Janus Mutual Funds With Adaptive Allocation Deliver Outstanding Returns

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Includes: JANFX, JANIX, JNHYX, JNOSX
by: Toma Hentea

Summary

A portfolio composed of three Janus equity funds, together with an intermediate term bond fund would deliver exceptionally high total returns with reasonable low volatility.

Adaptive allocation of the funds in the portfolio is performed by an optimization algorithm that seeks maximum return for a given volatility level.

Back testing simulation over 119 months' period from May 2005 to April 2015 demonstrates the strength of the strategy, achieving over 19% compound annual return, maximum drawdown below 16%.

This article applies portfolio rebalancing and adaptive allocation to a diverse set of three Janus equity funds, small, mid and large cap, growth and value, U.S. and foreign and an intermediate term bond fund. The investment strategy was presented in a previous article. For a portfolio made of a fixed set of assets, the strategy determines the allocations of the assets to achieve the largest possible returns for a target value of the volatility.

Four mutual funds have been selected for investment. They are the following:

  • Janus Triton (MUTF:JANIX)
  • Janus High Yield (MUTF:JNHYX)
  • Janus Overseas (MUTF:JNOSX)
  • Janus Flexible Bond (MUTF:JANFX)

The study will apply the investing strategies over the most recent 106 months, from May 2005 to April 2015. The reason we selected this particular time period is because the Janus Triton fund was created in February 2005. Since our investing strategies need an initial three-month period, the earliest time we can simulate starts in May 2005.

In table 1 we list the total return, the compound average growth rate (CAGR%), the maximum drawdown (maxDD%), the annual volatility (VOL%), the Sharpe ratio and the Sortino ratio of these individual funds.

Table 1. Performance of the Janus funds from May 2005 to April 2015:

 

TotReturn%

CAGR%

maxDD%

VOL%

Sharpe

Sortino

JANIX

255.43

13.39

-59.15

22.74

0.59

0.73

JNHYX

90.97

6.62

-27.43

6.01

1.10

1.06

JNOSX

57.82

4.62

-67.56

25.49

0.18

0.22

JANFX

75.00

5.70

-5.70

3.81

1.50

2.43

(NYSEARCA:SPY)

119.46

8.10

-55.18

20.29

0.40

0.48

As one can see from table 1, the overseas fund underperformed the market as represented by SPY producing a lower return with higher risk. The Triton fund, invested mostly in small cap growth stocks, over performed the market by producing much greater return with slightly higher volatility. The intermediate term bond fund produced lower return, but with much lower volatility and drawdown than SPY.

The objective of this research is to search for investment strategies that increase the returns while decreasing the risk as given by drawdown and volatility of the returns.

Here is a short review of the investment strategies used in this article. We consider the following three strategies:

(1) Fixed asset allocation. We created a four-fund portfolio initially invested 25% in each fund, without rebalancing.

(2) Target asset allocation with rebalancing. The portfolio is initially invested 25% in each fund and is rebalanced when the allocation to any fund deviates by 10% from its target.

(3) Adaptive allocation based on an optimization algorithm trying to maximize returns and minimize volatility of the returns. We shall use three different targets for volatility: low, medium, and high volatility targets.

The data for the study were downloaded from Yahoo Finance on the Historical Prices menu for all the tickers mentioned in tables 1 and 2. We use daily closing price data from February 2005 to April 2015, adjusted for dividend payments.

While I do not present any details of the implementation of the optimization algorithm I use, I do mention that implementations of the portfolio optimization algorithms are widely available in EXCEL, and many high level computer languages. Different variants of it were used in many Seeking Alpha articles by other contributors.

I apply the strategy using the daily closing prices adjusted for dividend payments. On the first day of each month we estimate the covariance matrix of the returns over the previous 65 trading days, roughly corresponding to the latest three-month period. Based on the means and volatilities of the returns, as well as the correlation between the asset returns, the algorithm determines the optimal allocation of the assets that would have produced the highest total return without exceeding a given level of volatility of the returns. This allocation is maintained until the first trading day of the next month when a new allocation is determined.

In Figure 1, we show the time allocation obtained for mid-target level of the volatility.

Figure 1. Portfolio allocation for a low volatility target.

Source: This chart is based on EXCEL calculations using the adjusted daily closing share prices of securities.

From figure 1, it is apparent that there are times when all equity is in bond funds (blue color), as well as times when all money is in stocks (green, violet and brown). As a matter of fact, during the 2008 crises almost all the money was invested in the bond fund .

In table 2 below, we present the simulation results for three asset allocation strategies: fixed weights, target weights with rebalancing, and adaptive allocation with three different levels of volatility. Here are some details about each line of the table:

Line #1 - fixed allocation without rebalancing; at the beginning of the investment period, each fund is allocated 25% of the capital.

Line #2 - target allocation with rebalancing at 10% deviation from the target. The target weights are all equal at 25%. Whenever the equity in any fund deviates by more than 10%, the portfolio is rebalanced to restore the initial 25% equal weights. As seen in column #4, there were 25 rebalancings of the portfolio within the 119 months of the study.

Line #3 to #5 - Each line gives the results of applying an adaptive allocation with a different volatility target. The portfolios may be rebalanced on the first trading day of each month.

Line #6 contains the benchmark results for a buy-and-hold of the SPY ETF.

Table 2. Performance of the different portfolio allocation strategies with funds from May 2005 to April 2015:

 

TotRet%

CAGR%

No.trades

maxDD%

Vol%

Sharpe

Sortino

Fixed allocation

119.81

8.11

0

-45.78

12.62

0.64

0.78

Target allocation

123.71

8.30

25

-42.24

11.98

0.63

0.84

Adapt. allocation LOW vol

149.39

9.72

103

-8.95

4.45

2.19

2.93

Adapt. allocation MID vol

328.26

15.92

98

-10.84

7.65

2.08

2.65

Adapt. allocation HIGH vol

465.72

19.25

87

-15.91

10.32

1.87

2.30

SPY

119.46

8.10

0

-55.18

20.29

0.40

0.48

The performance of the adaptive allocation strategy is quite remarkable. It produces both high returns and extremely low volatilities. It shows that one can easily trade volatility for returns.

In figure 1 we show the equity curves for fixed allocation, fixed target allocation and adaptive allocation with a low volatility constraint.

Figure 1. Equity curves for a fixed allocation portfolio with no rebalance (blue color), a portfolio with fixed targets and rebalancing (brown color) and a portfolio adaptively optimized with a low volatility constraint (green color).

Source: This chart is based on calculations using the adjusted daily closing share prices of securities.

From figure 1 it is apparent that the adaptive allocation produces higher returns over the ten-year interval with extremely low volatility.

Finally, Figure 2 illustrate the effect of volatility targets on the results of adaptive asset allocation.

Figure 2. Equity curves for the adaptive allocation strategy for a set of different volatility targets.

Source: This chart is based on calculations using the adjusted daily closing share prices of securities.

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

Disclosure: The author has no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

The author wrote this article themselves, and it expresses their own opinions. The author is not receiving compensation for it (other than from Seeking Alpha). The author has no business relationship with any company whose stock is mentioned in this article.