Fundamental Framework for Technical Analysis
J. V. Andersen, S. Gluzman, D. Sornette

TL;DR
This paper introduces a probabilistic framework based on market price velocity and acceleration, classifying market patterns through dimensionless parameters, and demonstrates its predictive power across various financial markets.
Contribution
It presents a novel, scale-invariant classification method for market patterns using a renormalized scenario approach with empirical validation on diverse financial data.
Findings
The renormalized scenario approach shows significant predictive power across all market phases.
Trend-following strategies perform well only in specific market conditions.
The method effectively combines multiple scenarios to optimize market trajectory predictions.
Abstract
Starting from the characterization of the past time evolution of market prices in terms of two fundamental indicators, price velocity and price acceleration, we construct a general classification of the possible patterns characterizing the deviation or defects from the random walk market state and its time-translational invariant properties. The classification relies on two dimensionless parameters, the Froude number characterizing the relative strength of the acceleration with respect to the velocity and the time horizon forecast dimensionalized to the training period. Trend-following and contrarian patterns are found to coexist and depend on the dimensionless time horizon. The classification is based on the symmetry requirements of invariance with respect to change of price units and of functional scale-invariance in the space of scenarii. This ``renormalized scenario'' approach is…
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Taxonomy
TopicsComplex Systems and Time Series Analysis · Stock Market Forecasting Methods
