6.7 From Indicator Value to Expected Value
An indicator's scale knows nothing about money. Calibrate the reading to the risk-adjusted return that historically followed it, scale it to a common target, and size on that, not the raw output.
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ali askar
6.6 Why Z-Scoring Makes Ranking Cleaner
Raw signals live on different scales, so loud names dominate any sort. Z-scoring recenters and rescales into standard-deviation units, keeping the conviction pure ranking throws away.
6.5 When an Alpha Metric Is U-Shaped
Ranking assumes more-of-the-metric means more return. When the metric is U-shaped, both extremes are good and a naive rank buys the worst names. Plot the shape, then fold it monotone before you sort.
6.40 From Econophysics to Practical Trading Signals
Econophysics describes markets beautifully but predicts nothing tradable alone. It shows where to look and keeps the tails in mind; reaching profit takes the same testing and costs as any strategy.
6.4 Cost-Aware Ranking: The Missing Step in Cross-Sectional Strategies
Ranked systems churn at the slice boundaries, where names flicker across on noise and you pay for each swap. A no-trade buffer holds them, and only net-of-cost performance tells the truth.
6.38 The Three Stages of a Trading Idea: Absurd, Familiar, Inevitable
Trading ideas go absurd, familiar, inevitable; the edge is largest at absurd, gone at inevitable. Hunt in uncomfortable places but verify ruthlessly, because most absurd ideas are wrong, not early.
6.37 Why Financial Markets Are Complex Systems
A market is a complex system where crashes emerge from millions of decisions, leaving power-law fingerprints. Fat tails and clustering are permanent; the lens explains markets, it can't time them.
6.36 Anomalous Diffusion in Financial Markets
If prices were random they'd diffuse like ink, spreading as the square root of time. Markets diffuse anomalously: faster trending, slower reverting. That exponent is the variance ratio in physics.
6.35 Entropy as a Market Concept
Entropy measures how unpredictable a series is, catching nonlinear structure the variance ratio misses. Maximizing it under market constraints produces fat tails: the tails are natural, not a glitch.
6.34 Lévy Distributions and Market Extremes
The Lévy distribution fits the body of returns far better than the Gaussian, with real power-law tails, and still misses the crashes. No single elegant curve captures the tail that kills you.
6.33 Fat Tails: Why Gaussian Thinking Breaks Trading Systems
The bell curve calls crashes impossible, and they keep happening, because real returns have fat power-law tails. Gaussian models fail in the crisis they exist for. Size for the move you can't model.
6.32 Variance Ratio Tests for Traders
For a random walk, variance grows linearly with time. The variance ratio compares real growth to that benchmark: above 1 trends, below 1 mean-reverts. It measures the past, not the future.