Prediction of Stock Value using Pattern Matching Algorithm based on Deep Learning

doi:10.35940/ijrte.b1007.0782s619

Cited by 3 publications

(5 citation statements)

References 7 publications

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“…To predict the Google stock price, Singh and Srivastava [65] Currently, DNN has been widely applied in the stock market to identify the trends and patterns among the financial time series data. Go and Hong [46] used the DNN method to predict the stock value. They first trained the method by the time series data and then tested and confirmed their model's predictability.…”

Section: Dnnmentioning

confidence: 99%

“…CNN algorithm is also used for analyzing social media data for sentiment analysis [58]. The DNN algorithm, likewise the LSTM, is only used to analyze financial time series data to predict stock prices [46,49,63] and the S&P 500 Index trend prediction [60]. The GRU algorithm, which is another DL model, is applied in the e-commerce section to analyze financial time series [80] and customer time series [82].…”

Section: Cryptocurrencymentioning

confidence: 99%

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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%

Section: Cryptocurrencymentioning

confidence: 99%

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

et al. 2020

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“…In another study, Go and Hong [64] employed the DL technique to forecast stock value streams Harvard IV-4. Additionally, they provided a simulation system for investors in order to calculate their actual return.…”

Section: Deep Learning In Stock Pricingmentioning

confidence: 99%

“…In another study, Go and Hong [64] employed the DL technique to forecast stock value streams while analysing the pattern in stock price. The study designed a DNN deep learning algorithm to Based on Figure 12, the highest accuracy is related to a simple feature model without filtering, and the lowest accuracy is related to a novel feature model with plunge filtering.…”

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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Evolutionary Framework with Bidirectional Long Short-Term Memory Network for Stock Price Prediction

Zheng

Wang

Chen

2021

Mathematical Problems in Engineering

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As an important part of the social economy, stock market plays an important role in economic development, and accurate prediction of stock price is important as it can lower the risk of investment decision-making. However, the task of predicting future stock price is very difficult. This difficulty arises from stocks with nonstationary behavior and without any explicit form. In this paper, we propose a novel bidirectional Long Short-Term Memory Network (BiLSTM) framework called evolutionary BiLSTM (EBiLSTM) for the prediction of stock price. In the framework, three independent BiLSTMs correspond to different objective functions and act as mutation individuals, then their respective losses for evolution are calculated, and finally, the optimal objective function is identified by the minimum of loss. Since BiLSTM is effective in the prediction of time series and the evolutionary framework can get an optimal solution for multiple objectives, their combination well adapts to the nonstationary behavior of stock prices. Experiments on several stock market indexes demonstrate that EBiLSTM can achieve better prediction performance than others without the evolutionary operator.

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Prediction of Stock Value using Pattern Matching Algorithm based on Deep Learning

Cited by 3 publications

References 7 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

Evolutionary Framework with Bidirectional Long Short-Term Memory Network for Stock Price Prediction

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