Risk Management Rules on Australian Equities

Risk Management Rules on Australian Equities

April 2, 2015 Research Insights, Tactical Asset Allocation Research, $ewa
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(Last Updated On: January 18, 2017)

A friend of the blog was inspired by our Robust Asset Allocation discussion, and conducted some backtests using our proposed risk management framework:

  • 50% simple moving average rule (MA)
  • 50% time series momentum rule (TSMOM)

For naming convenience, we call our 50/50 approach ROBUST.

Our friend conducted his analysis on the All Ordinaries Australian Stock Exchange Index (Aussie Index). The data analysis starts in 1984.

The conclusion from the analysis was as follows:

I am dubious as to the value of using these market timing signals…

We have no sacred cows; We simply seek the truth.

We decided to do our own investigation to confirm or deny the findings. At the outset, the sharpe ratios of our risk management concept were slightly worse than old-fashioned buy-and-hold.

Data Details

We look at the following return streams:

  • Aussie B&H = Buy-and-Hold Australia Composite index
  • Aussie MA = If last month return > Moving average (12 months), go long risky assets; Otherwise, go alternative assets (T-bills)
  • Aussie TSMOM = If excess return >0, go long risky assets; Otherwise, go alternative assets (T-bills). (*Excess return = total return over past 12 months less return of T-bill)
  • Aussie Robust = The average return of (Aussie MA + Aussie TSMOM)

We apply a 50bps transaction fee to the three timing strategies (MA, TSMOM, and ROBUST). All returns are price returns and do not include the reinvestment of distributions (e.g., dividends). Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Our sample period is from 9/1/1985 to 1/31/2015.

Summary Statistics:

On a CAGR basis, buy-and-hold is the winner. Also, on a risk-adjusted basis, as conveyed by Sharpe and Sortino ratios, B&H is a winner, but by a small margin. Overall, the results are not compelling for trend-based risk-management systems.

rolling maxdd sum
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

 

Of course, risk-management systems are in place for one reason: to manage risk. There are many ways to manage risk, but often this means lowering volatility, and more importantly, lowering maximum drawdowns and total drawdowns.

The chart above highlights that volatility is lowered and the sum of 5-year rolling drawdowns is much lower for the risk-managed strategies. Max drawdowns are also slightly lower, but not significantly so.

To investigate the risk-management aspects in greater detail, we took a closer look at 5-year rolling drawdowns and historical drawdowns.

5-year Rolling Max Drawdown

As we know, drawdown measures the peak-to-trough decline during a specific record period. 5-year rolling max drawdown measures the peak-to-trough decline during a specific 5-year window.

Why we care about rolling drawdowns?

Well, two strategies can have a similar max drawdown, but one strategy might incur more frequent drawdowns. If our focus in only on max drawdowns, we may miss an important element of the risk profile for a specific strategy.

Below we chart the 5-year rolling MaxDD for all four systems. The drawdown in the first period of the sample is not eliminated via the risk-management rules (akin to the 1987 crash in US equity), but we can see that MA and TSMOM significantly decrease drawdowns over time. Robust provides the best risk-management.

graph of 5-year rolling maxdd
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

Top Drawdowns Comparison

In the table below we outline the drawdowns in the B&H index and show the corresponding drawdowns associated with the risk-managed versions.

Aside from the 1987-1988 drawdown, risk-management clearly worked.

top 16 drawdowns table
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

Conclusion

At first glance, there is no evidence to suggest that risk-management rules work on the Australian equity market. However, after deeper investigation, there is some evidence to support the notion that risk-management rules do indeed “manage risk” associated with Australian equities.


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Definitions of common statistics used in our analysis are available here (towards the bottom)




About the Author

Wesley R. Gray, Ph.D.

After serving as a Captain in the United States Marine Corps, Dr. Gray earned a PhD, and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management that delivers affordable active exposures for tax-sensitive investors. Dr. Gray has published four books and a number of academic articles. Wes is a regular contributor to multiple industry outlets, to include the following: Wall Street Journal, Forbes, ETF.com, and the CFA Institute. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.


  • Jan Vrot

    I have often wondered how raa would work on Nikkei especially after the crash.

  • We’ve done a lot of work on various markets and assets across the globe. We’ll be posting that research when we get some time.

  • RT1C

    Further evidence that a weakness of TSMOM is that it doesn’t get you out fast enough to protect against crashes (of the 1987 variety).

  • Yep.
    When you find a trading rule that can prevent all drawdowns and all losses, please share. We’ll be billionaires!

  • RT1C

    Didn’t mean to imply that it was a fatal flaw; just a characteristic. I noticed the same thing looking at Gary Antonacci’s work. It seems that TSMOM works on rounding tops but not sudden sharp drops. It makes me wonder if it would benefit from a further tweak, perhaps an adaptive response. Not sure what that should be, nor have I done any work on it, but just for instance, I would assume that the shorter the lookback period, the more responsive TSTOM is in picking up crashes quickly. So maybe there is some adaptive model based on a comparison of short and long lookback times? Or would that just lead to excessive whipsawing? Probably would be hard to develop such a model without running into datamining/snooping problems.
    On the other hand, some forecasters were successful in anticipating major crashes based on macroeconomic factors. Maybe one could combine that with TSMOM to know when to use a short lookback period vs. long.

  • We’ve been down the rabbit hole of combing macro/valuation and trend into one big fancy enchilada. Actually traded the thing for almost a year…

    But you already anticipated the issue with these things–data-mining and robustness…

    I’m convinced market timing is 99% smoke and mirrors, but trend-following type rules may provide that 1% of differentiation that helps, at the margin.

    Tough business!

  • Steve

    This is a thoroughly interesting finding. As I’ve mentioned previously: nothing is going to save you from the “sharp drop” (unless you have a crystal ball). A trend following method by nature needs time to adapt to a changed market state.

    This table (of top drawdowns comparison) is amazing. The ’87 crash was – just that. A price shock, as I call them. They might only (hopefully!) happen no more than once, in an investment lifetime. The price shock aside (which nothing can protect against)…I think there does seem to be some case to be made for this type of risk management.

    Some decades you’ll regret it; because (in hindsight) there was no massive bear market. But that’s the point – that can only be done with hindsight.

    If, as an investor, we want to manage risk, it would seem that a trend following risk management will (a) reduce losses of bear markets without (b) reducing returns overall, too much. Perhaps such as to make it worthwhile.

    As a side note: price shocks then, are another (different) risk to consider. Something that can’t be addressed at all, without also asking, “how much money should I have in the market?”

  • Trip

    I trade a portion of my portfolio with the dual momentum strategy, so the problem of sudden price drops also concerns me.

    As you say, some kind of blended approach with shorter lookback periods (but not too short – mean reversion!) could help but then you’d introduce just more parameter salad. Don’t get me even started on also using macrofundamental factors.

    For the time being I’ve resigned myself to “protect” against surprise crashes by simply also using the good ol’ (trailing) stop loss. I know that introduces its own problems and is commonly advised against in the literature for good reasons: Set the stoploss too tight and you’ll get needlessly stopped out by random market noise too often. And if you set it too wide it loses its protective value and is useless. You could “snoop” out a golden sweet spot from historical data, but lacking that I just used a value I intuitively feel comfortable with (lets say around 10%)