We Found 30 Timing Strategies that “Worked”—and 690 that Didn’t

By Wei Dai, PhD, Head of Investment Research   |   Audrey Dong, Researcher

We Found 30 Timing Strategies that “Worked”—and 690 that Didn’t
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Oct 31, 2023

2 Minute Read

Topic

Equity Investing Implementation Investment Principles

Finding one timing strategy that worked in a backtest is not a difficult task. In fact, in our latest study on timing the market, size, value, and profitability premiums, we found 30 timing strategies that delivered reliable outperformance relative to staying invested in the premiums. The best-performing strategy? It was designed to time the market premium by switching between the market and Treasury bills. Thanks to the decision to sit on Treasury bills during market downturns in 2001, 2008, and 2022, the strategy outperformed the buy-and-hold market portfolio by an annualized 5.5% from 2001 to 2022.

Before getting too excited, let’s take a closer look at how this best-performing timing strategy works. It uses the valuation ratio to time the market premium in the developed ex US markets. At the end of each calendar year, the strategy compares the current price-to-book ratio of the market with its historical distribution over the most recent rolling 10-year period. When the price-to-book ratio exceeds the top 20th percentile of its historical distribution, the strategy gets out of the market and invests in one-month Treasury bills. When the price-to-book ratio drops below the 50th percentile of its historical distribution, the strategy switches back to the market portfolio.

The bold text above represents strategy parameters that one can choose in a backtest. To achieve that impressive outperformance, how important is it to use this exact combination of parameters? Well, as it turns out, quite important. As Exhibit 1 shows, tweaking one single parameter of the strategy, such as the rebalance frequency or the breakpoint, would reduce the excess return by more than half, making it no longer reliable. Furthermore, the same timing strategy is not effective at timing the market premium in other regions or other premiums in the developed ex US markets.


Exhibit 1

Shiny Object

Sensitivity analysis of the best-performing timing strategy
Bar graph showing annualized excess return for the best-performing strategy and nine other strategies with changes to a single parameter, as described in the text above. Two values are shown: reliably different from zero, with probability value less than 0.05, and not reliably different from zero, with probability value greater than or equal to 0.05. Best-performing strategy is reliably different from zero, with probability value less than 0.05, and a return of 5.5 percent. All other strategies are not reliably different from zero, with probability value greater than or equal to 0.05. When rebalance frequency is changed from annual to monthly, return is 1.5 percent. When measurement window is changed from rolling to expanding, return is 2.9 percent. When breakpoint is changed from top 20^th^ percentile to top 50^th^ percentile, return is 2.1 percent. When region is changed to US, return is negative 3.8 percent. When region is changed to emerging markets, return is 1.0 percent. When premium is changed to size, return is negative 0.7 percent. When premium is changed to value, return is negative 2.5 percent. When premium is changed to profitability, return is negative 1.6 percent.

Past performance, including simulated performance, is no guarantee of future results, and there is always the risk that a client may lose money. Simulated strategy returns are based on model/backtested performance. The performance was achieved with the retroactive application of a model designed with the benefit of hindsight; it does not represent actual investment performance.

Surprised? Don’t be. If you ask a large enough number of people to repeatedly flip a coin, someone will flip 10 heads in a row. Similarly, we should expect some strategies to generate outstanding results just by chance if we try enough parameter combinations. Indeed, the best-performing strategy is only one out of 720 strategies we simulated. As shown in Exhibit 2, the vast majority of these timing strategies underperformed. And this is before considering any trading and tax costs.


Exhibit 2

The Big Picture

Annualized excess returns for 720 timing strategies
Histogram showing annualized excess returns for 720 timing strategies using three different approaches: valuation ratio, mean reversion, or momentum. Key identifies six different outcomes: valuation ratio as not reliably positive, mean reversion as not reliably positive, momentum as not reliably positive, valuation ratio as reliably positive, mean reversion as reliably positive, and momentum as reliably positive. Y-axis shows percent, ranging from negative 8 percent to 8 percent. Results are as described in the text—only 30 timing strategies delivered reliable outperformance (with a range of just above zero to 5.5 percent) relative to staying invested in the premiums. The rest underperformed, with a range of zero to negative 8 percent. The average excess return for all strategies was negative 1.1 percent per year.

Past performance, including simulated performance, is no guarantee of future results, and there is always the risk that a client may lose money. Simulated strategy returns are based on model/backtested performance. The performance was achieved with the retroactive application of a model designed with the benefit of hindsight; it does not represent actual investment performance.

It is tempting to focus on a few shiny objects in backtests. But when we look at the big picture, it is clear the odds of successfully timing the equity premiums aren’t good. The underperformance of most timing strategies also serves as a reminder of the potentially high opportunity cost of mistiming the premiums. Hence, to improve the odds of capturing the premiums over the long run, it is time to stop timing the premiums.

METHODOLOGY APPENDIX

The timing strategies switch between the long and short sides of each premium in an attempt to outperform strategies that stay invested in the long side of the premium. Investing in the long side of the market, size, value, or profitability premiums means investing in the market, small caps, value stocks, or high profitability stocks, respectively. Investing in the short side of the market, size, value, or profitability premiums means investing in Treasury bills, large caps, growth stocks, or low profitability stocks, respectively. The following indices are used to proxy the long and short sides of each premium:

• Market premium: Fama/French market index for each region vs. the one-month US Treasury bill.

• Size premium: The return difference between the Dimensional small cap index and the Fama/French large cap index for each region.

• Value premium: The return difference between the Fama/French value index and the Fama/French growth index in each region.

• Profitability premium: The return difference between the Fama/French high-profitability index and the Fama/French low-profitability index for each region.

Index descriptions can be found here. The sample period for US premiums starts in July 1927, except for profitability, which starts in July 1963. The developed ex US sample starts in July 1990, and the emerging markets sample starts in July 1994. All data are monthly through December 2022.

To define the timing strategies, we use the following parameters: timing approach (valuation ratio, mean reversion, or momentum), region (US, developed ex US, or emerging markets), premium (market, size, value, and profitability), measurement window for calculating the timing signal’s historical distribution (expanding window or rolling window), breakpoint at which to invest in the short side of the premium (10th, 20th, or 50th percentile of the timing signal’s historical distribution), switchback at which to switch back to the long side of the premium (same as the breakpoint or 50th percentile of the timing signal’s historical distribution), and rebalance frequency (monthly or annual). These parameter combinations amount to 720 timing strategies (3 timing approaches × 3 regions × 4 premiums × 2 measurement windows × 5 breakpoint and switchback pairs × 2 rebalance frequencies).

The detailed description of the parameters and timing strategies can be found in “Another Look at Timing the Equity Premiums” (research paper, Dimensional Fund Advisors, October 2023). 

Source: Dimensional calculations using data from CRSP, Compustat, and Bloomberg.

The Dimensional and Fama/French Indices represent academic concepts that may be used in portfolio construction and are not available for direct investment or for use as a benchmark. Index returns are not representative of actual portfolios and do not reflect costs and fees associated with an actual investment. The Dimensional Indices have been retrospectively calculated by Dimensional Fund Advisors LP and did not exist prior to their index inceptions dates. Results shown during periods prior to each index's index inception date do not represent actual returns of the respective index. Other periods selected may have different results, including losses. Backtested index performance is hypothetical and is provided for informational purposes only to indicate historical performance had the index been calculated over the relevant time periods. Backtested performance results assume the reinvestment of dividends and capital gains.


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