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Intelligent Asset Management: Our case for active tactical rotation

·3 min read
Intelligent Asset Management: Our case for active tactical rotation

HOBOKEN, N.J., March 18, 2021 /PRNewswire/ -- DropShot Capital Management relies on artificial intelligence for its decision making, rebalancing its portfolio by adaptively weighting a universe of cross asset ETFs. However, a fundamental question would be to ask why!? Conventional asset management favors buy and hold portfolios that range in strategy from fully static weighting, to using optimizers to build portfolios based on risk adjusted returns or other criteria. These strategies aim to rebalance at a low frequency primarily to avoid transaction costs and additional tax burdens that come with active management, but also have the potential to miss large opportunities to capture returns as market conditions change. We wish to answer the question of whether trading actively pays off for the investor relative to passive strategies.

While static allocation may perform well in many cases, the case for active management particularly in cross asset allocations remains very strong. Being an entirely data driven firm, we have decided to let the data speak for itself. To do this we simulate managing a liquid portfolio from 3/2008 to 3/2021 with various levels of intervention on the part of the manager. Our universe is made up of the following ETFs that represent many of the asset exposures investors can take:
[ SPY , IEF , HYG , EFA , EEM , GLD , IWM , DBC ]

To simulate passive management of the portfolio we consider the following strategies:

  1. Equally Weighted Asset Allocation

  2. Yearly Risk Parity Optimization

  3. 60/40 ratio of SPY / IEF

On the active management the strategies considered are:

  1. 15-day Momentum rebalancing - Favor the assets in the universe that have returned the most over the last 60 days.

  2. Daily Reversion rebalancing - Favor the assets that have returned the least since yesterday at the end of each trading day.

  3. Machine Learning - Use machine learning models to generate predictive signals for the assets and weight according to those at the close each day.

Each of these strategies is run in two modes, one in which all assets are allocated to and one in which the signals are allowed to concentrate down to the top 3 preferred asset classes.

The results are shown below:

Assets Traded:

SPDR S&P 500 ETF Trust (SPY)

iShares 7-10 Year Treasury Bond ETF (IEF)

iShares iBoxx $ High Yield Corporate Bond ETF (HYG)

iShares MSCI EAFE ETF (EFA)

Shares MSCI Emerging Markets ETF (EEM)

SPDR Gold Shares (GLD)

iShares Russell 2000 ETF (IWM)

Invesco DB Commodity Index Tracking Fund (DBC)


Trading Date End: 2021-03-14

Trading Date Start: 2008-03-01

Strategy Type

Strategy

Returns

Sharpe
Ratio

Max
DD

Sortino
Ratio

Passive

Equally Weighted Asset Allocation

108%

0.46

-0.39

0.64

Passive

Yearly Risk Parity Asset Allocation

92%

0.51

-0.33

0.71

Passive

60:40 Ratio of SPY/IEF

205%

0.79

-0.31

1.14

Active

15d Momentum Rebalancing

97%

0.47

-0.36

0.65

Active

15d Momentum - Concentrated to top 3

111%

0.49

-0.37

0.67

Active

Daily Reversion Rebalancing

314%

0.75

-0.35

1.07

Active

Daily Reversion - Concentrated to top 3

638%

0.91

-0.36

1.31

Active

Daily Machine Learning - All Assets

381%

0.85

-0.34

1.25

Active

Daily ML - Concentrated to top 3

835%

1.09

-0.29

1.69


The opinions expressed in this article are for general informational purposes only and are only intended to provide specific advice or recommendations for any individual or on any specific security or investment product. It is only intended to provide education about the financial industry.

Active management and tactical rotation can significantly improve returns even when relatively simple strategies are used. DropShot Capital continues to pioneer AI driven strategies to meet these and other opportunities head on! Happy Investing!

Contact:
Chris Kramvis
Chris@dropshotcapital.com

SOURCE Dropshot Capital