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German Hyperinflation 1923 - Financial Independence and Black Swans

The German hyperinflation occurred exactly 100 years ago. No other event has probably had such a lasting impact on German society’s relationship with money as this event. In this article I would like to take a closer look at the impact of such “black swans” on our calculations for financial independence. The article is a bit longer and assumes knowledge of the basic functions of the simulator. In case of doubt, therefore please have a look at the documentation.

Data just from the U.S. stock market is not representative enough

Most studies on financial independence (and all publicly available FI calculators that I know of) use data from the U.S. stock and bond markets to calculate withdrawal rates (or, for lack of other available data, cover only a relatively short period back to 1970 at most). In my view, however, this leads to a classic easy-data bias: the U.S. data are available free of charge and cover a period of more than 150 years. This long period suggests that the extreme events it covers, such as the two world wars, the Great Depression and the stagflation period of the 1970s, can provide a sufficient basis for risk assessments. I deliberately quote my own basics article on the FI simulator: “The basic assumption of this simulator is that a very large spectrum of possible extreme situations is already covered and we assume that the actual future development of our portfolio will lie somewhere between the extremes with a fairly high probability.” However, as we have already seen in the blog article introducing historical German stock and inflation data, the safe withdrawal rates for a German investor invested exclusively in German stocks and exposed to German inflation rates are well below the comparable results for a U.S. investor. The results are interesting because Germany can serve as a prime example of how Black Swans can affect safe withdrawal rates due to the two lost world wars and the hyperinflation of 1923 (I refer to black swans here in reference to the well-known book by Nassim Taleb as unexpected extreme events that lie far outside the experience known up to that point). A systematic comparison of the results of a U.S. investor with a German investor should therefore allow a much better assessment of risks than looking at the U.S. market alone.

Last September, Scott Cederburg et.al. published a paper that goes in a similar direction and calculated safe withdrawal rates for a variety of other countries. The group uses commercial data from globalfinancialdata.com for this and also comes to much worse results than with the US-only data: Instead of the popular 4% rule, this paper concludes that rather a 2% rule should be used. This way the historically known risks of local equity markets would be taken into account as well instead of just hoping that those perform as well as the US equity market. As expected, this paper has triggered lively discussions (e.g. here) because it would of course require much larger portfolio sizes before financial freedom is achieved.

How to paint black swans gray

Since we unfortunately cannot rule out the possibility that we will see more black swans in the future, in this article I would like to attempt to understand the effects of at least some of the extreme events a little better. Ideally I would like to even mitigate their impact a little bit by optimizing the portfolio appropriately. In this case, the swans would then no longer be quite so black but hopefully only “gray”.

One major criticism of the Cederburg paper for example was that the group primarily studied investors who were only invested in their respective domestic markets. Even my own blog article introducing the German stock data ultimately describes a German investor who was fully invested only in the German stock market. Could this investor have had better results if he had also held U.S. stocks? And today we can even invest cheaply in fully global equity ETFs and such a global diversification should also help to mitigate those risks, right?

Furthermore, since version 0.3 of the simulator, we have the possibility to systematically examine different asset allocations and, for example, add bonds in order to reduce the volatility of our portfolio. Could this mitigate some of the impact of extreme events like the hyperinflation or the effects of the lost world wars?

Without access to the commercial market data from the Cederburg Paper, we have to limit ourselves to the U.S. and German stock and bond market data that is available in the simulator. However, as mentioned above, the histories of both countries cover a much broader range of extreme events than the U.S. market alone. So let’s take a closer look at various example scenarios using this available data:

1st case: German investor invested 100% in the German stock and bond market.

For a 100% stock portfolio, this case corresponds to the results I already presented in my blog post on integration of German stock and inflation data. To replicate these results we set the home currency to “DE Euro” and the home inflation to “DE” in the “Asset Allocation and Stock/Bond/Inflation Data Settings” tab. This ensures that we use the inflation data that applies to the cost of living in Germany and also that all currencies are correctly converted to EUR in each case. Since we are also looking at periods well before the introduction of the Euro, it is important to consider its German predecessor currencies as well. These were the Papiermark until the hyperinflation of 1923, the Reichsmark until 1948 and the D-Mark thereafter. These predecessor currencies are automatically included when “DE Euro” is selected. We also change the asset allocation, which is always set to 100% US stocks in the default, and set it to 100% DE stocks. We leave the other settings unchanged and thus obtain the following picture:

If we now look at the calculated withdrawal rates we see the disappointing result already known from the old blog post:

Instead of the “familiar” safe withdrawal rate of about 1200€ for our 480,000€ portfolio, which corresponds to 3% and uses only the US data, we get with 100% German stocks a safe withdrawal rate of only 519€ which corresponds to just 1.3% of the portfolio value. If we accept a bankruptcy probability of 2.5%, this withdrawal rate only increases to 599€ or 1.5%. This halving of the withdrawal rate also fits roughly to the results of the Cederburg Paper.

Let us now take a closer look at the middle chart: There, the withdrawal rate is listed depending on the virtual start date of the stock history and we can see that the minimum of the withdrawal rate results from the start date Feb. 2014, i.e. shortly before the outbreak of WW1. So interestingly, here the hyperinflation of 1923 does not seem to be the dominant effect in a 100% German equity portfolio. This is primarily due to the fact that stocks as tangible assets are much more independent from inflation than bonds or even bank deposits. However, looking at start dates between 1921 and 1923, we can also see the extreme fluctuations of the resulting withdrawal rates. Thus, stocks are not unaffected by such an extreme event.

Up to this point, we have merely reproduced the results of the older blog article once again. But now let’s take a look at how different asset allocations affect withdrawal rates. We first examine a possible admixture of German bonds. To do this, we go to the “Optimization of Asset Allocation” tab and calculate possible portfolios between the following two “extreme portfolios”:

The resulting graph is strongly dominated by statistical outliers at the top. To get rid of those, we simply enlarge the area of the actual boxplots with the mouse and obtain the following image:

My gut feeling before was that adding bonds would not have a positive effect due to their high real price losses in the context of hyperinflation. We see that a small addition of 10% bonds leads to a tiny increase of the safe withdrawal rate from 519€ to 535€, but this is, of course, no significant improvement. More striking is that the withdrawal rate of the 1st quantile (i.e. the value that is only undercut by 25% of the data points) increases from 1259€ to 1647€ with 20% bonds (the area between the 1st and 3rd quantile contains the middle 50% of the data points and corresponds to the “thick” part of the boxplot). Thus, for the middle 50% of the histories, adding bonds can lead to a significant increase in the withdrawal rate. The 25% worst histories are likely to be dominated by effects from the hyperinflation, and in this article we deliberately wanted to examine the effects of such extreme events and will therefore not ignore the lower part of the boxplot. If you look at the data point on the far left, which corresponds to a pure bond portfolio, you can see how violent the impact of hyperinflation has been: With a pure German bond portfolio of 480,000€ only a safe withdrawal rate of 87€ would have been possible.

As an extreme example, it is also worth looking at a German investor who holds part of the portfolio as cash. So we examine portfolios between the following two extremes. Portfolio A has 0% assets, i.e. the remaining 100% are cash or bank deposits that do not yield any return:

The resulting outcome shows how a cash-heavy portfolio would have been wiped out during the hyperinflation:

The safe withdrawal rate of a cash-only portfolio (the data point on the far left) would be pretty much exactly 0.00€ due to hyperinflation! So in essence it seems that our usual mechanisms for optimizing the asset allocation with bonds and cash are actually pointless in the case of an extreme event like a hyperinflation and do more harm than good. Of course, this is also what you would expect, as bonds and, above all, cash are most strongly affected by hyperinflation. Equities, as tangible assets, apparently still get you through such a phase comparatively better. I strongly suspect that real estate would also have helped here, but for lack of useful data, I must stick to the pure hypothesis here.

As a final exercise in this case, we now set the field “Use Data from” to 1924 in the “Asset Allocation and Stock/Bond/Inflation Data Settings” tab and thus filter out WW1 and hyperinflation from the historical data completely. All historical price histories thus start in 1924 at the earliest, i.e. after the introduction of the Rentenmark, which was later called the Reichsmark. Our withdrawal rates for different asset allocations between 100% German bonds and 100% German equities now look much more benign and resemble in their behavior what one sees when using purely US data:

With an admixture of 15% bonds, we now get a safe withdrawal rate of 1115€, which is well above the 519€, but still well below a comparable withdrawal rate of about $1473, which would be possible with purely US data (and US inflation rates). Nevertheless, the underlying historical price trends still include the lost WW2, which led to a massive devaluation of stock and bond prices (by about 90%). Against this background, this safe withdrawal rate of 1115€ for the worst case of all historical trajectories is still surprisingly high.

2nd case: German investor invested in the American stock market.

The case that now follows is somewhat fictitious, since in the past, of course, the free buying and selling of foreign currencies as well as foreign stocks was not always possible. But if we assume for the future that this is possible at any time, we can examine how an investor in Germany with a broad American securities portfolio would have fared. To do this, we change our own asset allocation back to the default case of 100% US equities, but leave home currency and inflation at “DE Euro” and “DE”, respectively:

The result was surprising to me at first:

Although U.S. stocks quoted in U.S. dollars should be totally independent from the hyperinflation of the German paper mark, we see a safe withdrawal rate of only 623€ i.e. not much more than a pure German stock portfolio would have delivered. If we look at what part of the data is responsible for this disappointing result, we see exactly the area of the hyperinflation 1923 as the starting date of the critical price histories, which I show enlarged again here:

My first reflex when I saw this counter-intuitive result was to doubt the underlying data, especially with regard to the Paper Mark - USD exchange rates from the time. To this end I have looked for other exchange rate tables from various sources, which showed partly deviating values especially in the 2nd half of 1923, i.e. at the height of the hyperinflation. To my astonishment, however, the withdrawal rates achieved with those tables were sometimes even lower. The exchange rates ultimately used here in the simulator between 1914 and the end of 1923 are from gesis.org and are based on monthly averages of the Berlin Stock Exchange. Since G. Gielen’s inflation data from the period are also available as monthly averages, I actually expect the least systematic error in this combination.

We can better understand what happens here when we look at the following chart, which shows inflation-adjusted (based on German inflation data!) returns of U.S. stocks and of the U.S. dollar itself in EUR (or the correspondingly converted German predecessor currencies) based on the data used in the simulator:

First of all, you can see that the shape of the exchange rate of the U.S. dollar (in red), of course, has a massive influence on the price trend of U.S. stocks in Euro (in blue). Especially the following periods are interesting:

  1. After the end of WW1 until the end of the hyperinflation at the end of 1923, there was a temporary and strongly fluctuating depreciation of the paper mark against the dollar recognizable by the relatively high peaks at the time.
  2. In 1945 and 1948, one sees step-wise devaluations of the Reichsmark, which are pure data artifacts: Between the end of WW2 and the introduction of the D-Mark in 1948, there were no official exchange rates between the Reichsmark and the U.S. dollar at all. I help myself here (like also all other data suppliers) with constant and unfortunately artificial intermediate values to be able to carry out calculations that cover this time span at all.

However, these exchange rate effects now explain some of the previous results:

  1. Portfolios in U.S. dollars, which were started exactly at the peak of these temporary paper mark devaluations in 1921-1923, suffered a massive exchange rate loss after the end of hyperinflation due to the relative appreciation of the German currency that followed. The resulting surprisingly low withdrawal rates are thus the result of the sequence-of-return risk that occurred.
  2. The artificial devaluations in 1945 and 1948 due to missing exchange rate data, on the other hand, lead mathematically to very high exchange rate gains of such an U.S. portfolio, which then also leads to the “spike” of the withdrawal rates up to a calculated 30,000€ in this period. Unfortunately, this is primarily a data artifact, but also shows how difficult a halfway decent analysis of such data is. The US data, by comparison, are very benign, keyword again: “easy-data-bias”.

So back to the original motivation for this article: The German investor who would have somehow anticipated the effects of hyperinflation and wanted to build a U.S. portfolio as a countermeasure, would have achieved only slightly better results than the analogous investor in German equities. If one wants to draw any lessons from this situation, then the only thing that comes to mind is that in such phases, exchange rates apparently also tend to become irrationally exaggerated and one thus runs the risk of having to buy at very unfavorable rates. This suggests that a globally diversified portfolio should be built up early enough, i.e. while exchange rates are still behaving rationally. If one had built up the US portfolio well before 1921, one would have been able to realize withdrawal rates of about 2000€ with this portfolio, and (theoretically) would have been able to cope with hyperinflation.

As a next step our example investor tries to further minimize risk by adding German bonds to his portfolio of U.S. equities. Perhaps this admixture will help to reduce volatility and thus increase the possible withdrawal rate again. We therefore set the two extreme points of the asset allocation as follows:

The result (after slightly zooming in with the mouse to again hide the statistical outliers at the top) actually looks much better: Adding 30% German bonds we reach a safe withdrawal rate of 1316€. This is still slightly below the optimal result with pure US data ($1473 with 25% US bonds) but is already in the same order of magnitude. Unlike the addition to German equities, the addition to US equities apparently leads to a significant reduction in volatility and thus to a strong increase in the safe withdrawal rate. This is surprising since, as mentioned above, German bonds suffered a real loss in value of about 90% during the hyperinflation. Let us now apply this apparently optimal asset allocation to our own portfolio:

We see that the minimum withdrawal rates with this portfolio actually no longer come from the hyperinflation but from the stagflation phase of the 1970s:

For the sake of completeness, we also calculate adding US bonds to US stocks for a German investor. Actually, the rule of thumb is that you should always buy bonds in your home currency to avoid the additional currency risk. Captain Hindsight, of course, sees it differently, because he knows that soon a hyperinflation in paper mark will come and thus buys himself a large chunk of US bonds:

The result is unfortunately disappointing: Adding US bonds has no positive effect at all and the best result is achieved with 100% equities. The minimum comes in all cases again from the history, which starts in the hyperinflation. The cause here are again the exchange rate effects already discussed above. These have the same effect on both asset classes and thus prevent the mutual compensation of price extremes, which otherwise help to reduce volatility.

3rd case: German investor with a mixed DE/US portfolio

For those who still haven’t completely lost the plot, there is one last scenario: an admittedly very crude approximation of a “world portfolio” could be a portfolio with 60% US stocks and 40% DE stocks. The 60% US share roughly corresponds to their actual share of the world’s market capitalization today, and for the rest we just take the only independent data set we have which are the german stock data. Let’s look directly at how an optimization of the asset allocation would look like with an additional admixture of German bonds to such a “global” portfolio:

With an admixture of 15% German bonds, we actually get a safe withdrawal rate of 1430€ from this portfolio, which is only just below the result for pure US data. Mind you, we have kept the complete German data set including World War 1 and the Hyperinflation of 1923 in there. I.e. a fictitious German investor with exactly this portfolio would have actually painted the black swans that took place during these times gray and would have come through relatively unscathed.

Preliminary conclusion

As we have seen, evaluating withdrawal rates with long-term German stock and bond data is quite a subtle undertaking due to the Hyperinflation of 1923. Therefore, if you want to make things easy for yourself, you should either stick with the default selection of US stocks and US currency and inflation data in the simulator or, when using the German data, always set the time period to at least 1924 by setting the “Use data from” field. This then implicitly assumes that we will not be threatened with something like a Hyperinflation in the future and the data are much easier to interpret.

However, if you want to be prepared for a Black Swan like 1923, this article may give you some initial ideas on how to steer your portfolio through such phases. Apparently, an admixture of bonds as well as a global diversification seems to help even in such extreme events. Of course, the exact portfolio proportions depend extremely on the available data and should therefore not be over-interpreted. Moreover, the next Black Swan (by definition!) could lie completely outside our known horizon of experience, and then all the lessons we learn from 1923 will quickly go up in smoke anyway.

Last but not least: I have already indicated that there are some data artifacts in the exchange rates and my confidence in the German data, especially from the extreme phase of hyperinflation in 1923, is not very high. Simply because there were such extreme increases in a very short period of time, I fear that no one has produced really reliable data comparable to today’s data quality. Therefore I could understand some frowning while reading this article and possibly some of you may have had the word “data mining” in mind.

Nevertheless, I hope this look beyond American data has been interesting. I look forward to your feedback on this, and I’m also happy to admit that I still see myself in the middle of the learning curve here.

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