A Two-level Cascade Evolutionary Computation based Covered Call Trading Model
By Cihan H. Dagli, Mustafa Ucar, İlknur Bayram, A. Ozbayoglu. 2013
7 citations recorded by the source corpus.
Abstract In this study, a two-level cascade stock trading model is proposed. In the first level, the buy/sell signals are created by optimizing the RSI technical indicator parameters with evolutionary computation techniques. Then using the selected parameters, in the second level actual trading is performed using an optimized covered call strategy. Again, the optimization is implemented with evolutionary computation. In this particular study, genetic algorithms (GA) and Particle Swarm Optimization (PSO) are chosen as the soft computing methods for optimization. Historical end-of-day close values and options data for the SP the testing is done with 2009 data. The results indicate that the proposed model outperformed not only the buy and hold strategy, but also buying and selling the ETF alone without the options. In future work different stock/ETF data and different combined options strategies will be included in the model to identify performances of different techniques.
Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Economics and Econometrics
In Management Science and Operations Research
Citations in the corpus
- Developing a two level options trading strategy based on option pair optimization of spread strategies with evolutionary algorithms (2015, methodology): “However it is also possible to use PSO in optimizing trading strategies [24].”