Aug 29

Beware the Benchmark Hugger … it might be you?


For quite a few years now, many commentators and researchers have criticized active strategies that charge active fees to receive benchmark-like returns. If a portfolio looks a lot like the benchmark it is trying to outperform, it doesn’t mean there won’t be outperformance, but after taking fees into consideration it is much more difficult. Taking larger position that are different from the benchmark will provide a portfolio manager with more opportunity to add alpha (risk-adjusted return) but at the same time, if those bets are wrong, then there is greater negative alpha potential too.

So, a popular portfolio construction method of many multi-manager portfolio constructors is to build portfolios of strategies which have greater idiosyncratic (or non-market) risks. The hope is to create greater alpha potential for their portfolio by avoiding the benchmark huggers, and at the same time diversify away various manager risks with the multi-manager approach. Sound reasonable? Well it does, unless you end up building the same type of portfolio you are trying to avoid.

Ultimately portfolio construction is about the efficient capture of risks we believe will add value and the avoidance of risks we believe won’t add value. Combining highly active strategies is about capturing idiosyncratic risks of an active manager in the hope positive alpha is created. What is sometimes forgotten is that market risk cannot be diversified away (except by other markets) and the diversification of strategies may diversify away the idiosyncratic risk you may be trying to capture.

Idiosyncratic Risk vs Alpha

Idiosyncratic risks are non-systematic risks of a portfolio. In the context of equities, those systematic risks include the market (e.g. S&P/ASX 200 or MSCI Australia), and perhaps various other systematic risks commonly called factors such as Value, Size, Momentum, et al.

We pay active managers the higher fees to turn idiosyncratic risk or these non-systematic bets into positive returns (otherwise known as positive alpha). But how much idiosyncratic risk is normal?

Chart 1 below breaks up total portfolio risks of all active strategies in the Australian market over the last 10 years into various systematic risks as well as idiosyncratic risk (green). It shows that over the last 10 years, the average idiosyncratic risk of all strategies (equal weighted) has been between 5% and 10% of total portfolio risk on a rolling 3 year basis with the dominant component being the market, which caters for around 85% to 90% of total portfolio risk. The other components of risk in this analysis come from a variety of factors which are important but are not the focus on this article.

Chart 1 – Portfolio Risk of Active Australian Equity Strategies

Source: Delta Research & Advisory

So the simple conclusion from this piece of analysis is that the market is easily the major component of active strategy’s total risk and this is consistent with numerous studies of long only strategies  … including Brinson, Hood and Beebower (1986), who showed that more than 90% of the portfolio risk came from the asset allocation decision (or market allocation decision).

Basically, if a strategy is long only, then market risk is going to play a significant role in the portfolio outcomes and on face value, and the alpha potential comes from a much smaller component of a portfolio’s risk.

Chart 2 – CAPM Alpha vs Market Risk Contribution

Source: Delta Research & Advisory

Chart 2 shows the relationship between Idiosyncratic and Alpha for Active Australian Equity strategies over the last 5 years.

Whilst Chart 2 as a whole does appear to be a fairly random, the line on the chart is placed to show that there may be a relationship between maximum Alpha and idiosyncratic risk. Basically maximum alpha appears to diminish with decreasing idiosyncratic risk. The chart also may suggest that the lower the idiosyncratic risk, the lower the spread of Alpha, potentially supporting concerns about Benchmark huggers not producing high enough Alpha but also avoiding negative alpha, which many in the past have suggested relates to minimising career risk, but I digress.

An Experiment – with a Portfolio of Highly Active Strategies

So, to demonstrate some of the effects of building a portfolio of highly active strategies I have conducted a simple experiment.

Using the Delta Factors database of actively managed strategies, I chose five strategies that each produced positive Alpha over the last 5 years, had high levels of idiosyncratic risk (i.e. more than 15% of total portfolio risk). I would imagine this is a relatively common approach. That is, choose the strategy with the best relative performance with some basic appealing characteristics.

The portfolio of strategies, for the sake of simplicity and avoid accidental strategy bias, is equal weighted and rebalanced monthly (and transaction costs are ignored). Table 1 shows the basic market characteristics of five chosen funds.

Table 1 – Active Australian Equity Strategies – June 2012 to June 2017

Source: Delta Research & Advisory

These five funds, which all appear within the data from Chart1 and 2, have very impressive characteristics, insofar that historically they satisfy what we would typically want from an Australian equity portfolio. That is, they have:

  • Full exposure to the Australian sharemarket … i.e. Market Beta ~1
  • Strong value-add … i.e. Average Alpha ~5.1%
  • Are truly active compared to peers with average Idiosyncratic Risk around 25%

This is obviously historic analysis only over the last 5 years, and we all know the past doesn’t equal the future … but it doesn’t stop of us from hoping. The construction of these highly active funds is about moving away from the benchmark huggers to produce the stronger possibility of high alpha.

So far so good.

Obviously, multi-manager portfolios comprise of more than one manager for each asset class. This is always done for diversification purposes. It may be diversification of styles, managers, or a variety of other risks. What many don’t measure or deeply understand is that a guaranteed outcome of diversification will always be the diversification of Idiosyncratic risk as you cannot diversify away market risk.

As Table 1 shows, this portfolio of active strategies has a historic average of 25% Idiosyncratic Risk. On its own that may be appealing but when they are combined into this portfolio, ignoring rebalancing transaction costs, the Idiosyncratic Risk decreases to 10% … representing a 60% reduction in the very risk we are hoping to capture! The

Now this 60% reduction in risk is specific to the portfolio, and would be lower, if fewer strategies were chosen. Either way, this portfolio of 5 strategies has created is a portfolio with significantly lower idiosyncratic risk than every single component strategy. If there is a belief that greater idiosyncratic risk is required for high Alpha, then this portfolio has significantly reduced that opportunity on a forward-looking basis. The past does not equal the future but it would be difficult to see that this is not a move towards a benchmark-like portfolio … and for highly active fees.


Now many might argue this is just one example and not all combinations of managers will reduce the idiosyncratic risk by this much … and that is absolutely correct. The question becomes, do you know the impacts of the risk characteristics of your multi-manager portfolio? And I would guess many would answer, “no”.

Overdiversification is a common reality in construction of multi-manager portfolios and can result in paying big bucks for more index like returns. But to manage this risk it is essential to measure it. Measuring risk contributions will help constructors ensure the desired risks are being captured more efficiently and can help reduce the effects of desired risks being diversified away. Given the growth in managed accounts across the financial planning industry and the shift towards single strategies for many clients, increased measurement of risks has never been so important for many investors.

Diversification is the only free lunch in investing. Mathematically it is due to less than perfect covariance or correlation as Harry Markowtiz’s Nobel Prize winning paper showed, but better portfolio construction is when you don’t diversify the risk you are trying to capture. That way your free lunch will hopefully taste nice.

Referenced Papers

Gary P. Brinson, L. Randolph Hood, and Gilbert L. Beebower, “Determinants of Portfolio Performance”, The Financial Analysts Journal, July/August (1986).

Markowitz, H. 1952. Portfolio Selection. The Journal of Finance 7 (1): 77–91.


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Aug 23

Good benchmarks, Bad benchmarks … and how to choose the right one

The following article was first published in the August Professional Planner magazine and can also be found on their website by clicking here … otherwise just read on …

The management guru, Peter Drucker, is attributed with the phrase, “you can’t manage what you don’t measure” and whilst we know that is not completely true, as we manage numerous tasks without measuring them every day, it is really about defining success. Having a measure of success helps define a goal to achieve and in the world of investment management, success often comes down to performance compared to a benchmark. Whilst most times this is a fairly simple, benign, and obvious issue, there are many examples where benchmarking is done poorly, can be misleading, and ultimately increase risks unbeknownst to the adviser or investor. Individual investors and customised client portfolios of financial advisors rarely have benchmarks.

This article looks at good benchmarking, bad benchmarking, plus a few tricky issues for consideration. An important part of the investment management process is performance analysis, whether up-front during the investment selection step, or during the review phase, and choosing the right benchmark should be integral.

What is a good benchmark?

In essence, a good benchmark is representative of a strategy’s investment universe and is therefore representative of its risk and return characteristics. This means some of the key characteristics of a good benchmark may include being:

  • Underlying securities and their weights are clearly defined
  • It is possible to passively invest in the benchmark
  • Rules behind the creation of the benchmark are clear and frequently calculated
  • Consistent with intended style or bias

Satisfying these characteristics is often relatively simple with little difficulty in finding suitable benchmarks for most strategies. For example, an Australian equity strategy may be small cap, large cap, or even absolute return focused, but if its mandate dictates that its investment universe is the top 300 stocks listed on the Australian Securities Exchange, an appropriate benchmark may be the S&P/ASX 300 Total Return index. This benchmark is even appropriate for the Small Cap focused strategy if it can invest in larger companies. If the Small Cap strategy is excluded from investing in the top 50 companies, then its benchmark could become the S&P/ASX 300 Total Return excluding the securities from S&P/ASX 50.

Some of the more well-known benchmarks, and their respective asset class, include:

  • S&P/ASX 200 Total Return – Australian Equities
  • MSCI World GR – Global Equities
  • Bloomberg Ausbond Composite – Australian Bonds, and
  • Bloomberg Barclays Global Aggregate – Global Bonds

Each of these satisfy the above-mentioned criteria required of a good benchmark.

Bad Benchmarks

Unfortunately, there are many strategies using inappropriate benchmarks.  The main culprits are often absolute return, credit or high yield debt, and CPI-plus…and/or…pretty much any risk strategy that uses Cash (or CPI) as a benchmark. Cash (or CPI) may well be related to an investment objective, particularly given their definitions as the risk-free rate and the expectation to outperform, however, cash (or CPI) will not be representative of strategy risks.

Similarly, when a strategy invests outside of the investment universe of their benchmark, it may be time for a new benchmark. Investing outside a benchmark may change the risk and return characteristics of the strategy so comparisons can become inappropriate and therefore riskier than realized. Think bond strategies that move up the credit curve and invest in unrated securities, despite having a benchmark that may be cash or investment grade quality.

Cash will always fail the test of a good benchmark for any risky strategy as it will never be representative of the investment universe. The investment industry largely built on the belief that higher risk is required to produce higher return so over the long run, of course risky strategies should outperform cash if that risk is remotely fairly priced; but that does not mean the strategy has exhibited appropriate return or risk to achieve it.

Mixing Benchmarks with Portfolio Construction method

Many strategies will claim they are “benchmark unaware”. Being “Benchmark unaware” has little to do with measuring the success of a strategy and is more a reflection of its portfolio construction methodology. In other words, a “benchmark unaware” portfolio is probably constructed with little consideration of their asset class benchmark, may only hold “best ideas” which are weighted according to conviction of success and not their market capitalization which is a core characteristic of most liquid benchmarks. However, to ascertain whether such a strategy is successful, it is appropriate to measure against a Benchmark that is representative of the investment universe.

For example, an “absolute return” equity strategy may claim to be “benchmark unaware”, with the ability to invest in any equity market in the world. In this case, the benchmark should be more like the MSCI All Countries World Index (ACWI) and certainly not, the frequently seen, cash or cash-plus benchmark. MSCI ACWI will be far more representative of the risk the strategy and if there is likely to be significant cash holdings on a regular basis, then perhaps a strategic or expected level of cash could be included in the benchmark definition. Confusing how a portfolio is constructed does not necessarily change the risk/return profile of a strategy … as risk-adjusted excess return to a traditional benchmark still requires significant skill of a manager no matter the level of active or idiosyncratic risk.

Multi-Manager Problem … can turn into a Multi-Benchmark Problem

Most superannuation funds and financial planners, use a multi-manager approach to designing investment portfolios. Because asset allocation is a key part of the portfolio construction decision, each asset class should be appropriately benchmarked which ultimately frames the underlying manager selection towards strategies that produce the desired asset class characteristics.

Where many investors start to make mistakes (or at least increase risk), is that there is often very little consideration of the asset class benchmark, and strategy selection can become more focused on the strategy’s own benchmark. It is possible to have all underlying strategies outperform their own benchmarks but underperform the asset class benchmark.

One of the more common examples of this is the inclusion of Small Cap Australia Equity strategies as part of the Australian equities asset allocation. Numerous performance analyses over the years have demonstrated outperformance by active managers in the small cap space so the inclusion of these strategies is based on this alpha potential. The fundamental belief is that small caps are a less efficient market enabling active managers to exploit opportunities to produce excess returns. However, what is sometimes ignored is the ability of small caps to produce risk-adjusted outperformance against the asset class benchmark, which may be the S&P/ASX 300 or MSCI Australia index. Alpha amongst Small Caps does not mean Alpha amongst Large Caps.

Another example is Infrastructure. Performance analysis across the Infrastructure suite of products in Australia is often troublesome as it appears almost every strategy has a different benchmark; so understanding whether one strategy is potentially superior to another, can be difficult if looking for outperformance. Different benchmarks between strategies is simply an apples and oranges comparison.

What portfolio constructors must focus on is comparison of strategies to their own asset class benchmark and consideration as to whether a strategy will outperform it. Taking this approach should provide better insights to relative performance behavior and relative risks … apples and apples comparisons are essential for better portfolio construction decisions.

Benchmarks 2.0

With the significant growth in Exchange Traded Funds, Smart Beta, and multi-factor investing, Benchmarking is becoming a multi-layered exercise. Assessing strategies to a traditional asset class benchmark continues to be important, but the separation of determining success (or otherwise) from style or security selection is also important … you should know what you are paying for. Assessing strategies to their own style benchmark enables a deeper understanding of manager capability.

For example, it is widely accepted that a value bias across most equity markets around the world has produced outperformance compared to traditional market-cap weighted benchmarks. Largely thanks to ETFs, it is much easier and cheaper to buy style indices, like Value, so assessing an actively managed value strategy against a value benchmark will go towards understanding whether the manager is skilled at stock selection or is simply successful on the back of the systematic value tailwind. Why pay active fees, if you can get a similar result from a lower cost passive strategy that has the desired style.

What to do?

This article has touched on a few issues around benchmarking and there are many others that can be addressed another time. Either way, there are distinct lessons that can enable better strategy analysis and therefore improved portfolio management decisions. The main lessons this article hgas touched on include:

  • Choose your own benchmark that reflects the investment universe of the portfolio (or asset class) you are designing
    • If your investment philosophy dictates a preferred style, then choose a secondary benchmark that is reflective of that style
  • Assess potential strategies against your chosen benchmark(s) to gain a better understanding of the relative risks, and of course, whether you believe there is outperformance or risk-adjusted value-add potential
  • If a strategy manages to a different universe and/or a particular style than yours, assessing the manager against benchmarks that reflect their investment universe and/or style can help determine whether they are skilled, maybe lucky, or otherwise; but this is secondary to the desired characteristics of your own measure of success

Ultimately, good benchmarking is simply about creating apples and apples comparisons to better measure success. Comparisons may be return, risk, or a range of other metrics. Be careful of mixing benchmarks with objectives, do not accept benchmarks not reflective of a strategy’s investment universe; and hopefully improved measures of success will lead to more robust portfolio management and better results for investors.


Bai-Marrow, A., & Radia, S. (n.d.). Benchmarks and Indices. Retrieved from Research & Position Papers – CFA UK:

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Aug 07

Four Stages of Investment Analysis

Professional Planner magazine published the following in their June Magazine and also on their website … you can also click here.

Otherwise … read on …

On the first Friday in May, the Chief Investment Officer of the Future Fund, Raphael Arndt, spoke about how they are refining their approach to their listed equities investment program. The primary concern was whether “they are paying for (active) managers stock picking skill”. His view was “if we want factor exposures, we can access factor indices much more cheaply without paying active management fees”. The primary catalyst to this thinking is that the Future Fund faces similar issues to all investors. That is, considering the long-run outlook for investment returns being nothing more than single digits, fees are a very high proportion of the total portfolio expected return so any reduction of fee from increased efficiency will have significant impact on final returns.

As a result, the Future Fund redefined the objectives of their $38billion of listed equities into 4 categories that can be summarised as …

  1. Capturing equity market risk
  2. Harvesting long term equity factor premia
  3. Delivering uncorrelated, good, skill-based returns
  4. Accessing desired exposures with a whole of fund perspective

I’m sure you’ve judged by the title, this article is not about explaining the direction of the Future Fund. The objective of this article is to communicate the benefits of a deeper level of investment analysis than I believe is currently performed across our industry. The reason why I mention the Future Fund’s approach to their listed equities program is that they are the most recent and maybe most prominent example of the application of, what I call, the fourth stage of investment analysis. That said, there is nothing new in the application of the fourth stage but with improving technology, analytical tools, access to a greater depth of breadth of investment strategy, and the financial planning industry’s move towards managed accounts, it is probably time for advised portfolios to take the same step.

Successful application of the fourth stage of investment analysis is likely to increase the move towards designing investment portfolios that have a stronger reflection of our investment philosophy (or beliefs), a more efficient investment allocation, with increased performance risk management.

So what are the Four Stages?

…they are best explained using a graphic as shown in Chart 1.

Stage 1 is pure performance analysis and is what most clients are solely focused on. It is focused on the overall return result and perhaps the volatility. But whilst Performance is typically a primary target, performance numbers alone (or over time) provides little insight as to whether an investment is truly good or bad. Looking only at performance often leads towards bad investment behaviours, such as selling low and buying high.

I would argue that most of the investment advisory community are at Stage 2 which is the assessment of the quality of an investment by comparing to a benchmark … which often concludes that outperformance is good and underperformance is bad. The choice of the benchmark is a critical part of this analysis and where most mistakes are made at Stage 2. Sometimes the benchmark is a peer group which may be fine depending on the investment type but best practice suggests a benchmark should be liquid representation of the underlying investment universe … so is typically a market-cap weighting of available investments. Common benchmarks include the S&P/ASX 200 for Australian equities or MSCI World for global equities.

If we believe that to achieve higher returns requires the acceptance of higher risk, then outperformance alone may be a dangerous way of assessing whether an investment is good or bad. Strategies may have benchmark outperformance over long periods of time, but not because they are necessarily skilful, but because they may be taking on lots of risk. A simple strategy example is a geared Australian Shares index fund … with the Australian sharemarket producing a performance of nearly 11%pa over the last 5 years, an initial loan to value ratio of 50% and borrowing costs at a very high and fixed 5%pa, would have produced returns for the fund in the vicinity of 15%pa. This outperformance has nothing to do with being skilful or good, it’s simply the result of accepting much greater risk than the market.

Chart 1 – Four Stages of Investment Analysis

Stage 3 adjusts for market risk and divides the portfolio’s risk into the two components we hear so much about … alpha and beta. Alpha is the market risk-adjusted outperformance often associated with measures of active management skill; and beta represents a strategy’s exposure to the market. For the above-mentioned Geared Australian Equities Index example, the Alpha should be slightly negative and close to the cost of the fund, whilst the beta (thanks to a Loan to Value ratio of 50%), should be up to 2 … which represents twice the market exposure (or risk). Whilst the geared index strategy has strong outperformance, it’s negative alpha suggests there is no skill because the return has been driven by having double the market exposure.

Understanding an investment’s Beta, or exposure to the market, is an essential part of portfolio construction because this is the measure that helps portfolio constructors determine the asset allocation role of a strategy. If we want to choose an investment that is fully representative then its beta should be around 1. If the strategy’s beta is less than 1 then that strategy may be holding significant amount of cash, so it potentially compromises the desired asset allocation and reduces the portfolio’s goal of capturing the intended “equity risk premium”. A fund with an expected beta of less than 1 will underperform its benchmark in a strong bull market, unless there is significant skill (or alpha) and, of course, that is far from a guarantee. However, that skill may also be due to luck or perhaps styles or factor exposures that happen to be in favour over a period of time. This is where Stage 4 Investment Analysis may be required.

Stage 4 investment analysis is where the Future Fund is at along with many other institutions and sophisticated investment professionals. Stage 4 further adjusts for non-market systematic risks which are typically represented by the Smart Betas that can purchased somewhat cheaply. In English, typical Smart Betas exposures may include style indexes such as Value (e.g. Low PE Ratio), Size (e.g. Small Cap), Momentum (e.g. Last Year’s best performers), Quality (E.g. High Profitability and Low Debt), and others. With the growth of the Exchange Traded Fund (ETF) market into these Smart Beta exposures, purchasing your preferred style of investing is getting easier. As Raphael Arndt of the Future alluded to, purchasing factor exposures is cheaper than pure active management and may sometimes present a more efficient way of gaining desired exposures to reflect your investment Philosophy (or beliefs) around what works in markets.

Stage 4 investment analysis explains which factor exposures (or Smart Betas) are driving portfolio outcomes as well as the exposures to each. This means that the Multi-Factor Alpha (refer Chart 1 – Stage 4) is the pure alpha (or skill) a manager brings to a strategy and is the result of their success in security selection, market timing or potentially, smart beta timing. Positive Multi-Factor Alpha is the holy grail of active management and when you consider the cheaper access of market-cap-weighted index funds, and smart beta funds, positive Multi-Factor Alpha is what investors should be paying the high fees for.

So, the Stage 4 investment analysis framework increases the chances of the portfolio constructor to choose investments that:

  • Reflect one’s investment philosophy with demonstrated characteristics around desired styles that are expected to outperform (Smart Betas/Factors)
  • Reflect the desired asset allocation with demonstrated market exposure that is likely to continue (Market Beta)
  • Has active managers with demonstrated potential skill from positive risk-adjusted outperformance (Multi-Factor Alpha)


  • Demonstrates a strategy is “true to label”. That is, the strategy’s market beta and smart beta exposures are consistent with the investment process communicated by the manager

Whilst this is not the end of the portfolio construction or strategy selection process or story, implementing a Stage 4 investment analysis framework is a strong move towards a deeper understanding of portfolio risk drivers.

Understanding likely portfolio risk drivers means potentially greater efficiency as risks can then be accepted, mitigated, or even removed. It changes the manager selection or retention approach to one of return driven to risk driven. This enables strategy roles to be more specifically defined. When executed correctly, portfolios may have lower strategy turnover, therefore reduced investment costs, and therefore better returns.

Perhaps the next steps include answering the questions include … what risks do I believe add value and how do I capture them and remove the ones I don’t believe in? This potentially brings us into the world of factor investing ….


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Feb 13

Economic Growth & Sharemarket Returns … looking for relationships


According to (Elroy Dimson, 2010) the “conventional view Is that, over the long run, corporate earnings will constitute a roughly constant share of national income, and so dividends out to grow at a similar rate to the overall economy. This suggests that fast-growing economies will experience higher growth in real dividends, and hence higher stock returns”.

Unfortunately, empirical evidence showing positive relationships between economic growth and sharemarket returns has not been as strong as many have expected and there are many examples, where high economic growth has coincided with terrible sharemarket returns. Emerging Markets and China are possibly one of the better examples of this relationship breakdown in recent years.

The purpose of this article is to revisit the relationship between economic growth and sharemarket returns. Whilst much of the literature, including on this subject focuses on the US, the data presented in this paper also includes Australia and United Kingdom. Why Australia and United? … A couple of reasons and both somewhat naïve. Firstly, they are spread geographically with different major trading partners, and secondly, they were the regions which enabled easiest access to long term data.

Data Sources

For the analysis that follows, all equity market returns are accessed from MSCI database, are calculated in local currency (i.e. AUD for Australia, USD for USA, and Sterling for UK), and data series commence 31/12/1969. The specific indices are MSCI Australia GR, MSCI UK GR, and MSCI USA GR.

Real GDP figures come from each country’s central bank data sources. That is, Reserve Bank of Australia, Bank of England, and Federal Reserve. Please note, at the time of writing Real GDP figures were not available for the December 2016 quarter for either Australia or USA.

Quarterly Analysis

Equity Returns vs Real GDP

Let’s start with a direct comparison of equity returns and real GDP using quarterly data for each country. Figures 1,2, and 3 show the results for Australia, UK, and USA, and only USA shows any relationship. Both Australia and UK show no relationship between equity returns and the same quarter Real GDP results at all…this is supported by the flat trend lines and the R2 result being close to 0.

The USA Trend suggests that for every additional 1% in quarterly Real GDP Growth it correlates with an additional ~2.2% in quarterly equity market returns. With an R2 of less than 5%, Real GDP does produce a great deal of explanatory power of equity returns, but the slope s highly statistical significant given it’s t-statistic is 3.04.

This suggests that of the three countries, if you can predict the quarterly GDP in advance then it might provide a slight edge in the USA only … it doesn’t necessarily help in Australia or the UK.

Figures 1,2, and 3 – Quarterly Equity Returns vs Real GDP for Australia, UK, and USA;
Figure 4 – Regression results for Figure 3’s trend line

Source: Delta Research & Advisory

Is the Sharemarket a leading indicator?

For many the potential spurious relationship between quarterly equity returns and Real GDP is unsurprising because the belief is that the sharemarket is more of a leading indicator, meaning that its direction is a forward predictor of future economic growth. The initial test for this is done by comparing the last period’s equity market returns to the current period real GDP.

The charts and results are shown in Figures 5,6, and 7. Once again, only the USA show a statistically significant trendline suggesting that last quarter’s equity market return might be a leading indicator for this quarter’s Real GDP. That said, the US Equity market return only explains around 10% of the variance in the next quarter’s Real GDP so there is a lot of unexplained variance due to other factors, which should not be surprising.

Neither Australia nor UK show a statistically significant relationship for last quarter’s sharemarket returns being predictive of this quarter’s Real GDP result.

Figures 5,6, and 7 – Quarterly Real GDP vs Equity Returns (Lag 1 Qtr) for Australia, UK, and USA; and accompanying regression statistics

Source: Delta Research & Advisory

Stretching out the Timeframe … Annual

So far we have looked at quarterly data only and many might argue that it is longer run economic growth that is important to sharemarket returns because quarter-to-quarter can be potentially meaningless due to volatility and the associated uncertainty or potential lack of obvious trend.

Figures 8,9, and 10 show the relationship between annual returns and annual Real GDP and the only significant trend is for the USA. The relationship between annual equity returns and annual Real GDP appears insignificant for both Australia and the UK.

Figures 8,9, and 10 – Annual Equity Returns vs Real GDP for Australia, UK, and USA;

Figure 11 – Regression results for Figure 10’s trend line

Source: Delta Research & Advisory

Interestingly compared to the quarterly results from Figures 3 and 4, there is greater explanatory power in the regression model. Whilst quarterly Real GDP explained around 10% of the variability of quarterly equity returns in USA, annual Real GDP explained more than 28% of the annual equity market variability in USA between 1970 and 2015.

These results show only a stronger relationship over longer term, once again, for USA.

Sharemarket as a Leading Indicator?

So is this year’s sharemarket return likely to provide an indication of next year’s Real GDP Growth? At the quarterly level, there appeared to be potential evidence suggesting the sharemarket might be a leading indicator in the USA only but at the annual level? It is the reverse result. That is, the relationship between lagged annual equity returns and real GDP growth is strong for Australia and the UK and weak for USA…refer Charts 12 through to 14.

In Australia, for every 10% in annual equity returns has meant next year’s Real GDP growth has been 0.383% higher. For the UK, that relationship is similar whereby every 10% in annual returns meant it’s Real GDP was 0.312% higher the following year. Still with R2 of only ~23% and ~15% respectively, there is still a lot of unexplained Real GDP variability as you would expect.

Figures 12,13, and 14 – Annual Real GDP vs Equity Returns (Lag 1 Yr) for Australia, UK, and USA; and accompanying regression statistics

Source: Delta Research & Advisory

Down the Final Stretch

The final analysis looks at 5 Year data. Statistical tests on this small sample size can be fraught with danger so a table of Real GDP and Equity Returns is presented in Figure 15 below. As mentioned above, all equity returns are calculated in local currency terms; red cells indicate below average results and green cells indicate above average results.

Overall there does appear to be some potential patterns. The Scatter Plot (Figure 16) shows that 5Year periods of above 3.5% Real GDP Growth coincided with double digit equity returns for all 3 countries, whilst Real GDP Growth averaging below 1%pa produced a couple of single digit annualised equity returns. Real GDP Growth between 1%pa and 3.5% pa … no obvious pattern.

Of course, this analysis is a little simplistic as it starts at a fairly random start date, i.e. when the equity returns data commence, and hasn’t explored beyond this one possibility.

Figure 15 & 16  – Five Year Real GDP and Equity Returns for Australia, UK, and USA

Source: Delta Research & Advisory

What does all of this mean?

It is important to point out that the analysis in this paper is simplistic insofar that it only looks at quarterly versus quarterly, annual versus annual, 5 yearly versus 5 yearly and considers quarterly and annual lags in equity returns to see if the sharemarket is likely to be a leading indicator. There are many other timeframe permutations that could be tested and the analyses presented in this paper provides no indication as to the true impact of economic news or sharemarket news or the ability to predict stronger or weaker economic outcomes will have on equity market return predictions.

Most importantly, there does appear to be a relationship between Real GDP Growth and Equity returns. There is, however, a big question mark as to what that relationship is because what has been shown in this paper is that the relationship differs between country and across different timeframes and lags.

The sharemarket might be a leading indicator for future economic growth and the ability to predict economic future growth might help predict future equity market returns. But, there are still very large risks of failure involved. At the risk of stating the obvious, there are many other factors to consider, and whilst this paper doesn’t address or compare, valuation metrics are still likely to be a critical factor in the future equity return expectation. That said, whilst the ability to predict future economic growth might not always help in predicting equity market success, it still may provide an edge from time to time.

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Nov 01

Melbourne Cup … Macquarie Quants take it up a notch

After a few tough years the Macquarie quant team have decided to upgrade their quantitative models. Looks like they’ve gone to a lot of effort so if they get it wrong again it might be quite fascinating to see how they respond. Anyway, for those who are interested … please click here for their quant-based tips.

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Oct 29

Global Economic Data…interactive chart

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Aug 17

Look for the Signal amongst the Noise


When disappointing performance occurs, alarm bells will typically ring in the minds of investors, advisers, asset consultants and perhaps the managers themselves. Investing has only ever been a long game but thanks to the internet, the 24-hour news cycle, social media, etc. etc., it appears that success is expected to occur quickly and this is the case with investing too. Investors have such an enormous selection menu and if one strategy doesn’t appear to be working out then it’s not difficult to find another … and this has been exacerbated with the reduction of transaction costs.

Investment performance has 2 major problems:

  1. Analysis timeframes are too short
  2. Only performance (or benchmark relative performance) is observed

Pure investment performance (or benchmark-relative performance) on a day by day, month by month and even year by year basis is mostly noise and therefore a somewhat redundant analysis exercise. Different styles, risks, industries, sectors, and markets, constantly go in and out of favour and the analysis of performance over these short timeframes is typically a waste of time that can lead to high transaction costs and potentially even worse performance.

The purpose of this article is a simple one. It takes a back-to-basics approach of performance analysis and demonstrates, using a simple case study, one approach of how to cut through the noise to find the signal.

The signal defines what is really happening. The signal is the bigger picture investment view that enables us to undertake better investment analysis, and therefore construct better investment portfolios.

The Short of it

The goal of our case study is to analyse two actively managed Australian equity strategies, renamed to Blue Fund and Orange Fund, to determine their respective suitability for an investment portfolio.

Some quick simple statistics of each fund to begin with…average monthly return and volatility (i.e. standard deviation) over the same time period used in Chart 1 shows the best performer is the Blue Fund, followed by the benchmark, and then the Orange Fund. Low volatility is typically preferred and despite performance volatility has the same order, somewhat counter to the old adage that higher return requires higher risk.

Based on this simple return analysis many would already say that the Blue fund is the superior fund based on the higher level of return per unit of volatility (similar to Sharpe Ratio). However, it is essential to know that Table 1 also suggests that the average monthly returns are not statistically different between both funds and MSCI Australia GR due to the high levels of volatility.

Table 1

Blue Orange - Table 1

Source: Delta Research & Advisory; MSCI

This statistical lack of difference between returns is supported by Chart 1 which shows the monthly excess returns of each strategy compared to the MSCI Australia GR index. There is no pattern whatsoever and no one data point is likely t be predictable of the future. Sometimes the Blue fund outperforms the Orange fund and vice versa, and they both outperform the index at different times and vice versa. Thanks to the short time period used for this analysis, i.e. Monthly, this chart provides little to nothing and is a very good demonstration of noise with no obvious sign of a signal.

Chart 1 – Monthly Excess Returns

Blue Orange - Chart 1

Source: Delta Research & Advisory

A Little Longer

The next step in this analysis progression is to increase the period of analysis for each time series data point from monthly to 6 monthly rolling and then 3 year rolling periods. Six months is a common time period used between client portfolio reviews and Chart 2 shows substantial volatility of these 6 monthly excess returns and therefore reasonable evidence alone that any one six-monthly period should not be used to make an investment decision based on performance…and I’m sure that never happens. Six monthly rolling average appears to show no signal for these two funds individually, so the search for a meaningful signal continues.

Chart 2 – 6 Monthly Rolling Average (Annualised) – Excess Returns

Blue Orange - Table 2Source: Delta Research & Advisory

On the other hand, potentially there is a signal from Chart 2 which includes timing differences between the Orange and Blue fund’s excess returns. That is, there does appear to be signs where their respective excess returns are moving in opposite directions, plus there are potential signs of the Orange Fund showing more extreme levels of outperformance or underperformance … however, at this point in the analysis these extreme levels should probably be taken with a grain of salt as it has only happened a few times … may be just good or bad luck???

So, moving from monthly returns to rolling 6 monthly rolling returns (Annualised), has yielded some potential insights but nothing conclusive.

Chart 3, takes the moving average of excess returns out to 3 years and the signals are getting a little stronger. The Orange fund has been a consistent underperformer on rolling 3 year periods since the middle of this time frame.

Anecdotally, 3 years is a popular timeframe for analysis and appears to be the amount of time investors and advisers are prepared to sustain underperformance of any one strategy before changing. The fact the Orange fund has had sustained underperformance over much longer than 3 years suggests that many investors or advisers may have excluded this fund from their consideration set or removed it from portfolios altogether.

However, the Blue Fund is looking very impressive, given very consistent rolling 3-year outperformance through almost the total timeframe so is looking to be the better strategy … or is it?

Chart 3 – 3 Year Rolling Average (Annualised) – Excess Return

Blue Orange - Chart 3

Source: Delta Research & Advisory

Moving on from Pure Performance – Adjusting for Market Risk

Risk-adjusted performance is frequently performed but rightly or wrongly it is rarely done on rolling timeframes and is usually performed across a single chosen time period. Unfortunately a single time period reduces a significant amount of information about investment performance behaviour so the risk-adjusted analysis performed here continues to to be time series based.

The Capital Asset Pricing Model

has a relatively poor reputation for predicting future returns. However, it is an excellent method for calculating exposure to the market (i.e. Beta) and risk-adjusted added value (i.e. Alpha). As we know, all active strategies aim to prove themselves with positive Alpha, which often doubles as the measure that defines “skill”. Using Capital Asset Pricing Model, performance analysis of both funds yields a market-risk-adjusted Alpha over rolling 3 year periods in Chart 4.

Chart 4 shows very different behaviours and potential signals for each fund. Firstly, their respective trends are in relative opposite directions, potentially suggesting opposite style.

The Blue fund has become negative in the earlier years, which wasn’t the case when analysing excess returns so there some currently unknown reasons for its market outperformance, as shown in Chart 3.

The Orange fund continues to show relatively poor results in the latter half of the timeframe, so its still looking like using this analysis many investors may have sold out of the Orange fund given this apparent weak performance.

Chart 4 – 3 Year Rolling Average (Annualised) – CAPM Alpha

Blue Orange - Chart 4

Source: Delta Research & Advisory

Adjusting for Style Risks

The final risk-based performance analysis takes the Capital Asset Pricing Model a step further and introduces other systematic risks into the mathematical model…this model is based on the multi-factor model sometimes called the Arbitrage Pricing Model.

Possibly the most well-known of the multi-factor models is the Fama-French 3 Factor model which adds two risk factors, value and size, to the single risk factor Capital Asset Pricing Model. For the purposes of this analysis, there are 3 additional risk factors which combine MSCI defined indices; Value minus Growth, Small Cap minus Large Cap, and a Momentum risk premium to MSCI Australia benchmark.

Chart 5 shows the Alpha that remains after adjusting for all four risk factors and we can see a dramatic change in result for the Orange Fund. It now has positive Alpha for almost all of the time period analysed, whereas the Blue Fund has a multi-year period of negative Alpha after adjusting for these multiple systematic risks. So whilst the Orange Fund produced underperformance compared to the market in the latter half (refer Chart 3 and/or 4) of this time period, after adjusting for common equity market risk factors, it’s added value (Alpha) is positive. This positive Alpha is what we would want to see from an active manager; positive value-add over and above potentially cheap and replicable systematic risks.

Chart 5 – 3 Year Rolling Average (Annualised) – Multi-Factor Alpha

Blue Orange - Chart 5

Source: Delta Research & Advisory

How did this happen?

So introducing non-market systematic risks improved the Alpha of the Orange Fund and slightly reduced the Alpha of the Blue Fund…how did this happen? The answer is a logical one…one or more of the systematic risks had a significant contribution to the performance of the Orange Fund…and the answer is shown in Chart 6. These two funds have clear distinct styles…the Blue Fund is clearly a Value-style (which MSCI define as holding low PE, low Price/Book, and/or High Dividend securities) and the Orange Fund clearly has a Growth style (which MSCI define as holding stocks with high earnings growth, revenue growth, and internal growth).

So whilst a fund manager may define how they invest with respect to style, deeper performance analysis can show whether that fund does what it says (i.e. is true to label), or can uncover what other risk exposures may be driving good or bad performance. It is worth noting that some qualitative research reports do not specifically define the Orange Fund as a “Growth” fund but given the track record, and the definitions of Growth, it is difficult to argue with the performance analysis.

Chart 6 – 3 Year Rolling Average – Exposure to Value/Growth FactorBlue Orange - Chart 6

Source: Delta Research & Advisory


The process outlined above will differ from strategy to strategy and will depend on what is important with respect to the role of a potential strategy within a broader portfolio. However, hopefully what this process shows is the significant benefit in undertaking performance analysis that looks through the short term noise that all strategies incur and look for signals using longer timeframes.

To find the signal for constructing better investment portfolios also requires digging deeper into a strategy’s return series. It may involve adjusting performance for multiple risks, such as the market or styles (Value, Growth, Size, Momentum, Quality, Credit, Duration, etc.) whilst still increasing the time period of analysis. Moving away from quarterly or annual performance measures that dominate the industry and survey data is essential. Moving away from analysing performance over short time periods is a positive move that in aggregate will result in superior portfolio performance from lower transaction costs.

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Jun 20

Market Inflation Expectations…lower than RBA

The above chart shows the yields for Australian Government Bonds, both nominal bonds and indexed bonds, as at the end of last week (although you can adjust the pricing date to any trading day of 2016). A simple way to determine the market’s inflation expectations over different timeframes is to simply subtract the difference. If you look hover the mouse over each line chart around the maturity date of circa 2020, the difference in yields is in the vicinity of 1.2% to 1.3% … low inflation expectations indeed and certainly a lot lower than the RBA target of 2% to 3%.

So if the RBA achieves its target, then these Australian Government indexed bonds will prove to be pretty reasonable performers … or at least relative to their nominal counterparts. I do know that most of the financial planning industry are still using 2.5% as their inflation target, or at least between 2% and 3% … clearly the market is currently thinking this is way too high and lower inflation than most of us expect should be expected.

PS … it also means the RBA cash rate is very likely to decrease.

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Jun 19

Major Central Bank Interest Rates … interactive chart

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May 30

The influence of equities on multi-asset strategies…both less and more than you think


Over recent years many commentators and experts have spoken of the significant risks superannuation funds are carrying with respect exposure to Australian equities. Most notable were comments a few years ago from David Murray, former Chairman of the Future Fund, and Ken Henry, former Federal Treasurer, who both said they had concerns that Australian superannuation funds were overweight Australian equities. Probably the comment I heard the most, coming from many investment professionals, was along the lines of, “balanced funds have around 60% to 70% in equities but this accounted for more than 90% of the portfolio risk”. So I thought I’d finally get around to checking out how true this statement is and if the industry as a whole has changed much over the last few years with respect to the influence of equities and their multi-asset portfolios.


If you trust my analysis and aren’t deeply familiar or interested in quantitative methods, then save yourself some sleepy time and skip to the results. Otherwise …

There are two primary analyses undertaken, and both involve regression analysis. Instead of analysing individual diversified strategies, I have chosen the following peer group indices as they pretty much capture the market as best as I can find …

  • Morningstar Australia OE Multisector Conservative (0-20% Growth Assets)
  • Morningstar Australia OE Multisector Moderate (20%-40% Growth Assets)
  • Morningstar Australia OE Multisector Balanced (40%-60% Growth Assets)
  • Morningstar Australia OE Multisector Growth (60%-80% Growth Assets)
  • Morningstar Australia OE Multisector High Growth (80%-100% Growth Assets)

These indices are also chosen as they pre-tax, thereby, producing an apples and apples comparison with the benchmarks which are also pre-tax.

The following benchmarks represent Australian equities, Global equities, and the risk-free rate …

  • MSCI Australia GR AUD
  • MSCI World GR AUD
  • Bloomberg AusBond Bank 0+Y TR AUD

The following models were used to assess contribution to portfolio risk by equities and the portfolio exposure…

  1. Rp-Rf = α + β1(Ra-Rf) + ε
  2. Rp-Rf = α + β1(Ra-Rf) + β2(Rw-Ra) + ε

Model 1 is used to calculate exposure and contribution to total portfolio risk by Australian equities and Model 2 is used to calculate the exposure and total contribution to risk by both Australian equities and global equites.

These regression models are applied to monthly returns between 31/12/1993 and 30/4/2016…which is a long time!

The variables from the models are:

  • Rp is the monthly return of peer group index
  • Rf is the Risk-free rate
  • α is the Alpha of the model (or beta-adjusted excess return)
  • β1 is the calculated exposure to Australian equities
  • Ra is the monthly return of Australian equities
  • β2 is the calculated exposure to the excess return of Global equities minus Australian equities
  • Rw is the monthly return of Global equities
  • ε is the residual error of the model

The R-squared value of each regression equation is calculated to determine the portfolio risk that can be explained by each model and is therefore used as a proxy for “risk contribution”. The R-squared of a regression model is also known as the “goodness of fit” and its calculation (without going into too much detail) is = “Explained Variation”/”Total Variation”.


Chart 1 – Australian Equity Beta

The above chart shows the Beta, or exposure, of Australian equities to each of the peer group indices. As expected, the higher the allocation to growth assets, the higher the exposure to Australian equities market. Interestingly, since late 2011 it appears the Australian equities beta has declined suggesting a lower exposure. Given the maximum growth assets for each peer groups isn’t much higher than the Australian equities beta for each peer group, you could interpret that Australian equities is the dominant asset class, and maybe it is. But, it may also suggest it is evidence of the relative high correlation between Australian equities and other growth asset classes.

Either way, these results are consistent with expectations and may somewhat support concerns around higher Australian equities allocations given their exposures or sensitivity appears to be a high proportion, but it also proof that this sensitivity to Australian equities has been in decline over the last few years or so.

Chart 2 – Australian Equity Contribution to Portfolio Risk

Now whilst the exposure to Australian equities appears fairly consistent with expectations; its total contribution to portfolio risk is a different story. In essence for the most part over the last 23 years (this chart starts the end of 1996 and is rolling 3 years so really starts in 1993); the Australian sharemarket contributes to a majority of risk across all multi-asset class peer groups and is therefore a very very important part of the portfolio.

Over the last few years, which is the very last point on the far right of the chart, this percentage is in the vicinity of 63% to 83% across each peer group which is somewhat consistent with the concerns spoken of Balanced funds by various experts … that is, “60% allocation to growth assets but responsible for 90% of the risk”. However, even for conservative strategies where the allocation to growth assets is less than 20%, Australian equities contributed at least two-thirds of the total portfolio risk over the last 10 years.

Chart 3 – Australian + Global Equity Contribution to Portfolio Risk

Chart 3 shows the same risk contribution statistic as Chart 2, but this time it is for Model 2, which adds the global equity market. The increase in risk contribution from both equity markets is only marginal because we are adding only the excess return by global equities over Australian equities and the two markets are fairly positively correlated so the impact of the additional asset class is small.

The more interesting results from Chart 3 include, over the last 10 to 15 years Australian Equities and Global Equities account for…

  • More than 90% of the total portfolio risk across Balanced, Growth, and High Growth peers of multi-asset class strategies.
  • More than 80% of the total portfolio risk for Moderate peers of multi-asset class strategies
  • Between 60% and 80% of total portfolio risk for Conservative peers of multi-asset class strategies, despite no more than 20% allocated to growth asset classes!!!

So irrespective of the allocation to equities or the equities market beta, across all risk profiles, equities are clearly the dominant asset class in terms of contribution to total portfolio risk.

So What?

So when “experts” say that “equities account for 90% of total portfolio risk of a balanced fund” therefore implying there is too much exposure, it is not necessarily about too much exposure just the importance of equities. So what should investors do to reduce this reliance on equities? As we see above, only holding 20% maximum of growth assets like the conservative peer group, still produces a very high proportion of portfolio risk due to equities.

The answer is to include non-correlated assets…or in English, add assets to the investment portfolio that behave differently from equities and go up when equities go down. This reverts to Markowitz 101 and is the continued search for the holy grail of investing…including non-correlated assets to the portfolio that can reduce the risk without reducing the return expectation or increase the return without increasing the risk.

The obvious non-correlated asset over many years has been conservative highly rated bonds…which I believe was Ken Henry’s suggestion when looking to reduce the reliance on equities.  Adding conservative bonds to a portfolio did reduce the contribution to risk from equities (see Charts 2 and 3 above) but as we know, adding conservative bonds is unlikely to improve the return expectations from equities and by our industry’s definition, obviously changes the risk profile. A good example of reducing equity risk are lifecycle funds. These have a moving risk profile (i.e. decreasing through time) and they gradually increase a fund’s exposure to bonds throughout time. The effect of this is to reduce the size of the volatility to combat sequencing risk leading into and through retirement, but the volatility will still be most dependent on equities.

Other potential lowly correlated considerations are alternatives, like property, private equity, infrastructure, or perhaps hedge fund strategies. There is much debate about the value of some alternatives (asset consultants and fund managers in favour of Alternatives and some big institutions are throwing in the towel, i.e. CALPERS), and if you do believe alternatives are the diversification solution, significant care must be taken to truly understand what is driving the underlying risk of these strategies. Particularly because equity markets may still be a very influential driving factor!!! Either way, if alternatives  do reduce the reliance on equities in portfolio risk equation, they do so by introducing other risks … which is not necessarily a bad thing but may be. So the challenge then becomes about assessing whether those risks are adequately compensated.

Final Thoughts

The investor faces very challenging times. Interest rates both here and around the world are so low, that retired millionaires are at significant risk of running out of money. To produce higher returns still requires the acceptance of higher risks but escaping equity market risk is not at all easily achieved without significant sacrifices in costs, liquidity, or chancing the unknown. So no matter what the investor’s investment strategy or risk profile, there will most likely be a strong reliance on equities driving their success. So please note, the communication of this bigger picture concept will always be more important than the marginal advantages gained or lost from manager selection, dynamic asset allocation, security selection or whatever the latest trend is.

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