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# Category Archives: Nonlinear Dynamics

## Can Machine Learning Techniques Be Used To Predict Market Direction? The 1,000,000 Model Test.

During the 1990′s the advent of Neural Networks unleashed a torrent of research on their applications in financial markets, accompanied by some rather extravagant claims about their predicative abilities. Sadly, much of the research proved to be sub-standard and the … Continue reading

Posted in Direction Prediction, Forecasting, Logit Regression, Machine Learning, Matlab, Modeling, Nearest Neighbor, Neural Networks, Nonlinear Classification, Nonlinear Dynamics, Random Forrests, S&P500 Index, Support Vector Machines
Tagged Direction Prediction, Forecasting, Machine Learning, Nearest Neighbor, Neural Networks, Nonlinear Classification, Random Forrests, Support Vector Machines
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## Long Memory and Regime Shifts in Asset Volatility

This post covers quite a wide range of concepts in volatility modeling relating to long memory and regime shifts. The post discusses autocorrelation, long memory, fractional integration, black noise, white noise, Hurst Exponents, regime shift detections, Asian markets and various topics froms nonlinear dynamics. Continue reading

Posted in ARFIMA, Asian markets, Black Noise, Correlation Dimension, Correlation Integral, FIGARCH, Forecasting, Fractional Brownian Motion, Fractional Integration, Henon Attractor, Hurst Exponent, Logistic Attractor, Long Memory, Modeling, Nonlinear Dynamics, Pink Noise, Regime Shifts, Strange Attractor, Uncategorized, Volatility Modeling, White Noise
Tagged ARFIMA, Black Noise, Forecasting, Fractional Brownian Motion, Fractional Integration, Henon Attractor, Logistic Attractor, Long Memory, Regime Shifts, Strange Attractor, Volatility, Volatility Dynamics, White Noise
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