Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction

Göçken, Mustafa; Özçalıcı, Mehmet; Boru, Aslı; Dosdoğru, Ayşe Tuğba

doi:10.1016/j.eswa.2015.09.029

Cited by 276 publications

(139 citation statements)

References 28 publications

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“…They conclude that information obtained through DC‐based analysis and from time series complement each other. A similar study, with similar results, has been published independently in Aloud .…”

Section: Directional Changessupporting

confidence: 81%

“…Some of these forecasting models have the traditional objective of predicting the change of the direction of market's trend (e.g. Chen & Chen, ; Davis & Episcopos, ; Galeshchuk & Mukherjee, ; Göçkena, Özçalıcıb, Borua, & Dosdo˘gru, ; Li, Deng, & Luo, ). In 2016, Bakhach, Tsang, and Jalalian () proposed a forecasting model, under the DC context, which aims to answer the question of whether the current trend will continue until a specific magnitude, expressed as a percentage, is reached before the trend ends.…”

Section: Introductionmentioning

confidence: 99%

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TSFDC: A trading strategy based on forecasting directional change

Bakhach

Tsang

Chinthalapati

2018

Intell Sys Acc Fin Mgmt

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Directional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. In this paper, we develop a contrarian trading strategy named TSFDC. TSFDC is based on a forecasting model which aims to predict the change of the direction of market's trend under the DC context. We examine the profitability, risk and risk-adjusted return of TSFDC in the FX market using eight currency pairs. The results suggest that TSFDC outperforms the buy and hold approach and another DC-based trading strategy.

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Section: Directional Changessupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

TSFDC: A trading strategy based on forecasting directional change

Bakhach

Tsang

Chinthalapati

2018

Intell Sys Acc Fin Mgmt

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“…ANNs strive to simulate, in a great fashion, the network of neurons (nerve cells) of the biological nervous system. The physical structure and information processing of the human brain are partially imitated with collections of interconnected neurons to model nonlinear systems [15]. Neurons are cells in the brain that contain input wires to other neurons called dendrites and output wires from a neuron to other neurons called axons.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

“…In this experiment, the stochastic universal sampling technique was adopted. This method chooses potential chromosomes according to their calculated fitness value in step (2) [15]. Both mutation and crossover were applied concurrently to reproduce the fittest offspring.…”

mentioning

confidence: 99%

Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems

Hassan

Hamada

2017

Computation

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Accuracy improvement is among the primary key research focuses in the area of recommender systems. Traditionally, recommender systems work on two sets of entities, Users and Items, to estimate a single rating that represents a user's acceptance of an item. This technique was later extended to multi-criteria recommender systems that use an overall rating from multi-criteria ratings to estimate the degree of acceptance by users for items. The primary concern that is still open to the recommender systems community is to find suitable optimization algorithms that can explore the relationships between multiple ratings to compute an overall rating. One of the approaches for doing this is to assume that the overall rating as an aggregation of multiple criteria ratings. Given this assumption, this paper proposed using feed-forward neural networks to predict the overall rating. Five powerful training algorithms have been tested, and the results of their performance are analyzed and presented in this paper.

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“…The prediction of the compressive strength of high-performance concrete was modeled in [71] through a metaheuristic-optimized least-squares support vector regression algorithm. In [72], a hybrid algorithm that integrates metaheuristics with artificial neural networks was designed with the aim of improving the prediction of stock prices. Another example of price prediction was developed in [26].…”

Section: Hybridizing Metaheuristics With Machine Learningmentioning

confidence: 99%

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

et al. 2020

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This article proposes a hybrid algorithm that makes use of the db-scan unsupervised learning technique to obtain binary versions of continuous swarm intelligence algorithms. These binary versions are then applied to large instances of the well-known multidimensional knapsack problem. The contribution of the db-scan operator to the binarization process is systematically studied. For this, two random operators are built that serve as a baseline for comparison. Once the contribution is established, the db-scan operator is compared with two other binarization methods that have satisfactorily solved the multidimensional knapsack problem. The first method uses the unsupervised learning technique k-means as a binarization method. The second makes use of transfer functions as a mechanism to generate binary versions. The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function (TF) and random operators.

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Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction

Cited by 276 publications

References 28 publications

TSFDC: A trading strategy based on forecasting directional change

TSFDC: A trading strategy based on forecasting directional change

Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

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