scholarly journals Intelligent Dynamic Backlash Agent: A Trading Strategy Based on the Directional Change Framework

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 171 ◽  
Author(s):  
Amer Bakhach ◽  
Venkata Chinthalapati ◽  
Edward Tsang ◽  
Abdul El Sayed
Keyword(s):  
Time Series ◽  
Risk Management ◽  
Financial Time Series ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Price Movement ◽  
Original Order ◽  
Financial Time ◽  
Directional Change ◽  
Change Framework

The Directional Changes (DC) framework is an approach to summarize price movement in financial time series. Some studies have tried to develop trading strategies based on the DC framework. Dynamic Backlash Agent (DBA) is a trading strategy that has been developed based on the DC framework. Despite the promising results of DBA, DBA employed neither an order size management nor risk management components. In this paper, we present an improved version of DBA named Intelligent DBA (IDBA). IDBA overcomes the weaknesses of DBA as it embraces an original order size management and risk management modules. We examine the performance of IDBA in the forex market. The results suggest that IDBA can provide significantly greater returns than DBA. The results also show that the IDBA outperforms another DC-based trading strategy and that it can generate annualized returns of about 30% after deducting the bid and ask spread (but not the transaction costs).

RANDOM MATRIX THEORY AND FINANCIAL CORRELATIONS

2000 ◽  
Vol 03 (03) ◽  
pp. 391-397 ◽  
Author(s):  
LAURENT LALOUX ◽  
PIERRE CIZEAU ◽  
MARC POTTERS ◽  
JEAN-PHILIPPE BOUCHAUD
Keyword(s):  
Time Series ◽  
Risk Management ◽  
Theoretical Prediction ◽  
Correlation Matrix ◽  
Matrix Theory ◽  
Financial Time Series ◽  
Statistical Structure ◽  
Correlation Matrices ◽  
Financial Time ◽  
Remarkable Agreement

We show that results from the theory of random matrices are potentially of great interest when trying to understand the statistical structure of the empirical correlation matrices appearing in the study of multivariate financial time series. We find a remarkable agreement between the theoretical prediction (based on the assumption that the correlation matrix is random) and empirical data concerning the density of eigenvalues associated to the time series of the different stocks of the S&P500 (or other major markets). Finally, we give a specific example to show how this idea can be sucessfully implemented for improving risk management.

Financial Data Mining Using Flexible ICA-GARCH Models

2010 ◽  
pp. 255-272
Author(s):  
Philip L.H. Yu ◽  
Edmond H.C. Wu ◽  
W.K. Li
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Risk Management ◽  
Independent Component Analysis ◽  
Value At Risk ◽  
Financial Time Series ◽  
Multivariate Time Series ◽  
Independent Component ◽  
Garch Models ◽  
Financial Time

As a data mining technique, independent component analysis (ICA) is used to separate mixed data signals into statistically independent sources. In this chapter, we apply ICA for modeling multivariate volatility of financial asset returns which is a useful tool in portfolio selection and risk management. In the finance literature, the generalized autoregressive conditional heteroscedasticity (GARCH) model and its variants such as EGARCH and GJR-GARCH models have become popular standard tools to model the volatility processes of financial time series. Although univariate GARCH models are successful in modeling volatilities of financial time series, the problem of modeling multivariate time series has always been challenging. Recently, Wu, Yu, & Li (2006) suggested using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series components and then separately modeled the independent components by univariate GARCH models. In this chapter, we extend this class of ICA-GARCH models to allow more flexible univariate GARCH-type models. We also apply the proposed models to compute the value-at-risk (VaR) for risk management applications. Backtesting and out-of-sample tests suggest that the ICA-GARCH models have a clear cut advantage over some other approaches in value-at-risk estimation.

scholarly journals A Directional-Change Event Approach for Studying Financial Time Series

2012 ◽  
Vol 6 (2012-36) ◽  
pp. 1 ◽  
Author(s):  
Monira Aloud ◽  
Edward Tsang ◽  
Richard Olsen ◽  
Alexandre Dupuis
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Financial Time ◽  
Directional Change ◽  
Change Event

Towards a non-linear trading strategy for financial time series

2006 ◽  
Vol 28 (3) ◽  
pp. 601-615 ◽  
Author(s):  
Fernanda Strozzi ◽  
José-Manuel Zaldívar Comenges
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Trading Strategy ◽  
Financial Time ◽  
Non Linear

scholarly journals A Directional-Change Events Approach for Studying Financial Time Series

2011 ◽  
Author(s):  
Monira Aloud ◽  
Edward P. K. Tsang ◽  
Richard B. Olsen ◽  
Alexandre Dupuis
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Financial Time ◽  
Change Events ◽  
Directional Change
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