Optimizing filter rule parameters with genetic algorithm and stock selection with artificial neural networks for an improved trading: The case of Borsa Istanbul

Özçalıcı, Mehmet; Bumin, Mete

doi:10.1016/j.eswa.2022.118120

Cited by 24 publications

(12 citation statements)

References 46 publications

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“…They aimed to improve the accuracy of the forecasting process. In another study, Ozcalici and Bumin [ 17 ] used GA to optimize the parameters of filtering rules and stock selection for enhanced trading. They integrated artificial neural networks into their approach to achieve better trading performance.…”

Section: Literature Review and Index System Constructionmentioning

confidence: 99%

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Evaluation and screening of technology start-ups based on PCA and GA-BPNN

Li,

Meng,

Liu

et al. 2024

PLoS ONE

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Purpose Due to the existence of information opacity, there is a common problem of adverse selection in the process of screening alternative technology start-ups (TSs) and determining investment targets by venture capital institutions, which does not reveal the true value of enterprises and makes the market inefficient. The aim of this paper is to design an evaluation and screening system help venture capital institutions to select the qualified TSs as their investment objective. Design A research framework of four dimensions that include conception, technical innovation, business model and team structure, was built based on previous studies. Based on the research framework, 15 second-level indicators and 33 third-level indicators were extracted with literature research method. This paper proposes an evaluation model with back propagation neural network (BPNN) optimized by genetic algorithm (GA) to improve the rate of selecting and investing in qualified start-ups. Findings The results show that the evaluation accuracy of the evaluation model for qualified and unqualified enterprises can reach 80.33% and 93.67% respectively, which has verified the effectiveness of the model and algorithm. Originality/Value This paper established an effective evaluation system based on PCA and GA-BPNN to help venture capital institutions preliminarily screen potential technology start-ups, which provides the possibility for venture capital institutions to greatly reduce the screening time and cost, improve the screening efficiency of TSs, and scientifically assess the risk of investee projects or investee enterprises to obtain sustainable and stable excess profits.

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Section: Literature Review and Index System Constructionmentioning

confidence: 99%

“…X 32 [17,27] Shareholder strength Whether the shareholders have a well-known or large organization or individual (3 points)…”

Section: Team Stabilitymentioning

confidence: 99%

Evaluation and screening of technology start-ups based on PCA and GA-BPNN

Li,

Meng,

Liu

et al. 2024

PLoS ONE

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“…Over the decades, many artificial intelligence algorithms have been developed and applied to financial market forecasting, for instance, artificial neural network [9]- [11], support vector machines [12]- [17], rough set theory [18]- [20], bayesian analysis [21]- [24] and evolutionary learning algorithms [25]- [28]. However, most of the past researches mainly focus on the accurate price forecast only.…”

Section: Introductionmentioning

confidence: 99%

Integrating Piecewise Linear Representation and Deep Learning for Trading Signals Forecasting

Chen

Zhu

2023

IEEE Access

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Trading signal forecasting is an interesting but challenging research topic in the field of financial investment, since the financial market is an nonlinearity and high volatility system influenced by too many factors, and a small improvement in forecasting performance can bring profits. To realize trading signals detection, this paper presents a novel method which integrates piecewise linear representation (PLR) with a deep learning learning framework to predict the financial trading points. Firstly, we utilize PLR to generate a number of turning points (valleys or peaks) from trading data and formulate the trading points prediction as a three-class classification problem. Then, the framework combined a convolutional neural network (CNN) for spatial features extraction and a long short-term memory (LSTM) network for temporal domain features extraction (CNN-LSTM) is used to learn the prediction model between the trading points and the financial time series data. Finally, we conduct a series of experiments among PLR-CNN-LSTM, PLR-CNN-TA and PLR-LSTM on companies of US , Turkey and daily Exchange-Traded Fund (ETFs) to test the performance of our established method. The experiment results show that our proposed method has better model performance and profitability with different investment strategies.

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“…Recent deep learning (DL) models have outperformed the traditional statistical and ML approaches on many stock market prediction tasks. Most ML approaches based on the optimization algorithms to obtain the best model parameters or rules, different optimization algorithms will lead to the model performances (Liu et al 2021 ; Ozcalici and Bumin, 2022 ). However, the ML and DL algorithms have been used to learn and predict the stock prices or trading actions, but the overfitting problem still happens in the training stage and cannot adapt in the future.…”

Section: Introductionmentioning

confidence: 99%

A prediction model of stock market trading actions using generative adversarial network and piecewise linear representation approaches

Tang

Hsu

2022

Soft Comput

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Supervised learning applied stock prediction tasks and obtained satisfactory performance. The trading strategies are very complex and diverse but supervised learning is only learned and fitted by gold standard trading strategies. Supervised learning approaches often have over-fitting problems. To learn distribution of gold standard answers, the generative adversarial network (GAN) models can generate extra similar samples to improve performance. Therefore, the paper proposes a generative GAN-based frameworks with the piecewise linear representation (PLR) approach to learn three trading actions, namely buying, selling, and holding. The proposed framework consists of two parts: first, PLR approach uses to detect historical prices to form trading sequences with three actions, PLR can provide a guided trading strategy to discriminator of GAN. Second, the generator of GAN is used to generate/predict daily trading actions, and the discriminator is used to detect the real/fake trading actions from the PLR/generator of GAN. Experimental results indicate that the proposed GAN-based frameworks outperform the long short-term memory network.

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Optimizing filter rule parameters with genetic algorithm and stock selection with artificial neural networks for an improved trading: The case of Borsa Istanbul

Cited by 24 publications

References 46 publications

Evaluation and screening of technology start-ups based on PCA and GA-BPNN

Evaluation and screening of technology start-ups based on PCA and GA-BPNN

Integrating Piecewise Linear Representation and Deep Learning for Trading Signals Forecasting

A prediction model of stock market trading actions using generative adversarial network and piecewise linear representation approaches

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