Effects of Decision Models on Dynamic Multi-objective Optimization Algorithms for Financial Markets

Maximizing profit in financial time series, like foreign exchange, with computational intelligence techniques is very challenging. It is even more challenging to make a decision from a multi-objective problem, like automated foreign exchange (Forex) trading. This study explores the effects of five d...

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Veröffentlicht in:2019 IEEE Congress on Evolutionary Computation (CEC) S. 762 - 770
Hauptverfasser: Atiah, Frederick Ditliac, Helbig, Marde
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2019
Schlagworte:
computational intelligence
Computational modeling
Currencies
DMOPSO-CD
DNSGA-II
DVEPSO
dynamic multi-objective
foreign exchange
Forex
Heuristic algorithms
Optimization
Particle swarm optimization
Sorting
stock market
Stock markets
swarm intelligence
Technical indicators
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Zusammenfassung:Maximizing profit in financial time series, like foreign exchange, with computational intelligence techniques is very challenging. It is even more challenging to make a decision from a multi-objective problem, like automated foreign exchange (Forex) trading. This study explores the effects of five decision models on three state-of-the-art dynamic multi-objective optimization algorithms namely, dynamic vector-evaluated particle swarm optimization (DVEPSO), multi-objective particle swarm optimization with crowded distance (MOPSO-CD) and dynamic non-dominated sorting genetic algorithm (DNSGA-II). A sliding window mechanism is employed over the USDZAR currency pair. The results show that each decision model generates different net profit. However, gray relational analysis (GRA) and objective sum (SUM) consistently performed better across all algorithms and technical indicators (relative strength index (RSI) and moving average convergence divergence (MACD)) than other decision models. Moreover, DNSGA-II was the most stable algorithm.
DOI:10.1109/CEC.2019.8790275