNZ, who leverage their timezone vantage point — as the first to see the trading day open — by producing a thorough recap of the previous day.

Method: I used my own Python code to load the reports, extract text from them, omit generic words like “the” and “lower”, count the most common words for every month, and use a mix of rules to determine every month’s macro meme. The code did all of this — over 500 reports to parse — in 178 seconds. You can contact me if you would like to see my code.

Seasonality: does it work?

Currency traders pay a lot of attention to seasonality. “NZDUSD tends to sell-off in August” is often cited as an argument (combined with other considerations, of course) to either short the pair or at least not go long.

Yes, NZDUSD tends to sell-off in August, but does that mean you should do it?

Come September, we will probably move on to the seasonality tendencies for that month, and there is seldom a review of whether seasonality “worked” in August.

So here I’m doing just that. I am “trading” every seasonality recommendation to test whether there is any value in this metric at all. If a currency pair exhibits strong seasonality for a month – i.e. it strengthened or weakened in 4 out of 5 months in previous years – then I long/short that pair.

The results are decent, but they’re neither unanimous (some currencies do better than others) nor consistent over time (lately, seasonality hasn’t been doing that well).

Seasonality has never been a particularly successful strategy for USDCHF or USDTWD
For currencies where it has been successful, that success petered out around 2017

Combined returns of a seasonality strategy for G10 excluding NOK and SEK

So the short answer is that this is a strategy whose best years are probably behind it. Since 2017, you wouldn’t have lost much on seasonality, but you would not have made much either.

Everything done on Python.

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.