• Corpus ID: 5427551

HURST EXPONENT AND FINANCIAL MARKET PREDICTABILITY

@inproceedings{Qian2005HURSTEA,
  title={HURST EXPONENT AND FINANCIAL MARKET PREDICTABILITY},
  author={Bo Qian and Khaled M. Rasheed},
  year={2005}
}
The Hurst exponent (H) is a statistical measure used to classify time series. H=0.5 indicates a random series while H>0.5 indicates a trend reinforcing series. The larger the H value is, the stronger trend. In this paper we investigate the use of the Hurst exponent to classify series of financial data representing different periods of time. Experiments with backpropagation Neural Networks show that series with large Hurst exponent can be predicted more accurately than those series with H value… 

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References

SHOWING 1-10 OF 27 REFERENCES

Neural networks for financial forecasting

TLDR
This thesis investigates the use of the Backpropagation neural model for time-series forecasting using a Neural Forecasting System (NFS) and develops a new method to enhance input representations to a neural network, referred to as model sNx.

NEURAL NETWORKS FOR TECHNICAL ANALYSIS: A STUDY ON KLCI

This paper presents a study of artificial neural nets for use in stock index forecasting. The data from a major emerging market, Kuala Lumpur Stock Exchange, are applied as a case study. Based on the

Fractal Market Analysis: Applying Chaos Theory to Investment and Economics

FRACTAL TIME SERIES. Failure of the Gaussian Hypothesis. A Fractal Market Hypothesis. FRACTAL (R/S) ANALYSIS. Measuring Memory----The Hurst Process and R/S Analysis. Testing R/S Analysis. Finding

Chaos and Order in the Capital Markets: A New View of Cycles, Prices, and Market Volatility

THE NEW PARADIGM Introduction: Life Can Be So Complicated Random Walks and Efficient Markets The Failure of the Linear Paradigm Markets and Chaos: Chance and Necessity FRACTAL STRUCTURE IN THE

An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks

  • S. Walczak
  • Computer Science
    J. Manag. Inf. Syst.
  • 2001
TLDR
It is shown that those neural networks-given an appropriate amount of historical knowledge-can forecast future currency exchange rates with 60 percent accuracy, while those neural Networks trained on a larger training set have a worse forecasting performance.

The expected value of the adjusted rescaled Hurst range of independent normal summands

SUMMARY Hurst's empirical law concerning geophysical time series such as annual river flows was framed in terms of an adjusted rescaled range, namely, the range of cumulative sums of deviations of

Multifractality in Foreign Currency Markets

Several empirical studies have shown the inadequacy of the standard Brownian motion (sBm) as a model of asset returns. To correct for this evidence some authors have conjectured that asset returns

Nonlinear Pricing: Theory & Applications

A Toy Story for Wall Street. Nonlinearity: A Retrospective. Nonlinearity: A Prospective. Fractal Analysis. Results of the Hurst Exponent. Nonlinear Technology. Biology and the S&P. Father Time.

Fractal Market Analysis: Applying Chaos Theory to Investment and Economics

The Global Macro Economy and FinanceMultifractal Detrended Analysis Method and Its Application in Financial MarketsChaos Theory in the Financial MarketsFractals and ChaosNew Trading