• Corpus ID: 5427551


  author={Bo Qian and Khaled M. Rasheed},
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… 

Scaling Behavior of Time Series and an Empirical Indication to Financial Prediction

This paper compares some typical series and finds that when Hurst exponents are greater than 0.9, DFA is a better method to distinguish the two series with exponents of small difference in that range where non-stationary most likely exists, useful in solving the important open problem of telling whether the real financial data are Brownian motions.

Hurst Exponent and Trading Signals Derived from Market Time Series

This contribution constructed a new technical indicator MH (Moving Hurst) based on Hurst exponent that describes chaotic properties of time series and stated and proved a hypothesis that this indicator can bring more profit than the very well known indicator MACD (Moving Averages Convergence Divergence).

Hurst exponent, fractals and neural networks for forecasting financial asset returns in Brazil

Our scope is to verify the existence of a relationship between long-term memory in fractal time series and the prediction error of financial asset returns obtained by artificial neural networks

The use of the Hurst exponent to investigate the quality of forecasting methods of ultra-high-frequency data of exchange rates

Over the last century a variety of methods have been used for forecasting financial time data series with different results. This article explains why most of them failed to provide reasonable

Predicting Stock Market Time Series Using Evolutionary Artificial Neural Networks with Hurst Exponent Input Windows

Fractal analyses based on Hurst exponent calculations are used to characterize the time series and to identify appropriate input windows for the enhanced evolutionary artificial neural network (EANN) model.

Application of Hurst Exponent (H) and the R/S Analysis in the Classification of FOREX Securities

This paper presents the relationship between the Hurst Exponent (H) and the Rescaled Range Analysis (R/S) in the classification of Foreign Exchange Market (FOREX) time series by the supposition of

Entropy-Based Indicator for Predicting Stock Price Trend Reversal

This article designed aggregated entropy-based (EB) indicator and explored its ability to forecast the turning point of trend of the financial time series and to calibrate the stock market trading strategy.

A Relative Tendency Based Stock Market Prediction System

  • U. ManChonK. Rasheed
  • Computer Science
    2010 Ninth International Conference on Machine Learning and Applications
  • 2010
A novel stock market prediction system which focuses on forecasting the relative tendency growth between different stocks and indices rather than purely predicting their values is presented.

Forecasting the portuguese stock market time series by using artificial neural networks

It is shown that neural networks can be used to uncover the non-linearity that exists in the financial data by training four types of neural networks for the stock markets and using the models to make forecasts.

Tracing of stock market long term trend by information efficiency measures




Neural networks for financial forecasting

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.


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
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