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Is the Swiss Index Better Than Top US Based Indices? What About the Returns and Risk? 90 Years of Data Analyzed in Total | CSSMI vs SPY, ONEQ, QQQ
Summary: 1. Data analysis reveals that monthly returns of Swiss Market Index, Nasdaq and S&P 500 are statistically similar
2. Risk profile of Swiss Market Index is not the same as Nasdaq, Nasdaq is riskier
3. Risk profile of Swiss Market Index is statistically identical to S&P 500's risk
4. All things considered, Swiss market Index is a very attractive alternative to the top US indices (discussion included)
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Allocation of capital, a critical concern in portfolio management, demands in-depth analysis. This report examines whether the Swiss Market Index (INDEXSWX: SMI) is better than Nasdaq or S&P 500.
Proponents present a number of reasons why the SMI is better than the top US-based indices; however, only a rational analysis can extract inferences from data. Regardless of the proposed advantages or disadvantages of the three, this report relies solely on data to draw conclusions. 90 years of data has been analyzed in this report, 3 decades each per index, for an in-depth and up to data analysis.
So, what does the data reveal? Which is better, Swiss Market Index, S&P 500, or Nasdaq? (Performance analysis)
First, we must consider the long-term growth rate (geometric mean) of the three for the last 3 decades (1991-2021):
1. The long-term growth rate (geometric mean) of Swiss Market Index is 6.5% p.a. CHF (8.15%, factoring in USD long-term depreciation against CHF of 1.6% p.a.)
2. The long-term growth rate (geometric mean) of Nasdaq is 11% p.a. (USD)
3. The long-term growth rate (geometric mean) of S&P 500 is 10.2% p.a. (USD)
At first glance, we can see that long-term growth rates are very similar; however, this parameter is very simplistic and does not take into account the long-term monthly movements that can help us ascertain any differences between the three, with significant nuance (further clarification provided after 'key takeaways' section).
Hypothesis testing is employed in this report to gauge the returns and the risk, for equivalence. The pooled variance test and the F test is used for this analysis:
Pooled variance test is conducted as:
H0: µ1 -µ2 = 0, vs H1: µ1 -µ2 ≠ 0
The mean returns of Index 1 minus Index 2 (comparison) are analyzed for equivalence.
F test has been conducted as:
H0: σ1 = σ2, vs H1: σ1 ≠ σ2
The variance of the returns of Index 1 is compared to the variance of the returns of Index 2 (comparison) to assess equivalence.
All factors included in this report are explained in an easy to understand language!
Key Takeaways
The analysis reveals that the returns of the SMI are statistically identical to the returns of the S&P 500; the risk, as measured by variance, of the returns of the S&P 500 and SMI is also similar, statistically. The data analysis reveals that one cannot be considered better than the other, considering the performance of the last 3 decades. The geometric mean of the SMI may at first appear lower when gauged in CHF, however, when factoring in the exchange rate, CHFUSD, the long-term growth rate is only 200 basis points (2%) lower than that of S&P 500.
The monthly returns of Nasdaq and SMI are also identical, statistically. This means that the difference between the returns is not big enough to be statistically significant; the returns are similar, thus.
However, risk is a different story. The risk of Nasdaq and SMI is not the same. Data reveals that the monthly returns of Nasdaq demonstrate a higher risk, as measured by variance, compared to the returns of SMI. SMI is less risky than Nasdaq.
Putting together the data-derived insights: Data analysis reveals that the long-term returns of SMI, as measured by geometric mean, for the last 30 years, adjusted for currency fluctuations, are comparable to Nasdaq and S&P 500. The monthly variance of returns, the risk, on the other hand is slightly less pronounced compared to Nasdaq, however, comparable to S&P 500.
List of Companies in SMI:
The SMI does provide good international exposure through stable names such as Nestlé, Lonza group, Novartis, Roche holdings, etcetera.
With a possibility of political turmoil or tensions with an international adversary like China, North Korea, or Iran impacting the major North American Indices, an allocation in SMI, through iShares SMI ETF, does not seem illogical. Circumspection is very important, primarily proactively. If more than 35% of an investor's portfolio is allocated to North American indices, prudence would command lowering it to no more than 25%, and the SMI can play an important role there.
For example, an investor with 35% or more of her portfolio allocated to North American Indices, should consider diversifying due to a build-up of a cluster of external and internal possibilities that can impact the North American Indices negatively.
For risk-adjusted returns of indices analyzed in this report, i.e., returns offered per unit risk & other diversification concerns, see our report: Which stock indices/index-based ETFs provide the best risk-adjusted returns
While it is true that turmoil in, or associated with North America, should substantially impact the SMI as well, as the SMI declined 53.42%, peak to troughs, in the 2008 financial crisis, and 21.6% in the 2020 market crash, compared to Nasdaq's similar decline of 52.1 for the 2008 financial crisis, and about 20% for the 2020 market crash, groups such as Nestle, Swatch, Lonza, Novartis, Roche, etcetera are growing in multiple emerging economies.
Presently, it is worth noting that the US is the biggest export market of Switzerland.
Long-term, while the sensitivity of SMI to international financial fluctuations should remain present, the reliance of its constituents on North America should continue to decline. Thus, it does make sense to have some exposure to SMI.
However, exposure should be limited to below 15% in the overall portfolio, ideally around 10%, with the broader assumption being that SMI is a slightly 'mellower' version of the S&P 500, with its constituents demonstrating healthy growth in emerging markets. The most prominent growth opportunity in the S&P 500 and Nasdaq is in tech, arguably.
For SMI, this is not the case primarily; thus, one needs to balance between the possibilities of the US tech giants taking a technological leap, resulting in healthy capital gains for shareholders, vs the mellower growth of the Swiss giants, buying a bit of both is better than picking just one.
Another important point here is that investors have favored risk-on assets in recent years; this approach has been driven by higher risk appetites influenced by expansionary monetary policy by key central banks. However, a ‘cheap money approach’ cannot last long-term, especially with concerns of inflationary pressures still being high. Thus, if the overall investment environment in Europe and North America reverts back to favoring a risk-off approach, where preservation of capital is the most important factor, SMI, and ETFs based on it—due to a prominence of somewhat stable names and being based on a risk-off currency (CHF)—should perform better than risk-on investments, such as Nasdaq, Crypto, etc.
See also:
How Much Can Metals, Energy, & Grains Fall In a Market Crash? | Which Is the Best Commodity to Hold?
Analysis of difference of returns, SMI, S&P 500, and Nasdaq (90 years of data analyzed in total)
Swiss Market Index vs. Nasdaq (374 months of data analyzed)
Pooled variance test:
Two sample t-test (pooled variance), using T distribution (DF=746.0000) (two-tailed) (validation)
1. H0 hypothesis Since p-value > α, H0 is accepted. The average of SMI's population is considered to be equal to the average. of the Nasdaq's population. In other words, the difference between the average of the two populations is not big enough to be statistically significant. 2. P-value p-value equals 0.198951, ( p(x≤T) = 0.0994755 ). This means that if we would reject H0, the chance of type I error (rejecting a correct H0) would be too high: 0.1990 (19.90%). The larger the p-value the more it supports H0. 3. The statistics The test statistic T equals -1.285688, is in the 98% critical value accepted range: [-2.3314 : 2.3314]. x1-x2=-0.0051, is in the 98% accepted range: [-0.009200 : 0.001785]. The statistic S' equals 0.00396 4. Effect size The observed standardized effect size is small (0.094). That indicates that the magnitude of the difference between the average and average is small.
What does this mean, simplistically? Is Nasdaq better than the Swiss Market Index?
There is no evidence with any statistical significance backing the assumption that the Swiss Market Index's monthly returns are statistically different from Nasdaq's monthly returns. The returns are statistically similar, and there is evidence supporting this conclusion.
What about the risk? Is the risk of Swiss Market Index (SMI) and Nasdaq the same?
F test has been conducted for definitive answers:
F test for variances, using F distribution (dfnum=373,dfdenom=373) (two-tailed) (validation)
1. H0 hypothesis Since p-value < α, H0 is rejected. The sample standard deviation (S) of SMI's population is considered to be not equal to the sample standard deviation (S) of Nasdaq's population. In other words, the difference between the sample standard deviation (S) of the two populations is big enough to be statistically significant. 2. P-value The p-value equals 2.292e-11, ( p(x≤F) = 1.146e-11 ). It means that the chance of type I error (rejecting a correct H0) is small: 2.292e-11 (2.3e-9%). The smaller the p-value the more it supports H1. 3. The statistics The test statistic F equals 0.4967, which is not in the 98% region of acceptance: [0.7856 : 1.273]. S1/S2=0.7, is not in the 98% region of acceptance: [0.8863 : 1.1283]. The 98% confidence interval of σ12/σ22 is: [0.3902 , 0.6323].
F test for variances, using F distribution (dfnum=373,dfdenom=373) (right-tailed) (validation)
1. H0 hypothesis Since p-value > α, H0 is accepted. The sample standard deviation (S) of SMI's population is considered to be less than or equal to the sample standard deviation (S) of Nasdaq's population. 2. P-value The p-value equals 1, ( p(x≤F) = 1.146e-11 ). It means that the chance of type I error, rejecting a correct H0, is too high: 1 (100%). The larger the p-value the more it supports H0. 3. The statistics The test statistic F equals 0.4967, which is in the 98% region of acceptance: [-∞ : 1.2374]. S1/S2=0.7, is in the 98% region of acceptance: [-∞ : 1.1124]. The 98% confidence interval of σ12/σ22 is: [0.4014 , Infinity].
What does this mean, simplistically? Is Nasdaq riskier than the Swiss Market Index?
There is evidence with statistical significance backing the assumption that Nasdaq's risk is different from the risk of the Swiss Market Index, as measured by variance. Nasdaq is riskier than the Swiss Market Index, and the risk profiles are not similar; there is statistical evidence backing this conclusion.
SMI vs. S&P 500 (374 months, or more than three decades of data analyzed)
Pooled variance test:
Two sample t-test (pooled variance), using T distribution (DF=746.0000) (two-tailed) (validation)
1. H0 hypothesis Since p-value > α, H0 is accepted. The average of SMI's population is considered to be equal to the average. of the S&P 500's population. In other words, the difference between the average of the two populations is not big enough to be statistically significant. 2. P-value p-value equals 0.646695, ( p(x≤T) = 0.323348 ). This means that if we would reject H0, the chance of type I error (rejecting a correct H0) would be too high: 0.6467 (64.67%). The larger the p-value the more it supports H0. 3. The statistics The test statistic T equals -0.458544, is in the 98% critical value accepted range: [-2.3314 : 2.3314]. x1-x2=-0.0014, is in the 98% accepted range: [-0.007400 : 0.001785]. The statistic S' equals 0.00316 4. Effect size The observed standardized effect size is small (0.034). That indicates that the magnitude of the difference between the average and average is small.
The Null is not rejected, therefore.
What does this mean, simplistically? Swiss Market Index is better than S&P 500?
There is no evidence with any statistical significance backing the assumption that the Swiss Market Index's monthly returns are statistically different from the monthly returns of the S&P 500. The returns are statistically similar, and there is evidence supporting this conclusion.
What about the risk? Is the risk of Swiss Market Index and S&P 500 the same?
F test has been conducted for definitive answers:
F test for variances, using F distribution (dfnum=373,dfdenom=373) (left-tailed) (validation)
1. H0 hypothesis Since p-value > α, H0 is accepted. The sample standard deviation (S) of SMI's population is considered to be equal to the sample standard deviation (S) of S&P 500's population. 2. P-value The p-value equals 0.7945, ( p(x≤F) = 0.7945 ). It means that the chance of type I error, rejecting a correct H0, is too high: 0.7945 (79.45%). The larger the p-value the more it supports H0. 3. The statistics The test statistic F equals 1.089, which is in the 98% region of acceptance: [0.8081 : ∞]. S1/S2=1.04, is in the 98% region of acceptance: [0.899 : ∞]. The 98% confidence interval of σ12/σ22 is: [0 , 1.3475].
The Null is not rejected, therefore.
What does this mean, simplistically? Is S&P 500 riskier than the Swiss Market Index?
There is no evidence with any statistical significance backing the assumption that the variance of the S&P 500 is different from the variance of the Swiss Market Index. The two have similar variance, and there is no statistical evidence to support the assumption that monthly returns of one have a different level of risk compared to the other.
Excel Data:
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