Portfolio compositions

Considering the compositional weights of the different portfolios in table 5.1 we can make the following observations. In the metal sce-nario the optimization reveals that:

• nine metals are present in the safest, while only two are populating the opti-mal portfolio,

• copper, silver and nickel do not appear in the safest, nor the optimal portfolio,

• the combination of uranium and gold rep-resents more than half of the composition of the safest portfolio,

• palladium represents with 83% of the weight the most important metal in the optimal portfolio,

• only gold appears in both the safest and optimal portfolio.

When looking at the ore type scenario a differ-ent picture emerges. It appears that:

• metals that cannot be extracted from mono-metallic mineral deposits find their expression in poly-metallic ones (i.e. platinum in MS.STLW, cobalt in in LAT.MOA or RED.TNK, lead in MVT.TRI). The weights of these poly-metallic ores within the safest portfolio are also similar to those of their respec-tive metals in the safest metal portfolio.

• of all 28 investigated ore types only nine are necessary for the safest and one for the optimal portfolio.

• uranium and gold dominate the safest portfolio with more than 50% of their combined weight, which is very similar to the metal only portfolio.

• the mono-metallic gold ore PLAC.YUK represents the entire optimal portfolio.

M - metals O - ores

metal MVP TAN MVP TAN ore type

% % % %

Al 14.53 14.93 BAUX.WP

Au 27.91 16.83 29.12 100 PLAC.YUK

Co 6.82

Mo 4.72 4.96 POR.CMX

Pb 1.93

Pd 83.17

Pt 2.13

Sn 9.95 10.24 PLAC.BAN

U 26.42 26.91 UUN.CIG

Zn 5.57

Ni-Co 1.04 LAT.MOA

Pt-Pd-Au 1.11 MS.STLW

Pb-Zn 6.22 MVT.TRI

Cu-Co 5.46 RED.TNK

µ 0.28 0.44 0.28 0.43 µ

σ 2.48 6.21 2.52 4.06 σ

Table 5.1: Portfolio weights, risk & return 1948-2018

Discussion

When we compare the optimization results for the metal and ore portfolios, we can see a gen-eral match between metals and their metal con-taining ore equivalents. All metals that have mono-metallic equivalents are represented by such in the safest and optimal portfolio. All metals that can only be mined in poly-metallic ores are represented by the latter. So does cobalt in the safest metal portfolio finds its ore equivalent in LAT.MOA and RED.TNK, platinum in MS.STLW, while lead and zinc

find theirs in the MVT.TRI ore. The only dis-crepancy for this general rule can be found in palladium. As we can see in figure 5.2, the poly-metallic ore equivalent for palladium has in MS.STLW a substantial lower return com-pared to the mono-metallic gold ore. Therefore, the gold ore becomes the single most important type in the optimal ore portfolio. It is thereby a perfect example on how geological constraints can make the outcome of the ore optimization so much different than that for metals.

5.4 Summary & Discussion

This chapter provided five major insights.

First, metal and poly-metallic ore returns de-picted in a MPT risk-return framework reveal that distinct metal group zones exist, which show similar risk and return characteristics.

We found that a coherent base metals zone in the center is surrounded by energy at the bot-tom and left, precious metals above and minor metals to its right.

Secondly, we determined that metals that ap-pear in mono-metallic ores find their expression in such within the ore portfolio analysis. Met-als that are not found in mono-metallic ores find their equivalent in poly-metallic ores. This however holds only to a degree since we also have seen that geological constraints do have an influence on the results of the ore portfolio optimization (e.g. Pd vs. MS.STLW).

Thirdly we showed that fewer assets with higher weights prevail in optimal portfolios, while the opposite holds for the safest portfo-lios. This finding aligns well with the old saying of ”there is safety in numbers!”, i.e. diversifica-tion reduces risk.

Moreover we revealed that the returns of the optimal portfolios for both the metals and ore case exceed the risk free rate of return rf. The safest portfolios on the other hand show returns that are inferior to it.

Lastly, we were able to determine the compo-sition of the safest and optimal portfolios for metal and ore portfolios for the time between 1948 and 2018. While a combination of assets represent the safest portfolios, we can state that palladium can be considered the most lucrative metal, with PLAC.YUK being the most

lucra-tive ore type in the considered time period.

There is one important implication of these in-sights. As we have seen, in the period of over 70 years the Metals & Mining Industry can be considered both, outperforming and underper-forming compared to the risk free rate rf, de-pending on what kind of portfolio is considered.

While its is good to see that mining is having a positive return overall, it still begs the ques-tion on why investors should invest in anything other than treasury bonds, gold and palladium.

This is where the question of timing comes in.

We have to keep in mind that long term returns of assets since 1948 do not necessarily mean that there is not a short term upside for met-als such as uranium or aluminum that exceed the returns of treasury bills or other metals.

After all, long term averages always diminish upside return potentials over shorter time peri-ods. This requires a different, dynamic analysis which will be considered in the next chapter.

There are also pitfalls to this approach. First, if we would expand the analysis to include all ores for which data is available that go beyond the generalized ore types, the results, especially those for the safest portfolios might be differ-ent. In addition we have to keep in mind that the results are based on the values of metals and in-situ value of ores. Neither does include costs that are associated with the production of both. Taking those into account, as well as their changing nature over the years would al-ter the respective return behavior and therefore the results. Hence a net return analysis would certainly look different than what we present here.

5.5 Conclusion

The analysis of a 70 year risk-return data set for metals and ore types provided valuable in-sights, facilitating tools and methods pioneered by the Modern Portfolio Theory. The analysis not only provided a new understanding of how different metals and ores relate to each other in terms risk and return, but also gave

spe-cific answers on what the most lucrative and safest ore types and metals are in recent his-tory. These fundamental insights should help in shaping our understanding on what ore types are the must lucrative or the safest over long time horizons.

”To improve is to change, so to be perfect is to change often.”

Winston Churchill

Chapter 6