Paper Info
Title
Regression genetic programming for estimating trend end in foreign exchange market
Abstract
Most forecasting algorithms use a physical time scale for studying price movement in financial markets, making the flow of physical time discontinuous. The use of a physical time scale can make companies oblivious to significant activities in the market, which poses a risk. Directional changes is a different and newer approach, which uses an event-based time scale. This approach summarises data into alternating trends called upward directional change and downward directional change. Each of these trends are further dismembered into directional change (DC) event and overshoot (OS) event. We present a genetic programming (GP) algorithm that evolves equations that express linear and non-linear relationships between the length of DC and OS events in a given dataset. This allows us to have an expectation when a trend will reverse, which can lead to increased profitability. This novel trend reversal estimation approach is then used as part of a DC-based trading strategy. We aim to appraise whether the new knowledge can lead to greater excess return. We assess the efficiency of the modified trading strategy on 250 different directional changes datasets from five different thresholds and five different currency pairs, consisting of intraday data from the foreign exchange (Forex) spot market. Results show that our algorithm is able to return profitable trading strategies and statistically outperform state-of-the-art financial trading strategies, such as technical analysis, buy and hold and other DC-based trading strategies.
Year
DOI
Venue
2017
10.1109/SSCI.2017.8280833
2017 IEEE Symposium Series on Computational Intelligence (SSCI)
Keywords
Field
DocType
regression genetic programming,trend end,foreign exchange market,forecasting algorithms,physical time scale,price movement,financial markets,alternating trends,upward directional change,downward directional change,directional change event,overshoot event,genetic programming algorithm,OS events,modified trading strategy,foreign exchange spot market,profitable trading strategies,express linear relationships,trend reversal estimation approach,currency pairs,nonlinear relationships,Forex,DC-based trading strategies
Econometrics,Trading strategy,Foreign exchange market,Computer science,Buy and hold,Genetic programming,Financial market,Market research,Spot market,Technical analysis
Conference
ISBN
Citations 
PageRank 
978-1-5386-2727-3
0
0.34
References 
Authors
7
3
Name
Order
Citations
PageRank
Adesola Adegboye100.34
Michael Kampouridis29416.60
Colin G. Johnson3933115.57