Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for June 2024 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for June 2024 (Final)
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What Works Best?

Of all the active investing/trading strategies investigated by research summarized in the blog, which ones that individual investors can practically implement work best? With reservations (because of all the uncertainties, statistical biases/criticisms and contradictory evidence to which the research is subject), here are some best guesses:

First, some precepts ranging from obvious to arguable:

  • Asset prices repeatedly cross above and below their long-term trends.
  • Driven by long-term (many centuries) real economic expansion and inflation, these long-term trends are generally up.
  • Departures from trend derive from tendencies of financial markets, which are social systems, to overreact or underreact systematically to new information.
    • Confirmation bias helps sustain misreactions.
    • Misreactions to good/bad information may be asymmetrical in both degree and speed.
    • Misreaction may be path dependent, such that similar new information may lead to different misreactions under different market conditions. 
  • Variations in prices are highly correlated for similar assets (which form asset classes), but substantially uncorrelated most of the time for different asset classes. 
  • Individual investors can access asset classes (diversified across similar components) via exchange-traded fund (ETF) and mutual fund proxies.

Strategic diversification across asset classes harvests uncorrelated volatility via periodic rebalancing to some basic class allocation weights. Periodic rebalancing, independent of any ability to time asset classes, tends to move funds from asset classes that are above their long-term trends to those below their long-term trends.

While expanding the number of asset classes held and increasing the rebalancing frequency have theoretical (frictionless) benefits, there are real-life trade-offs between: (1) number of asset classes held and rebalancing frictions; and, (2) rebalancing frequency and rebalancing frictions.

There are analytical models for optimizing asset class allocation weights (such as Modern Portfolio Theory and derivative models). However, empirical results suggest that simple equal weighting is competitive, probably because the inputs to analytical methods involve unreliable estimates of future asset class returns, volatilities and mutual correlations.

For simplicity and friction avoidance, individuals might consider a strategic allocation involving perhaps five to ten equally weighted asset classes (via low-fee ETFs or mutual funds) and annual rebalancing. This largely passive approach requires little effort after setup.

For investors seeking an active edge, there is evidence supporting belief that the price paths followed by assets and asset classes are partly predictable, supporting belief in strategies based on intermediate-term momentum and longer-term reversion to value. These strategies may be complementary aspects of the same marketwide sloshing of capital from asset class to asset class. Because of misreaction dynamics and asymmetries, it appears that investors must be more nimble to exploit momentum than reversion to value, and to exploit overvaluation than undervaluation.

Individual investors can implement momentum and/or value allocation strategies for asset classes (again, via low-fee funds, keeping search and trading costs down). These strategies achieve strategic diversification to some degree via the number of asset classes held at one time and by varying the asset classes held over time.

In summary, diversification with momentum and value strategies applied at the asset class level via low-fee funds (especially with momentum and value in combination) may be among the best approaches for individual investors.

To reiterate, the above observations are guesses regarding the kind of practical investing/trading strategies that work best. Data snooping bias and defects in statistical assumptions are pervasive in research (see Avoiding Investment Strategy Flame-outs), and these shortcomings generally suborn overstatement of expected returns. Also, “best” is a function of individual goals and constraints.

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