TuringTrader's Stocks on the Loose

Key Facts

  • aggressive momentum strategy w/ risk-parity position-sizing
  • trades individual stocks and holds bond ETFs to reduce volatility
  • rebalances weekly

Similar Strategies

Stocks on the Loose is a proprietary premium strategy by TuringTrader.com. We first introduced the portfolio in early 2020, based on concepts reaching back to 2015. In March 2021, we updated the strategy for higher returns and lower volatility, and replaced the risk-off strategy in March 2022.

Stocks on the Loose aims to beat the S&P 500 at lower volatility and with lower maximum drawdowns. The strategy trades stocks with a momentum approach but gradually rotates into a managed bond portfolio when stock market conditions seem unfavorable to achieve the stated objective.

With its weekly rebalancing schedule, Stocks on the Loose has moderate maintenance requirements.

Strategy Rules

The operation of Stocks on the Loose can be summarized as follows:

  • trade all S&P 100 stocks, plus U.S. treasuries as a risk-off asset
  • rebalance once per week (on Wednesdays)
  • disqualify stocks trading below their 100-day moving average
  • disqualify stocks that made any single-day moves exceeding 10% in the past 90 days
  • rank stocks by their volatility-adjusted momentum, calculated as the product of slope and R2 of a 90-day logarithmic regression
  • only open new positions, if TuringTrader's Market Vane indicates bullish conditions
  • use fixed-fractional position sizing, based on the 20-day average trading range
  • ensure healthy position sizes by limiting total portfolio risk, capping the maximum allocation to a single stock, and scaling back exposure with increasing market volatility
  • invest any unused capital in TuringTrader's Buoy portfolio

Most of these rules are taken verbatim from Clenow's Stocks on the Move strategy. We recommend reading Clenow's book to understand the strategy's rationale better.

Expanding upon Clenow's work, we improved the money management to remedy the following issues: concentration in too few assets, taking excessive total portfolio risk, and holding idle cash. These proprietary changes lead to a much more even-keeled behavior and significantly higher returns.

Diversification

Stocks on the Loose typically allocates 70% to 100% of its capital towards a set of 3 to 5 stocks. In more turbulent times, the strategy rotates into a managed bond portfolio.

With this allocation, the portfolio bears significant concentration risk, in addition to the market risk. Nonetheless, the portfolio's beta is around 0.3, thanks to the strategy's timely active management.

Returns & Volatility

Over the entire economic cycle, Stocks on the Loose outperforms the S&P 500 by a wide margin. Further, the portfolio beats the S&P 500 in many years, even outside of recessions.

This behavior results in more predictable returns and lower drawdowns. The Monte-Carlo simulation confirms these claims.

Account & Tax Considerations

Stocks on the Loose trades frequently and regularly triggers taxable events. Investors should expect almost all capital gains to be short-term. Therefore, the strategy works best in tax-deferred accounts.

Because the strategy holds up to 6 high-flying and potentially expensive stocks simultaneously, it requires an account holding no less than $30,000 to function as intended.

Portfolio Revisions

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Performance

This table shows the portfolio's key performance metrics over the course of the simulation:



The following chart shows the portfolio's historical performance and drawdowns, compared to their benchmark, throughout the simulation:


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This chart shows the portfolio's annual returns:



The following charts show the Monte-Carlo simulation of returns and drawdowns, the portfolios 12-months rolling returns, and how the portfolio is tracking to its benchmark:


Asset Allocation

The portfolio last required rebalancing after the exchanges closed on . Due to fluctuations in asset prices, the exact allocations vary daily, even when no rebalancing occurred. The current asset allocation is as follows:


SymbolNameAllocation
 
 

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