Excel autocorrelation

Hello from SuperstonksQuants (HomeDepotHank69’s quant group)! For those of you who were not around, u/HomeDepotHank69 posted a series of excellent quant posts a while ago, and formed a group of quant-oriented apes to tackle some difficult questions. His account is now deleted, but we’re trying to keep his spirit alive. Some of us (including myself) have not been active for a few months for a variety of reasons, but there are still a lot of wrinkles there working. I found out recently that my original post on statistics was removed, so I’d like to post an updated version here.
But first, I want to quickly remind everyone to keep up your privacy! Several of my fellow quants got ‘attacked’ on social media after they posted some personal information. As far as I know, nothing too serious has happened, but as we get closer to MOASS they might start using dirtier tactics.
Tldr: Standard correlations (Pearson, Spearman, etc.) are misleading when trying to compare stocks. We found a fairly simple way to fix the issue by transforming the data using differencing first, then running cross-correlations or rolling correlations on the transformed data. A fellow quant ape, u/orangecatmasterrace, created a shiny app so everyone can compare tickers without needing any coding experience: https://orangecatmasterrace.shinyapps.io/stonk\_app/
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This is a longgg post, so here is a table of contents of sorts:
1. Introduction to statistics
2. Standard correlations
3. Major issue with standard correlations when looking at stocks
4. Removing autocorrelation from the data
5. Proof of concept
6. Rolling correlations (not included in my original post)
7. Tool so you can do it without any coding!
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This is not only my work, but of many other SuperstocksQuants members (particularly u/orangecatmasterrace, u/xpurpleamyx, and several more that requested to remain anonymous). My qualifications - I teach statistics at the university level.
Now, on to STATISTICS!
Introduction to statistics
One major strength of apes is our determination to find the truth from actual data, not relying on fb posts from our uncles. Statistics is just a method for working with data, and to answer the question, how confident are you in the effect? This is a big deal when you are making claims about worldwide illegal activity, so we want to make the tools available to all apes.
I am on the low-level analysis side, and wanted to bring you all up to speed on how to answer questions like: Is X related to Y? This is one of the most common types of questions we receive, so that is what I have been focused on. It turns out, this is more complicated to answer than you might think. If you google how to see if two stocks are related, even Yahoo Finance will tell you to run a correlation. Standard correlations are misleading due to the type of data we are working with.
That being said, I will start with the most common type of correlation, the Pearson’s correlation coefficient, so we can talk about stats in general and then move on to my recommendations on what to use.
Standard correlations
A correlation is used when you have two continuous variables (like two stocks), and you want to see if they are related. The standard correlation combines two things: how well the two variables move together (covariance) and how spread out the data is (variance). THIS IS AMAZING, correlations are seriously clever. However, every statistical analysis comes with assumptions (the fine print) that even peer reviewed publications sometimes miss.
The most common correlation analysis is a Pearson’s correlation coefficient. When you run a Pearson’s correlation, you get two numbers labeled r and p. The r is a value between -1 and 1, the absolute value of which reflects the strength of the relationship, and the sign indicates the direction (positive – as one variable increases, the other increases; negative – as one variable increases, the other decreases). Here are some visual examples (the x and y axis are your two variables, such as GME and XRT).
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Source: https://statistics.laerd.com/
As we can see, when two variables move together, their r is large (left graphs) and if two variables are not related, then r is close to 0 (right graphs).
From your r and the size of the dataset, you can get a p-value. This is a value you get from most statistical analyses (not just correlations) that answers that question I mentioned above, how confident are you? The p-value is a number that ranges between 0 and 1. The lower the value, the more confident you can be that the effect exists. For correlations, the lower the value the more sure we are that the two stocks (or whatever) are related.
In more detail, the p-value is the probability that you found the effect due to chance (sort of). That is, there is always some probability that two stocks will vary together just due to random chance, rather than because of some underlying market manipulation. The p-value helps us find that probability.
As your r gets farther from 0, the smaller your p is likely to be. In the graph above, you would find the lowest p-value for the two graphs on the left, slightly larger for the two middle graphs, and a p-value close to 1 for the two graphs on the right.
Major issue with standard correlations when looking at stocks
Let’s try some real data. If I look at the Pearson Correlation between GME and the popcorn stock, I find r = 0.71, p = 2.2 e -16. That’s a p value with more zeros than I even thought possible! Now let’s look at something that should not be correlated, GME and SPY (r = 0.78, p = 2.2 e -16). They are basically the same. They are so similar I had to continually check my code. This is certainly not true for all stocks, I just found it to be a good example. [Note: these values are from my original post and may have changed slightly since then]
Why might this be?
All statistical analyses come with fine print, or assumptions, that must be met for the analysis to work. At the bottom of this linked page, there is a nice list of the assumptions for Pearson correlations, along with explanations: https://statistics.laerd.com/statistical-guides/pearson-correlation-coefficient-statistical-guide.php
The biggest issue with stock data is it breaks the independence assumption (assumption 3). The independence assumption basically says that each observation within a variable should be independent of other observations in the same variable.
If we return to the correlation figure above, typically each ‘dot’ or each data point comes from one ‘participant’. For example, say I have 15 apes and an all-inclusive banana buffet. I measure the size of each ape and how many bananas they eat. I’d likely see something similar to the upper left graph (the larger the ape, the more bananas they eat). A crucial aspect of data like this, is that the number of bananas eaten by one ape does not depend on the number of bananas another ape has eaten. That is, one dot or one data point does not depend on another.
In stock data (or crypto), this is not true. The opening price on one day depends on the price on a previous day. This type of data is called time series data, which just means it changes over time. One way statisticians talk about this is called autocorrelation, which is simply that data points in a variable are correlated with others in that variable (it is correlated with itself).
[DISCLAIMER: I am not an expert in time series data. I have been doing a lot of research lately to try to catch up. If anyone has a better method than the one below, please let me know!]
Removing autocorrelation from the data
There are a few ways to deal with this, but we ultimately decided to remove the autocorrelation from the data and do our statistics on the result. The methods used are not developed by us. They were found from an excellent graduate student statistics class at Penn State (https://online.stat.psu.edu/stat510/lesson/1) and from this online text book by Rob J Hyndman and George Athanasopoulos (https://otexts.com/fpp2/index.html).
We tried A LOT of different methods (filtering data through ARIMA models, transforming data using various functions, etc). The resulting data from many of the methods could only be used specifically for correlations, and there are a lot of other tests we want to do. We decided on a simple transformation that does a great job (though not perfect), called differencing. The result will sometimes have small autocorrelation left in the data, but it is simple, clean, and hopefully I will convince you that it works for our purposes in the next section.
Differencing is typically used to stabilize the mean of a time series, so you can focus on fluctuations (what we are interested in). It is a transform where you subtract the previous observation from the current observation for all data. So difference(t) = observation(t) – observation(t-1). There is a lot more to discuss about this, and I would be happy to answer questions, but here I want to move on to the evidence that this method works (this post is long enough as is).
Proof of concept
Before showing the proof of concept, I want to go over one additional issue with stock data, that the relationship might be delayed in time. For example, if I thought Put OI was related to changes in GME price, that relationship might be delayed a day or two (e.g. high put OI on day 1 results in a change in GME price on day 3). In statistics we call this lag. To check for lag, I use cross-correlation, which is basically running correlations on the two variables while adjusting the lag. The graphs I’m about to talk about all use cross correlation.
Now to proof of concept. If you’ve stuck with me so far, congrats! I know this is a lot.
Let’s look at some graphs. On all graphs below, the left panel shows price data, and the right shows the cross-correlation results. The results are shown as a bar graph with lag on the x-axis (days in this case) and Pearson’s correlation coefficient (r) on the y-axis. If a bar crosses the horizontal blue line, it is significant at the p = 0.05 level.
To help with interpreting the data, I messed around with the mini squeeze from GME in January (happy anniversary!). The following is correlating that data with itself with different lags/transformations, which means that the r = 1 in every result.
https://preview.redd.it/0l40iqniwpe81.jpg?width=1280&format=pjpg&auto=webp&s=036f994ac014c05a61b39301f2674881fc115fdc
Let’s take this one at a time. In the first set, No lag (pos), the two time series are perfectly matched, so we see a large positive r at lag = 0. Likewise for the No lag (neg), but now the time series are exactly opposite from one another, so we see a large negative r at lag = 0. The bottom three sets adjust the lag between the two time series. As we adjust it, we see the largest r at different lags (seen as the large bar at lag = 10 or -10). For all of these, lag indicates the number of days. That is a high r at lag = 1 means the two stocks are related to each other offset by one day.
Coooool cool cool so we know the method (difference the data, then run a cross correlation) captures lag with idealized data, now let’s look at how GME relates to the popcorn stock and SPY like we did with standard correlation to see if all this work is actually worth it. As a reminder, a standard Pearson’s correlation found an extremely strong relationship between GME and popcorn stock (as expected) as well as between GME and SPY (not expected).
https://preview.redd.it/7tvpbtaexpe81.jpg?width=1280&format=pjpg&auto=webp&s=311b17b2a49f709c26e524de08cf393c4d529743
This is what I was hoping for! GME and popcorn stock show a strong correlation at lag 0, yet GME and SPY show very little correlation across the time period. I tested a bunch of other stocks (and simulated data) that shouldn’t be related, so now I feel confident that the method works (if you want more information, please let me know). Here are some additional comparisons I made in my original post just as examples (have not been updated):
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Question 1 from homedepothank69
Rolling correlations (not included in my original post)
So far the technique works pretty well to answer the question, are X and Y related? Another question you might want to ask is, at one point in time did X and Y become related? The hedgies use a crazy amount of BS to manipulate stocks, and they seem to change their strategy fairly regularly, so it’s good to know at what time they use one technique or another. This is where rolling correlations come in. Basically, you run a correlation for data between day 1-20, then you run it for data between day 2-21, and so on. In this way, we can find roughly when the two became related.
Tool so you can do it without any coding!
A fellow quant ape, u/orangecatmasterrace, is a wizard at shiny apps and converted our code into an easy to use browser app. Here is that link, as well as his original post on correlations:
https://orangecatmasterrace.shinyapps.io/stonk\_app/
https://www.reddit.com/Superstonk/comments/o1a73z/hi\_apes\_we\_need\_to\_about\_a\_little\_thing\_called/
All data is scraped from yahoo finance when you enter the request, so the data is always up to date. As discussed above, the data is differenced first before running the analysis. They used the code for a rolling correlation, so you can enter any ticker over any time period and find out when they became correlated (if ever). Here is just one example, GME vs. popcorn stock:
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https://preview.redd.it/gyvpyviiype81.jpg?width=1889&format=pjpg&auto=webp&s=02d03694c4686d678dbce014020ebde313e8d303
On the left you can enter any tickers you want to compare, as well as the date, along with other preferences. On the right are the results. The top shows the price of the two tickers, and the bottom shows the r value across different dates. So GME and popcorn were not related at all until the mini squeeze in January. After that, they have remained correlated for the whole year.
I won’t discuss any speculation on what all of this means, rather the purpose of this post is to make sure you have the tools necessary to answer the questions you want. I hope all you lovely apes will share any interesting relationships you find!

Hey everyone,
First-time effort-poster here. I started with $MT and $VALE LEAPs right after the original DD, bought every subsequent dip, got torched with the big-tech sell-off in late February, doubled down on Vitarded stonks, made everything back and more, and am now sitting on 206 options set to expire June through October because that is my area code and I am superstitious.
As an English major by trade, I like to think I'm fairly good at assessing the reliability of market commentary, and the great news is that more or less every reputable source agrees with our thesis over the long term. However, there appear to be significant headwinds emerging very shortly (maybe even this power hour, if not last week's dip!), which could hugely impact the value of options in that June through September range—in other words, the majority of this sub's positions.
My favorite source of information outside this forum is Predictive Analytics Models, which, though it uses equity futures as its preferred instrument, shares a similar philosophy to the Vitarded:
-Led by insider with hella experience
-Trades on macro information
-Long time horizon for plays
-Good at making money
So when the founder—a Swiss ex-hedge-fund guy with an excellent track record—correctly calls a market and commodities peak at the beginning of last week, then writes two articles saying the sky may be falling on us soon, I think it's worth sharing them with y'all.
I'm pretty sure I can't link the articles (either the bot will torpedo any SA links or they'll be behind a paywall), so I'll just summarize each, C&P below and intersperse screenshots of the models they use (which I have trouble interpreting).
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If these articles are correct, then we have a few possible courses of action:
-Roll medium-term options out to January 2022 or later on a green day
-Sell them on a green day and re-enter later
-Convert many of them to commons (or sell and buy commons later)
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Some possible counterarguments to the models (and therefore selling / rolling) would be:
-Commodity equities have recently not had much of a correlation with either commodity prices or the rest of the S&P
-Earnings are guaranteed to be sky-high this upcoming quarter, if not also the next
-Trying to time things is dum
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Especially interested in u/vitocorlene and u/graybushactual916's thoughts on the matter.
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-CML
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ARTICLE ONE
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SUMMARY
-Lack of liquidity due to destruction of bank reserves
-Fed expected to tighten policy even more
-Value stocks may be disproportionately harmed
ARTICLE
Systemic Liquidity Seasonality Suggests That Equity Markets Are Due To Tip-Over Within The Next Two Weeks; Yields Likely To Fall As Well, So High-Tech May Outperform Other Sectors
May 16, 2021 5:08 PM ET
This article updates what we wrote about the equity markets four weeks ago at the PAM portal: (“The equity markets head for a window of peak performance in the short-term, as good economic data may tip the Fed Reserve into changing its rhetoric and policy”.) This is we had to say:
But for many participants, including us, this doesn’t feel like a “runaway” bull market. Moreover, some areas of the market - most notably the formerly strong tech cloud stocks - are conspicuously lagging. This bull market is very peculiar. We also make the case that there are fundamental, liquidity issues that are starting to crop up, and the Fed Reserve’s reaction function may be starting to change.
It does look like risk appetite has dwindled, and continues to dwindle in the near term. The connotation is that the big institutional buyers are leaving the tech (high-risk) market, and starting to turn defensive. As long as this is the case, the current market tends to be a low-energy affair, and will tend to be very susceptible to external shocks, like changes in monetary and fiscal policies, and to liquidity flow issues, which we discuss in some detail below.
Original chart in the April 2021 article
https://preview.redd.it/3t4mixyukyz61.png?width=1312&format=png&auto=webp&s=66d1fbf32c0e0e467f45b5808f5afa5f79e8c9d9
This is how the above chart looks now
https://preview.redd.it/lnl1piyvkyz61.png?width=1312&format=png&auto=webp&s=85aa1b31aaec19c6b422aceddf6d347bc285d4c3
We continue the focus on the higher risk, high-tech, biotech and small cap sectors to gauge the appetite of the market for risk, and juxtapose that to the current systemic liquidity flows originating from the US Treasury and the Federal Reserve. We find that these markets are fast approaching a window of peaking performance over the next two to three weeks.
The high-tech sector did underperform, as bond yields continue to ratchet higher, leading to an outperformance of value and small caps. However, bond yields have rebounded in the past several weeks, putting a check on the slide in momentum stocks.
The most significant driver for stock and bond market performance is yet to come into effect, but we expect that to happen before the May month is over. We discussed it briefly in last month’s article, but this month this issue will be front and center.
We wrote about the systemic liquidity tightening in the face of announced, sharp drawdown in the Treasury General Account balance from what was $1.6 trillion early when we last wrote about it, to less than $500 Billion by end of June 2021. We also said that most of these funds will flow to the newly enhanced Fed O/N RRP facility, the likely destination for these funds which need to find a home. And that is what happened.
The TGA drawdown was further exacerbated by the termination of the Supplementary Leverage Ratio (SLR) waivers ended at the end of March, the G-SIB large banks dis eschewed the trouble those deposits bring to their capital ratio calculations. Therefore, term money had to move out, and there aren’t many places to go. The RRP is the safest and less onerous place for these funds to go to, as zero pct rate at this Fed facility was a lot better than the negative term (money) market rates that is prevailing up to this time.
The problem is that TGA flows to RRP facility counteracts liquidity inflows, tightens systemic liquidity; reduces bank reserves, and pushes up volatility (VIX) which undercuts equities, and push long-term yields lower (see chart below).
The VIX is very sensitive to systemic liquidity, especially the Fed's Balance Sheet (e.g., Bank Reserves).
The take-ups at Fed's O/N Reverse Repo facility is building into a tsunami, which expunges Bank Reserve wholesale, after a lag.
There is empirical evidence that the effect of liquidity is transmitted to the SPX via the VIX, but the impact comes only after a long lag.
This is the most insidious part – most investors can’t comprehend the long lags, so are oblivious to the approaching danger.
https://preview.redd.it/dmkcbswxkyz61.png?width=1294&format=png&auto=webp&s=94dc020ac9ae57e97b4d5cb878ae8ed4cfb42a54
The process goes like this: when an investor enters an RRP transaction with the Fed, the Fed sells a security to the investor with an agreement to repurchase that same security at a specified price at a specific time in the future (0 percent in this case). Securities sold under the RRP facility continue to be shown as assets held by the Fed, but the RRP transaction shifts some of the liabilities on the Federal Reserve’s balance sheet, specifically from deposits held by depository institutions (also known as bank reserves) to reverse repos (also on the liabilities side) while the trade is outstanding.
In other words, RRP transactions reduce the stock of bank reserves. It is the change rate of bank reserves which powers the rise and fall of financial asset prices -- which is why the drain towards the O/N RRP facility will hurt financial asset prices at some point. That point is fast approaching (see rectangle in chart above). That inflection point can come as early as the 3rd week of May, or during the last week of May.
There’s other supporting empirical evidence which we will show presentation style below.
These are modeled systemic liquidity factors affecting rise/fall of SPX, 10Yr Yield. Watch Factors Absorbing Bank Reserves (inv, directly tightens liquidity) and Factors Which Supply Reserves. Destruction of bank reserves model is crucial to watch. This model provides an inflection point during the period May 19 – 24 after which equities and bond yields should fall (see chart below).
https://preview.redd.it/kj4lh0yykyz61.png?width=1324&format=png&auto=webp&s=9396c480404e4de2a94f2e05042b9cb66759f80a
Here is a set of liquidity models which we have been showing regularly (see chart below) – the Fed's Balance Sheet and Bank Reserves chart: last Thursday and Friday, there was a massive SPX recovery, but that may just be a head fake. Liquidity seasonality tips over again during the period of May 12 - 19 (post a May 11 peak), so it looks like the sell-off is not done yet.
The VIX is telling us, exactly, that the ongoing blow-off in equities should transition into a tip-over later in the May 17 week. VIX changes hew close to changes in Factors Which Absorb Bank Reserves (brown line, chart below), which was validated by a top inflection on May 11. But other Fed Balance Sheet internals indicate also indicate another May 14 - May 19 peak. We go with the latter date.
https://preview.redd.it/qgzqvfpzkyz61.png?width=1318&format=png&auto=webp&s=eccfcb18fcc5e3df78c427e45fcbdcbe369202d5
Here’s a much bigger liquidity flows picture: there`s a negative covariance between Implied Volatility vs CB-5 central bank-provided aggregate systemic liquidity (see chart below). We have shown versions of this model several times before.
Rising systemic liquidity pushes down Implied Volatility, which strengthens stock values, and vice versa (lag of 8 months).
If the impact of liquidity flow changes continue to apply to changes in equities, then a bottom in equities come in July.
https://preview.redd.it/elbe5xe0lyz61.png?width=1310&format=png&auto=webp&s=998498a54dc30cbbc9262b995fa349a247774d9f
The inflection higher in Building Permits, Housing Starts, Residential Investment., post COVID-19, may still continue to rise . . . . . . but Homebuilders, Home Depot and Lumber prices may have peaked, or are peaking (see chart below). This is an old chart which we have shown several times before to update the outlook on the US housing market.
We are also showing that the equity markets (S&P 500) are sensitive to downward fluctuations of the US housing market. By this measure, equities are topping out. Housing data may again start to perk up on early August – that is when the US housing and materials associated with the industry will start becoming interesting again.
https://preview.redd.it/ghj5uw23lyz61.png?width=1320&format=png&auto=webp&s=55005d4f9f2e80cd982bf9a927cdeee92f3495f4
Summary:
Liquidity seasonality wanes again as we head towards late May and that could last until July. Weaker stock markets and lower yields have historically been associated with declining liquidity flows, and we do not expect it to be different this time around.
But probably the most important aspect is Fed that is more likely than expected to begin walking back its ultra-loose rhetoric sometime soon, as we continue to see upside surprises in Non-Farm Payroll (the April 2021 low data is probably an outlier), in stock market earnings, and in the dramatic acceleration in vaccination and a reopening of the domestic economy.
The last chart below provides an example of how changes in the US Treasury and Fed Reserve policy mix impacts equities and bonds. For the markets, total aggregate liquidity is the net difference of the US Treasury’s debt issuance, and the amount of securities purchased by the Federal Reserve. It is therefore crucial to know if the Fed is tightening while the Treasury continues the pace of its debt issuance. And vice versa.
https://preview.redd.it/w3a4sr54lyz61.png?width=1322&format=png&auto=webp&s=4d0df606ca04abbac437836b87a639d92ea7525b
Several FOMC members have already been floating the idea that a tapering conversation should begin when 75% of the population vaccinated, and all signs point to this occurring sometime before mid-June. The June 16th FOMC meeting is therefore shaping up as providing a potential surprise change in Fed rhetoric and monetary policy.
This has the potential of lowering the total value of securities purchased by the Fed – and that lowers the delta between Fed Purchases of Securities (SOMA) and Treasury’s Debt Issuance. That is of prime significance because that delta determines the direction of Bank Reserve growth. If the Fed tightens, and the Treasury issues more debt, then Bank Reserve growth will slow, and that weakens equities, and lowers bond yields.
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ARTICLE TWO
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SUMMARY
-The commodity supercycle is more or less a sure bet and we're presently in its early stages
-However, it is likely to last long enough that exposure through short- and medium-term options will be challenging
-In the short to medium term, said options might get kneed in the balls due to the same cause as above—diminishing liquidity flows—causes them to peak now (or even last week?)
-But it's possible to forecast with confidence that they'll pick up again September through January.
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ARTICLE
Time To Take Profits In Long Commodity Bets; Step Aside Until September As Risk Assets Prices May Moderate On Diminishing Liquidity Flows
May 18, 2021 5:23 AM ET
The last time we wrote about commodities (base metals) was in the March, 2021: (“The commodities “super-cycle” ignites base metals take-off as super-abundant global liquidity is put to work on major infrastructure projects”). We wrote about the possibility of a so-called “super-cycle” in commodities, and showed model work which seem to support the theme. We said:
Looking back all the way to 1972, commodities have never been cheaper relative to the broad stock market. The average of this ratio is 4.1 over 50 years. Today, it sits near 0.5. But it seems poised to soar. What we at PAM did was to juxtapose a vector autocorrelation analysis which shows a possible 15- to 18-year cycle in the ratio between commodities and equities. We show the result below, which illustrates a commodity super-cycle, possibly in the making.
Original chart on the March 2021 article
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https://preview.redd.it/fx197seslyz61.png?width=1296&format=png&auto=webp&s=7f322248b1bab6adaa9fe59e8e03351eff23bfec
A commodities supercycle is considered to be a multi-year trend, where a wide range of basic resources enjoy rising prices thanks to a structural shift in in demand versus supply. Typically, what happens, is that supply stagnates or drops for several years as economic demand is itself weak or constant. However, at one point a new business cycle starts, and demand picks up, while supply is unable to immediately react. We believe the global economy is in that situation today.
This is how the chart above looks today.
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https://preview.redd.it/8oxkkr6tlyz61.png?width=1318&format=png&auto=webp&s=070d6909e5e4f3553b3c9eb9368fe0f111511699
Simply put, commodities, particularly industrial and base metals, are responding to the perfect storm of drivers. These drivers are primarily supply disruptions followed by a recent rebound in demand for commodities like copper could be defining a larger trend. In addition, central bank stimulus across the globe could be combining with demand factors to spark a commodities super-cycle in the not-distant-future.
Commodities are undoubtedly on the move. Copper is at an eight-year high and lumber has tripled in a year’s time. Is this the start of a commodities super-cycle or are these price moves “transitory” like the Federal Reserve keeps telling us? We believe that the Fed is likely mistaken in the longer-run. Higher inflation will be a component driver of higher commodity prices, maybe not this year, but likely next year.
There is no doubt that what primarily drives commodity prices, in the final analysis, is systemic liquidity. And liquidity is oozing today like there is no tomorrow. Most major central banks have lowered rates and global fiscal policy greatly accelerated spending in order to ease the pain of lockdowns. The U.S. national debt alone has increased by a staggering 16% since the onset of the pandemic. Aside from enabling demand and facilitating purchase of commodities, extreme levels of monetary liquidity also ignite fears for the stability of fiat currencies – and that is particularly true for the US Dollar, the unit of global trade exchange.
There is some evidence that change in the US Dollar is impacted by the change rate of the US Treasury’s debt issuance (but what matters is the change rate of debt, not the change in nominal amounts or levels of debt). See chart below. This relationship is particularly essential in timing the ebb and flow of the exchange rate of the US Dollar, as the change rate of US debt issuance leads by one quarter. That as a given, we can also use this to have a macro view of the future potential changes in the price of commodities, in general.
https://preview.redd.it/g4n937utlyz61.png?width=1302&format=png&auto=webp&s=02f8334d57d2dde23fe61e5b6ad2cf2b1cc18cbd
This is the ultimate liquidity flow for the US financial system. And of course, we know that there is a very strong inverse correlation between the exchange value of the US Dollar and the price of commodities (see chart below). That’s the linkage between fiscal monetary policy and the price of commodities – it runs through the global medium of exchange – the US Dollar.
https://preview.redd.it/dzmhfqkulyz61.png?width=1296&format=png&auto=webp&s=ca576dbf70d6c554f6cb4f42107b5ea818442349
Right off the bat, we say that with the intention of the US Treasury to moderate the issuance of debt over the next two quarters . for reasons that we explained in detail in the bond article in April, ("Bonds’ And Equities’ Big Picture: Bond Yields Should Peak Soon, Now That The SLR Issue Has Been Resolved; Equities May Switch Back To Positive Covariance With Yields"), we are facing some moderation in the previously frenetic rise in prices of commodities, especially in base metals, over the next two quarters.
We bolster this moderating view by reposting the development in China’s Total Social Financing – the lead indicator of what to expect in anything that has to do with commodity prices (see two charts below).
https://preview.redd.it/iqau65fwlyz61.png?width=1306&format=png&auto=webp&s=8ac65ad11a8a336a573d4cfb75a67bf02060659f
China’s growth rate has slowed down from the frenetic rate generated about a decade ago, but it is still the single, largest consumer of commodities in the world today. The Chinese growth rate may have declined, but the volume of raw materials and resources need to sustain current growth (in a comparatively larger economy) is still comparable to those obtaining a decade ago.
That growth rate is tightly regimented by the ruling Chinese Communist Party (CCP) and is discussed and ratified in December of every year – and it is laid out 5 years in advance. This schedule of budget expenditures is implemented in real-time by via Total Social Financing (TSF). That is what makes TSF a very potent lead indicator of what China’s PMI manufacturing will likely do in the future (see chart above). That in turn is a lead indicator of what commodities (base metals in particular) will likely do in the near-future (see chart below).
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There is a larger macro picture if we are to use China’s fiscal policy as yardstick to determine the future outlook for commodity prices. The People Bank of China (PBoC) also publishes its Net Lending/Borrowing intentions (of 5 years ahead). China’s budgetary expenditures are the low-frequency expression of these lending and borrowing intentions. To extend the analogy further, the TSF is the high-frequency expression of these intentions (see chart below).
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Note that the correlations work with a long lag between the primary driver data and its impact on the commodity price underlying (between 9 months to 1 year). That is not a disadvantage, as these correlations have become almost deterministic. It is possible therefore to have a commodity outlook almost one year in advance. We have been using this TSF-based forecasting method, and have had phenomenal success so far.
Summary:
If we put all of these drivers together (the fiscal liquidity flows, US Dollar outlook, TSF future development), we see that commodity prices should peak this month of May, moderate significantly until September, and then rally again to a top sometime in Q1 2022, as global growth takes a pause (see chart below). The outlook on base metal miners should be similar.
https://preview.redd.it/8u2js0kylyz61.png?width=1266&format=png&auto=webp&s=12118d08611289bd82718a1c336f2c398a15210a
Next year should see global growth moderate from whatever high point it may achieve in 2021, the year of recovery. That is not merely an economic forecast – it is a mathematical truism, and a lagged function of the large economies' fiscal expenditures (see chart above). We should expect commodity prices (especially the China sensitive resources, like base metals) to moderate as well. So commodity investors should cash in on commodity-based profits NOW, bide your time, and then get back into the market by September. We will be there with you, guiding you all the way.
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