S&P adjusted for M1 and CPI

The seemingly unlimited printing of money by central banks has driven markets to unimagined heights. But what does market performance really look like after adjusting for M1 money supply and inflation CPI?

Money supply M1 and inflation CPI

Let’s first have a look at the absolute numbers for consumer price index (for urban consumers) and the US M1 money supply. Luckily there is no direct correlation between those two measures. On this chart an expansion of M1 did not lead to a rising inflation. But there are surely other examples in history.

CPI and M1 money supply

CPI and M1 money supply  (CPI left scale, M1 right scale)

CPI adjusted stock market

Adjusting the stock market (S&P500) for CPI shows its real growth rate from the point of an average consumer. It is not 45 times higher than 1970, but only 5 times higher. If your investment in 1970 bought you one average consumer basket, it will buy you 5 baskets right now, but not 45, as the nominal value might suggest.

SPX CPI adjusted 1970

SPX CPI adjusted 1970, % gains since 1970

House price index adjusted S&P 500

Some rich investors cynically call the basket of goods the CPI calculation is based on the “poor peoples basket”. So let’s adjust the market for the “middle class CPI” – the S&P Case-Shiller home price index. It is based on residential house prices.

SPX house price adjusted 1987

SPX house price adjusted 1987, % gains since 1987

The nominal value of the market should buy you 12 houses now, when you invested the equivalent of one house in 1987. But unfortunately the house prices went up too, so the adjusted calculation shows, that you only can afford 2 and a half houses with the money invested in 1987.

If you invested at the beginning of 2000, the money made on the market would hardly buy you a new swimming pool. 11% up for the adjusted value…

SPX house price adjusted 2000

SPX house price adjusted 2000, % gains

Money printing – Inflation Angst

The impact of inflation has been quite dramatic, it ate most of the returns which made you feel rich. Inflation Angst is mostly based on the incredible expansion of money supply. So let’s adjust the market with M1.

SPX M1 adjusted 1975

SPX M1 adjusted 1975

The next chart clearly shows how we printed the market up since the financial crisis in 2008. M1 adjustment does not leave any gains in financial markets. You just have more paper dollar as there have been more paper dollars printed. (which went to stock owners, but not to working class people)

SPX M1 adjusted 2008

SPX M1 adjusted 2008

Bitcoin adjusted S&P 500

Given the incredible money printing and the expected high inflation, a lot of people think about alternative assets.

Big Elon is one of the most prominent advocates of the idea to put your money not into inflationary dollars but Bitcoin. The Bitcoin algorithm makes this asset a deflationary one. The maximum number of coins is limited, and as people lose their wallets the available number of coins is actually decreasing.  So the more money is put into this market, the more Bitcoin goes up. You need more and more paper dollars to buy a Bitcoin.

Noting the S&P 500 in Bitcoin instead of dollars shows the effects of this:

S&P 500 bitcoin adjusted

S&P 500 bitcoin adjusted

Since the corona lows in march 2020 S&P 500 gained 70% in dollar value, but it lost 80% from the point of a bitcoin investor.

So chose wisely in which market/currency you put your money, and don’t base your expectations on the nominal gains of any market.  Always adjust for CPI or the measure of the product/asset basket your luck depends on.


thanks to FRED and Refinitiv for the data, thanks to Tradesignal for the charting software.

The coastline paradox and the fractal dimension of markets

Coastlines are fractal curves. When you zoom in, you will see similar shaped curves on every scale. The same is true for market data. On a naked chart you can hardly tell if it is a daily or hourly chart. This article will explore this feature of crinkly curves and show how much markets and coastlines have in common.

The coastline paradox

When trying to measure the length of the British coastline you will quickly notice, that the length measured depends on the length of the ruler you use. The shorter the ruler, the longer the measured length of the coastline.

When measuring a straight line, the length of the ruler has no influence. You can measure 1 meter with a 1cm ruler applied 100 times or with a 50cm ruler applied 2 times. Both methods will give you the same result. Not so when measuring a crinkly line like a coast.

British coastline length paradox

British coastline length paradox (c) wikipedia

In 1967 Benoit Mandelbrot wrote a famous article in Science magazine about this problem. This was the birth of fractal geometry. The basic assumption was, that if a curve is self similar, this self similarity can be described by the fractal dimension of a curve. Self similarity means, that if you zoom into a curve, it looks similar on all zoom levels.

Coastline paradox in financial markets

Continue reading

VIX Futures spread trading

VIX futures are usually in contango, meaning that the next month future is quoting at a higher price than the current month VIX future. But this spread in not constant, and at the end of the expiry cycle an interesting VIX future spread trading idea comes to my mind…

End of cycle VIX futures spread trading

Having a look at the chart below you hopefully see the spread trading idea by yourself: Continue reading

How to detect unwanted curve fitting during backtest

Whenever you develop an algorithmic trading strategy, unwanted curve fitting is one of the most dangerous hazards. It will lead to substantial losses in real time trading. This article will show you some ways to detect if the performance of your algorithmic trading strategy is based on curve fitting.

Curve fitting – what is it?

Every algorithmic trading strategy will have some parameters. There is no way around it. You will have to decide what length your indicators have, you will have to specify a specific amount for your stop loss or profit target. Beside the actual rules of your strategy the chosen parameters will usually significantly influence the back-test performance of your strategy. And with any parameter you add the danger of curve fitting rises significantly. Continue reading

The Probability of Normality

When selling implied volatility you want the market to stay within the  expected range. But what is the historic probability that markets behave as expected? And what other analysis could be done to enhance your chances and find the periods when it is wise to sell an at the money straddle? This article will try to give some answers to this question.

The normal distribution cone

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Daily Extremes – Significance of time

Analyzing at which time daily market extremes are established shows the significance of the first and last hours of market action. See how different markets show different behavior and see what can be learned from this analysis.

Probability of Extremes

A day of trading usually starts with a lot of fantasies for the future, then we try to survive the day and end it with a lot of hope for tomorrow. This psychological pattern can also be shown when analyzing intraday market data. A high level of fantasies usually leads to a strong market movement, and thus market extremes can often be seen near the beginning or the end of the trading session. Continue reading

Factor investing in portfolio management

Factor investing has been around in portfolio management for some years. Based on algorithmic rules it became the big thing in trading and the ETF industry. But is there still some money to be made? Is small beta still smart or just beta? This article will give you a Tradesignal framework to test the factor investing ideas by your own.

Factor investing

Buy and hold has been a profitable approach in investing. But customers ask for more. So technical analysis came around and held up the promise that market timing is possible. As the returns did not match this promise, algorithmic trading was invented. Clearly defined rules made it possible to backtest any given strategy, and if done properly, the returns equal the ones promised during the backtest. But this requires a lot of intellectual power and relies on cheap execution, so these returns are usually not available to the public. Continue reading

Dollar Cost Averaging Investment Strategy – success based on luck?

This article is about the dollar cost averaging investment strategy and the influence of luck in it.

The Dollar Cost Averaging Investment Strategy

To invest parts of your income into financial markets has been a profitable approach, especially in times when bond yields are low. One approach to do so is the dollar cost averaging investment strategy. Continue reading

Historic Bear Markets & Crashes (business as usual)

Since S&P500 has lost 20% from its top in 2018 and everybody is talking about bear markets. See what has happened in history.

We all have been spoiled by artificially low volatility over the last years.

Now people blame the gone-wild president or algorithmic trading for the market correction, but let us have a look into history to see how common market corrections have been over the last century. Continue reading

Bullish? Buy stock or sell put option?

So you are bullish on a specific stock, but you also have realised that timing is major problem? So what would be the best strategy to implement your bullish opinion but avoid the problems of any timing strategy?

Selling a put option might be the answer.

Bullish probability

For discussing this question let’s use the current Apple chart as an example. The question is, if you are bullish on apple, should you buy 100 Apple stocks right away or should you sell an at-the-money put option. To find the pros and cons of these two possibilities let’s have a look at some charts. Continue reading

Market crash or market correction?

Over the last days and weeks some traders have been worried if the currently ongoing correction in the markets will evolve into a crash, or if it is just a normal correction.

Crash or correction

The main difference between a correction and a crash is the panic level. But it is not the absolute level of .VIX, the CBOE implied volatility meter. It is the difference between realized and implied volatility that defines a crash which defines real panic. Continue reading

IV Percentile – when to sell volatility

Volatility trading: when to buy and when to sell volatility

You got to know when to hold ’em,
Know when to fold ’em,
Know when to walk away,
And know when to run.
(Kenny Rogers)

When to sell implied volatility

Volatility is a nicely reverting time series. If it is high chances are good that it will come down again. The only problem is to find out when volatility is high, and when it is low. Unfortunately there are no absolute levels, you can’t say that 50% implied volatility is high, as this specific stock might have an implied volatility of 80% most of the time. So you can only compare the current volatility level to historic levels and so define if volatility is currently high or low. Continue reading

Bet on Bollinger

Ever since John Bollinger introduced his Bollinger Bands in the early 1980s the bands have been a favourite indicator to all technical trades. This article is about the prediction capabilities of Bollinger bands.It researches the Bollinger breakout probability.

How good are the chances to be outside or inside of the bands in the future? How do these probabilities relate to the current position the market has got relative to today’s Bollinger band? What impact has overall volatility on these statistics? These questions will be answered below.

Bollinger Bands Breakout Probability

By definition of the indicator most of of the times the market will trade inside the Bollinger band. But this is only of minor interest to me. As a trader I am more interested on what will happen in a few days from now. Where will the future market be? Shall I bet on a breakout or sell a straddle?

So I did some tests on the forward prediction qualities of the Bollinger band indicator.

For all tests I used the 20 day, 2 standard deviations setting, which is the standard setting for most charting packages. Then I analysed the positioning of the market in 20 days form now to see if Bollinger bands can be of any help with these questions. Continue reading