Portfolio Allocation Optimization With Adaptive Risk Protection

| About: SPDR S&P (MDY)

Summary

Momentum allocation strategies did not perform well in 2015.

Fixed allocation strategies performed better than momentum strategies in 2015, but worse in 2008.

An adaptive risk protection strategy performed better than fixed strategies in 2008 and better than momentum strategies in 2015.

Portfolio rebalancing and momentum asset allocation are two strategies commonly used for enhancing the return of portfolios built on equities and bonds. Unfortunately, those strategies do not offer flexibility regarding the level of risk. On the other hand, the Mean-Variance Optimization strategy can be applied at any risk level so that it matches the average investor's risk tolerance. With that in mind, we can design strategies that achieve low, medium or high risk levels.

In this article, we propose the application of an adaptive risk protection strategy. Its objective is to combine the allocations obtained for low, medium and high risk, such that the resulting allocation has a volatility of returns below a given level as specified by the investor's risk tolerance.

We are showing these results by backtesting a portfolio of bonds and equities.

The portfolio is made up of the following two ETFs: the SPDR S&P MidCap 400 ETF (NYSEARCA:MDY) and the iShares 20+ Year Treasury Bond ETF (NYSEARCA:TLT).

Basic information about the funds was extracted from Yahoo Finance and MarketWatch and is shown in Table 1 below.

Table 1

Symbol

Inception Date

Net Assets

Yield%

Category

MDY

5/4/1995

18.8B

1.31%

Mid-Cap Blend

TLT

7/22/2002

5.25B

2.60%

Long Term Treasury Bond

The data for the study were downloaded from Yahoo Finance on the Historical Prices menu for MDY and TLT. We use the daily price data adjusted for dividend payments.

For the adaptive allocation strategy, the portfolio is managed as dictated by the Mean-Variance Optimization algorithm developed based on Modern Portfolio Theory (Markowitz). The allocation is rebalanced monthly at market closing of the first trading day of the month. The optimization algorithm seeks to maximize the return under a constraint on the portfolio risk determined as the standard deviation of daily returns.

The portfolios are optimized for three levels of risk: Low, Mid and High. As a benchmark for comparison, we added the performance of an equal weight strategy with rebalancing at 20% deviation from the target. The rebalancing is done whenever the allocation of an asset reaches 40% or 60%. This strategy rebalanced the portfolio 5 times during the 14 years of backtesting from January 2003 to December 2016.

In Table 2, we show the performance of the strategy applied monthly from January 2003 to January 2017.

Table 2. Performance of MVO algorithms applied monthly versus equal weight portfolios

TotRet%

CAGR%

Volatility%

maxDD%

Sharpe

Sortino

Low risk

289.57

10.2

8.85

-17.51

1.15

1.63

Mid risk

482.45

13.41

12.71

-21.43

1.05

1.52

High risk

811.17

17.1

15.77

-26.93

0.87

1.56

Adaptive risk

372.56

11.62

9.27

-16.88

1.25

1.76

Fixed equal weight

246.57

9.28

10.67

-24.75

0.87

1.18

Equal weight rebalanced

271.45

9.83

10.17

-23.26

0.97

1.32

From Table 2 we see that the adaptive risk strategy has highest Sharpe and Sortino ratios, so its risk-adjusted return is higher than the adjusted returns at any risk level. Still, its absolute return is lower than the absolute returns of the Mid and High risk strategies; it is somewhere in between those of Low and Mid risk.

To look deeper into the behavior of various strategies we present the annual returns in Table 3 below.

Table 3: Annual returns of all strategies

Low risk

Mid risk

High risk

Adaptive risk

Equal weight

Equal weight rebalanced

2003

15.05%

26.25%

32.36%

14.78%

26.63%

26.63%

2004

10.52%

7.78%

6.64%

11.16%

7.14%

7.14%

2005

9.90%

4.55%

4.42%

8.83%

13.27%

12.10%

2006

6.93%

13.72%

14.21%

8.30%

8.37%

8.10%

2007

11.96%

14.70%

14.83%

11.69%

1.23%

2.92%

2008

13.90%

26.77%

29.42%

14.11%

-14.23%

-10.55%

2009

-6.52%

-3.80%

31.25%

8.93%

18.98%

19.56%

2010

20.44%

23.63%

28.25%

20.81%

18.45%

17.25%

2011

21.87%

36.05%

40.66%

25.18%

10.19%

16.85%

2012

8.65%

2.83%

3.65%

7.02%

11.84%

11.60%

2013

6.51%

26.90%

33.06%

9.94%

12.41%

9.73%

2014

18.68%

15.64%

15.57%

19.19%

15.62%

16.75%

2015

-2.89%

-7.00%

-8.03%

-2.78%

-1.79%

-1.25%

2016

9.40%

8.81%

4.66%

9.55%

9.20%

7.16%

As can be seen from Table 3, the High risk strategy had either the highest or the lowest return of all the strategies in all 14 years. It had the highest return in 8 years and the lowest return in 6 years. Not surprisingly, it was the worst-performing strategy over the latest three years. It is well known by now that from 2014 up to now, the momentum strategies underperformed.

It is for this reason that I looked into enhancing the momentum strategies. One possibility is to adapt the level of risk to the volatility of the markets. The adaptive risk protection can be achieved by weight-averaging the allocations of various risk-level strategies.

Overall, the adaptive risk strategy was the top-performing in the last three years, being the top in 2014 and 2016 and close to the top in 2015.

To help compare the performance of the strategies, we show the evolution of their equities in the three graphs that follow.

The equity curves for all adaptive strategies with fixed risk levels are shown in Figure 1.

Figure 1. Equity curves of the portfolios with MVO monthly optimization at fixed risk levels.

(Source: All charts in this article are based on calculations using the adjusted daily closing share prices of securities.)

Looking at Figure 1, it is obvious that the High and Mid risk strategies performed poorly in 2015-16. The Low risk strategy would have been a better choice.

Figure 2. Equity curves of the adaptive risk strategy and portfolios with MVO monthly optimization at Low and Mid fixed risk levels.

Finally, in Figure 3, we show side by side the equity curve for the adaptive risk and the fixed equal weight rebalanced when the allocation deviates by more than 20%.

Figure 3. Equity curves of the adaptive risk strategy and the fixed equal weight rebalanced at 20% deviation from the target.

Finally, in Table 4, we show the current allocations for all adaptive strategies.

Table 4. Current allocations for January 2017

MDY

TLT

Low risk

56%

44%

Mid risk

100%

0%

High risk

100%

0%

Adaptive risk

69%

31%

Conclusion

From the data presented above, we can see that the adaptive risk strategy allows the investor to enhance the performance of a conservative strategy such as the Low risk strategy by adding some risk when the markets are in a strong uptrend. The strategy reduces the risk level when the markets become more volatile.

Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Additional disclosure: The article was written for educational purposes and should not be considered as specific investment advice.

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