Advanced Deep Learning Framework for Stock Value Prediction

Das, Sumanjit; Mishra, Sarojananda

doi:10.35940/ijitee.b2453.0881019

Cited by 5 publications

(5 citation statements)

References 42 publications

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“…In addition, hybrid methods that are constructed based on DNN have been reported to be very accurate in the financial time series data. For example, Das and Mishra [45] proposed an advanced model to plan, analyze, and predict the stock value, using a multilayer deep neural network (MDNN) optimized by Adam optimizer (AO) to find the patterns among the stock values. Moews et al [48] proposed a method to predict the stock market's behavior, as a complex system with a massive number of noisy time series.…”

Section: Dnnmentioning

confidence: 99%

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

et al. 2020

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This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.

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Section: Dnnmentioning

confidence: 99%

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

et al. 2020

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“…Figure 13 provides a comparison of the proposed method with the similar method reported by different studies in terms of accuracy. Figure 13 reports the comparative analysis from Das and Mishra [65]. Based on Figure 13, the proposed method by employing the related dataset could considerably improve the accuracy value by about 34, 13, and 1.5% compared with Huynh et al [66], Mingyue et al [67], and Weng et al [68], respectively.…”

Section: Deep Learning In Stock Pricingmentioning

confidence: 94%

“…In the study by Das and Mishra [65], a new multilayer deep learning approach was used by employing the time series concept for data representation to forecast the close price of current stock. Results were evaluated by prediction error and accuracy values compared to the results obtained from the related studies.…”

Section: Deep Learning In Stock Pricingmentioning

confidence: 99%

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Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

et al. 2020

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The popularity of deep reinforcement learning (DRL) applications in economics has increased exponentially. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated dynamic economics systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into the state-of-the-art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems in the presence of risk parameters and the ever-increasing uncertainties.

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“…It also converges fast and provided a solution for the majority of the challenges faced by different optimisers which include slow convergence and vanishing gradients. The Adam optimiser has been used in the design of a new gated branch neural network for an advanced driver assistance system [44] as well as been the most used optimiser in the recent DL model developments and has been used across diverse industrial applications [45,46].…”

Section: Adammentioning

confidence: 99%

Advances in Optimisation Algorithms and Techniques for Deep Learning

Nwankpa¹

2020

Adv. sci. technol. eng. syst. j.

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In the last decade, deep learning(DL) has witnessed excellent performances on a variety of problems, including speech recognition, object recognition, detection, and natural language processing (NLP) among many others. Of these applications, one common challenge is to obtain ideal parameters during the training of the deep neural networks (DNN). These typical parameters are obtained by some optimisation techniques which have been studied extensively. These research have produced state-of-art(SOTA) results on speed and memory improvements for deep neural networks(NN) architectures. However, the SOTA optimisers have continued to be an active research area with no compilations of the existing optimisers reported in the literature. This paper provides an overview of the recent advances in optimisation algorithms and techniques used in DNN, highlighting the current SOTA optimisers, improvements made on these optimisation algorithms and techniques, alongside the trends in the development of optimisers used in training DL based models. The results of the search of the Scopus database for the optimisers in DL provides the articles reported as the summary of the DL optimisers. From what we can tell, there is no comprehensive compilation of the optimisation algorithms and techniques so far developed and used in DL research and applications, and this paper summarises these facts.

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Advanced Deep Learning Framework for Stock Value Prediction

Cited by 5 publications

References 42 publications

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

Advances in Optimisation Algorithms and Techniques for Deep Learning

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