Swiss Pension Fund Aggregate Indices Performance Monitor

Credit Suisse Pension Fund Index & UBS Pension Fund Index Family

From 31.12.1999 onward, Credit Suisse calculate the first pension fund aggregate index, based on some 100 Swiss pension funds to whom Credit Suisse acts as Global Custodian. While the index has monthly increments, it is only published quarterly. With an inception date of 31.12.2005, UBS Group began to publish a family of pension fund indices, also based on custody. In contrast to the Credit Suisse counterpart, UBS publish their indices monthly. UBS distinguish pension fund indices by size: small, medium, and large pension funds, plus a general pension fund index. Below, the performance of Swiss pension fund aggregate indices is most often compared to the medium risk-tier synthetic pension fund benchmark index LPP-C40. This is a synthetic performance benchmark with a static asset allocation, of which 40% is in equities. Performance benchmarks in asset management (with the exception of peer group references) are free from any attempt to generate ‘value-added’. Thus, benchmark indices must not be read as quality reference. They are merely a description of circumstances.

Taking Advantage Of Monthly Performance Updates

The calculation of many quantitative and qualitative metrics requires large data data samples to generate meaningful results. Unlike the data for specific pension funds, any of the aforementioned aggregate indices is available with monthly frequency, thus permitting a depth of analysis and comparisons not possible with quarterly, or annual data. As individual pension funds show a very high degree of congruency with the aggregate indices used here, the analysis of monthly data for the aggregates can be taken as advance insight into pension funds investment performance released much later.

Exhibit-01 (above)

As entry into the analysis, Chart-01 shows the most recent 18 months of history, rebased to 100. The three graphs give an initial, visual inspection of pension fund performance in the context of the two main domestic asset classes, and global benchmark indices. A table indicates pension fund’s congruency with selected references, also calculated over the latest 18 months.

Exhibit-02 (below)

In Table-01, numerical data is given for fixed dates between the latest month end and 36 months ago. Actual values are shown in the upper section of the table, while the lower section displays corresponding rates of return.

Exhibit-03 (above)

Chart-02 shows annualised rates of return for the Swiss PF Composite and the medium risk-tier benchmark index (LPP-C40) calculated across various time frames, reaching as far back as the end of 1999.

Exhibit-04 (below)

The chart plots trailing 3-year normalised rates of return for the Swiss PF Composite and LPP-C40. The plot is slightly smoothed for the sake of filtering out mere ‚noise’. As a result, values may appear different when compared to the ‚actual’ data as shown in the Tables.

Exhibit-05 (above)

Just as the previous exhibit plotted trailing 3-year normalised returns, Chart-04 shows 3-year normalised risk. The metric used here is ‚observed risk’ (a risk definition conceived by Agathos) which is not only more sensitive than the popular pseudo-risk proxy ‚Volatility’. It is also directional, with no sign ambiguity. In Exhibit 05 observed risk is shown ‚as is’ meaning as a negative value which positions low risk in the upper part of the scale. That is fully intentional. Observed risk, as all other forms of ex-post risk tends to serve as a contrary indicator. Low readings (referring to the past) thus tend to suggest excessive optimism, and vice versa.

Exhibit-06 (below)

Swiss pension funds run their investment portfolios extremely closely to benchmark indices. The exact degree of dependence, or ‚congruency’ (= r-squared) will vary, subject to the length of record used in the regression analysis, the interval chosen (months, quarter, years, and not least the reference used. Table two covers monthly performance over the latest 60 months and compares the Swiss PF Composite to six different performance benchmarks. The regression data in the lower part of the table identifies the most congruent benchmark (for the 60 monthly changes entering the regression) in bold typeface.

Exhibit-07 (above)

The graph plots trailing r-squared values of the Swiss PF Composite with three risk-tier benchmarks (LPP-C25 = low, LPP-C40 medium, LPP-C60 high), showing how congruencies differ across time.

Exhibit-08 (below)

Chart 06 in Exhibit 08 illustrates the regression of monthly rates of change for the Swiss PF Composite and the (currently= most congruent benchmark (identified in Table 02 above). In addition to the equation data (intercept = alpha, slope = beta) the difference in change of value is indicated (SPF minus benchmark).

Exhibit-09 (above)

In Exhibit 09 several pension fund aggregates are compared to the Swiss PF Composite. Supplementing a simple comparison of change in values, regressions are shown using the composite as independent variable (x-value), and each of the aggregates as dependent (y-value). In the lower part of the table, the sums of monthly gains and losses are shown, expressed as values relative to those for the composite.

Exhibit-10 (below)

Chart 07 covers the latest 60 months of history and places annualised returns (vertically) and observed risk (horizontally) in a grid. The dashed diagonal line describes equilibrium (risk efficiency = 1.0) between risk and return. The desirable place to be in the scatter is above (high return) and left (low risk). The scatter to the right shows readings a year ago.

Exhibit-11 (above)

Chart 08 in Exhibit 11 splits monthly price changes into gains (green) and losses (red), showing cumulated values for the Swiss PF Composite (bars) and LPP-C40 (black line), using the percentage scale on the left. The ratios of Swiss PF Composite readings to those of the benchmark are shown as coloured line, using the scale on the right. In the table below the plots, numerical values are given for current readings as well 36, 24, and 12 months ago.The ratio of relative gains to relative losses is also given.

Exhibit-12 (below)

Exhibit extends the observation period to ten years. The chart is a straightforward plot of values over 120 months, rebased to 100. The Swiss PF Composite is shown as red line, area shaded in grey denotes the LPP-C40 benchmark. The return index for 10-year Swiss government bonds is also included, as are three compounding rates of return.

Exhibit-13 (above)

Shown are statistics for LPP-C40, the Swiss PF Composite and five pension fund aggregates. Distance from highs and lows observed during the most recent ten years is given. Annualised rates of return across ten years are adjusted for observed risk calculated for the same period yielding risk-adjusted returns. Finally, data form the distribution of monthly returns are shown. Horizontal distortion of the distribution greater than 10% to either side of the median is highlighted.

Exhibit-14 (below)

Shown is the Swiss PF Composite’s performance, relative to each of the risk-tier benchmarks over ten years, rebased to 100 120 months ago in the upper chart. The lower chart plots the relative performance of the higher risk universe (LPP-C60) relative to the lower risk universe (LPP-C25).

Exhibit-15 (above)

The chart shows drawdown from all time highs of the Swiss PF Composite. Horizontal lines represent normalised risk of each risk-tier, putting drawdowns into perspective.

Exhibit-16 (below)

Exhibit 16 seems more complicated than it is. It tracks the success ratio of Swiss pension funds all the way back to the end of 1999, differentiation not only between time increments (months, quarters, years) but also the success ratio versus all three risk-tiers. The red bars represent frequency of performance better than benchmark. Values are shown net of coincidence (equal to 50%). In the table above each bar chart, count and average degree of performance better (green) or worse (red) than the benchmark is shown numerically. The data serve to calculate weighted (by frequency and degree) performance of the sum of increments above (green) or below (red).

Data Sources

Raw data underlying all calculations and illustrations was sourced from:

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