A multiobjective optimization approach for market timing

Mohamed, Ismail; Otero, Fernando E. B.

doi:10.1145/3377930.3390156

Cited by 4 publications

(8 citation statements)

References 31 publications

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“…The goal was to find a trading strategy maximizing profitability in foreign exchange markets. iRace was also applied in [Mohamed and Otero 2020] to tune implementations of a multi-objective genetic algorithm and a particle swarm optimization to decide when to buy or sell on the stock market.…”

Section: Related Workmentioning

confidence: 99%

A Methodology for Definition and Refinement of a LSTM Stock Predictor Architecture using iRace and NSGA-II

Costa

Wanner

Martins

et al. 2022

Anais Do I Brazilian Workshop on Artificial Intelligence in Finance (BWAIF 2022)

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This paper presents a novel methodology aiming to define and refine a LSTM architecture applied to predict stock market prices. The methodology, dubbed STOCK-PRED: THE LSTM PROPHET OF THE STOCK MARKET, uses iRace and NSGA-II algorithms. The LSTM is built in two steps: (i) initially, iRace determines a robust set of hyperparameters using a compound objective function; afterwards, (ii) one of the best structures is used to define a tiny search space for the NSGA-II populations. In this step NSGA-II optimizes, simultaneously, the Mean Squared Error, in relation to price prediction, and the Accumulated Accuracy Rate for a time horizon of seven days, relative to growth price tendency. The methodology is tested considering stock market tickers from USA and Brazil. Even in a challenging scenario surrounded by possibly turbulent events, the Stock-Pred predicts the prices based on historical data and machine learning techniques. Since the optimization problem is noisy and the objective functions have a high computational cost, we consider a low budget in relation to the number of fitness evaluations. The analysis of the non-dominated solution indicates that the proposed methodology is promising, achieving a MAPE of 1.279%, 1.564% and 2.047% for BVSP, IBM and AAPL stocks respectively.

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Section: Related Workmentioning

confidence: 99%

A Methodology for Definition and Refinement of a LSTM Stock Predictor Architecture using iRace and NSGA-II

Costa

Wanner

Martins

et al. 2022

Anais Do I Brazilian Workshop on Artificial Intelligence in Finance (BWAIF 2022)

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show abstract

“…In this section, we take a detailed look at the PSO and GA algorithms introduced in [10]. In order to avoid confusion during comparisons, the multiobjective variants of the algorithms from [10] will be prefixed with the Greek letter Lambda, resulting in the multiobjective algorithms being labeled as λ-PSO, λ-PSO SP and λ-GA. 1 As mentioned in [10], the algorithms underwent a number of modifications to adapt them to multiobjective optimization.…”

Section: Algorithmsmentioning

confidence: 99%

“…In this paper, we provide a deeper insight into the algorithms introduced in [10], and address the limitations in the results there. We will provide a better evaluative context in regards to performance by comparing the results of the algorithms against NSGA-II, a well established multiobjective optimization GA algorithm, and MACD, a component widely used in market timing strategies to generate recommendations for action to take.…”

Section: Introductionmentioning

confidence: 99%

“…We will provide a better evaluative context in regards to performance by comparing the results of the algorithms against NSGA-II, a well established multiobjective optimization GA algorithm, and MACD, a component widely used in market timing strategies to generate recommendations for action to take. We will also look into the diversity and spread of the solutions in the Pareto sets returned by the algorithms in [10], and compare them with NSGA-II and MACD by visualizing the results using RadViz [11].…”

Section: Introductionmentioning

confidence: 99%

“…The remainder of this paper is structured as follows: Firstly, We will begin by taking a deeper look at the algorithms introduced in [10], and contrasting their structure with known multiobjective PSO algorithms. Secondly, we will compare the performance of the algorithms against NSGA-II and MACD, as well as their single objective optimization counterparts from [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Performance Study of Multiobjective Particle Swarm Optimization Algorithms for Market Timing

Mohamed

Otero

2022

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)

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Market timing is the issue of deciding when to buy or sell a given asset on a financial market. As one of the core issues of algorithmic trading systems, designers of such systems have turned to computational intelligence methods to aid them in this task. In our previous work, we introduced a number of Particle Swarm Optimization (PSO) algorithms to compose strategies for market timing using a novel training and testing methodology that reduced the likelihood of overfitting and tackled market timing as a multiobjective optimization problem. In this paper, we provide a detailed analysis of these multiobjective PSO algorithms and address two limitations in the results presented previously. The first limitation is that the PSO algorithms have not been compared to well-known algorithms or market timing techniques. This is addressed by comparing the results obtained against NSGA-II and MACD, a technique commonly used in market timing strategies. The second limitation is that we have no insight regarding diversity of the Pareto sets returned by the algorithms. We address this by using RadViz to visualize the Pareto sets returned by all the algorithms, including NSGA-II and MACD. The results show that the multiobjective PSO algorithms return statistically significantly better results than NSGA-II and MACD. We also observe that the multiobjective PSO SP algorithm consistently displayed the best spread in its returned Pareto sets despite not having any explicit diversity promoting measures.

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Stock-Pred: The LSTM Prophet of the Stock Market

Costa

Cruz²

2022

Communications in Computer and Information Science

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A multiobjective optimization approach for market timing

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 4 publications

References 31 publications

A Methodology for Definition and Refinement of a LSTM Stock Predictor Architecture using iRace and NSGA-II

A Methodology for Definition and Refinement of a LSTM Stock Predictor Architecture using iRace and NSGA-II

A Performance Study of Multiobjective Particle Swarm Optimization Algorithms for Market Timing

Stock-Pred: The LSTM Prophet of the Stock Market

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