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

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).

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

scholarly journals Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Tianle Zhou ◽  
Chaoyi Chu ◽  
Chaobin Xu ◽  
Weihao Liu ◽  
Hao Yu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Financial Market ◽  
Financial Time Series ◽  
Influential Factors ◽  
Series Data ◽  
Phase Point ◽  
Price Fluctuations ◽  
Financial Time

In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data.

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