Strategy types overview
An overview of common strategy families: momentum, mean reversion, value screening, pairs trading, and automated bots. Education, not a guide to trade on.
This page is deliberately built differently from what you usually find on trading strategies elsewhere. It doesn't tell you which rules to follow. It tells you which strategy types exist, what decades of academic research have found about them, and where the weaknesses lie.
The reason for this difference is simple. A rule like "buy when the price is above the 200-day average" sounds like a guide to follow, but without context it's useless or even dangerous. Understanding why a strategy family showed an effect at all across long periods and many markets, and why that effect often weakens over time, is the education this section is about.
Each of the following pages covers one strategy family: the basic idea, a well-known research finding about it, the attempted explanations for why the effect exists at all, and the reasons it's harder to exploit in practice than on paper.
One pattern runs through almost every strategy type: they worked better in academic studies than they do in a single investor's reality. Trading costs, taxes, slippage, and sheer competition from professional players eat up a substantial share of the measured excess return. That's not a coincidence, it follows directly from the definition of an efficient market.
Academic studies of trading strategies are almost always conducted on historical price series with simplified cost assumptions. The magnitudes of measured excess return refer to broadly diversified portfolios held long-term, often with monthly or quarterly rebalancing, not to individual positions of a retail investor with limited capital.
A recurring finding in the literature is the weakening of documented anomalies after publication. An effect described in a paper attracts capital trying to exploit exactly that effect. This additional capital changes price formation and shrinks future excess return, a pattern the literature calls post-publication decay, documented for several of the strategy families covered here.
For a retail investor, that implies: an academically supported strategy family isn't a promise of a specific future return, it's the description of a historically observed pattern with a plausible, but not conclusively settled, explanation. The lessons on Backtesting and Overfitting apply in full to every page that follows.
Summary
- These pages explain strategy types, they don't recommend any.
- Academic results apply to broad, long-held portfolios, not automatically to individual positions.
- Many documented effects weaken once they become known and get used.
Did you get it?
What sets these pages apart from a trading guide?
They explain a strategy family's mechanism and research standing, but give no rules to trade on.
Why do academically supported effects often weaken in practice?
Because capital trying to exploit the effect changes price formation and shrinks future excess return.
What kind of portfolio do most study results apply to?
Broadly diversified portfolios held over long periods, not individual positions with limited capital.
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