Predicting financial market in big data: Deep learning

Hasan, Afan; Kalipsiz, Oya; Akyokuş, Selim

doi:10.1109/ubmk.2017.8093449

Cited by 19 publications

(5 citation statements)

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“…The classification performance obtained by ESA is evaluated to be higher than those obtained with the chi-square feature selection and logistic regression classifier. The approach in [20] focuses on stock market prediction by evaluating deep learning methods for classification purpose. In [21], the accuracy rate of 94.21% prediction is obtained by using LSTM deep learning method in Turkish texts.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Word Embedding and Deep Learning Based Model to Forecast the Direction of Stock Exchange Market Using Twitter and Financial News Sites: A Case of Istanbul Stock Exchange (BIST 100)

Kilimci¹,

Duvar²

2020

IEEE Access

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To forecast the movement directions of stocks, exchange rates, and stock markets are significant and an active research area for investors, analysts, and researchers. In this paper, word embedding and deep learning-based direction prediction of Istanbul Stock Exchange (BIST 100) is proposed by analyzing nine banking stocks with high volume in BIST 100. Though English news articles have been employed for forecasting of market direction previously, to the best of our knowledge, Turkish news articles and user comments from social media and different platforms have not been utilized with the combination of deep learning techniques and word embedding methods to predict the direction of Turkish stocks and market. For this objective, long short-term memory networks, recurrent neural networks, convolutional neural networks as deep learning algorithms and Word2Vec, GloVe, and FastText as word embedding models are evaluated. To demonstrate the effectiveness of proposed model, four different sources of Turkish news are collected. The news articles about stocks from Public Disclosure Platform (KAP), text-based technical analysis of each stock from Bigpara, user comments from both Twitter and Mynet Finans platforms are gathered. Experiment results demonstrate that the combination of deep learning techniques and word embedding methods have a great potential to predict the direction of BIST 100. INDEX TERMS Deep learning techniques, financial sentiment analysis, stock market prediction, word embedding methods This work is licensed under a Creative Commons Attribution 4.

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Section: Literature Reviewmentioning

confidence: 99%

An Efficient Word Embedding and Deep Learning Based Model to Forecast the Direction of Stock Exchange Market Using Twitter and Financial News Sites: A Case of Istanbul Stock Exchange (BIST 100)

Kilimci¹,

Duvar²

2020

IEEE Access

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“…Deep learning has also been incorporated in long-short-term memory neural networks for financial market predictions by Fischer and Krauss [42] where day returns are calculated for each day at defined stocks. Hasan et al [43] investigate how to apply hierarchical deep learning models for the problems in finance such as stock market prediction and classification; the deep learning models are based on neural networks, recurrent neural networks with big data finance datasets.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Genetic and deep learning clusters based on neural networks for management decision structures

Serrano

2019

Neural Comput & Applic

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Judgments are taken in a structured way; both human and business management decisions involve a hierarchical process that requires a level of compromise between risk, cost, reward, experience and knowledge. This article proposes a management decision structure that emulates the human brain approach based on genetic and deep learning cluster algorithms and the random neural network. Reinforcement learning takes quick and specific local decisions, deep learning clusters enables identity and memory, and deep learning management clusters make final strategic decisions. The presented genetic algorithm transmits the learned information to future generations in the network weights rather than the neurons. Because the subject's information, a combination of memory, identity and decision data, is never lost but transmitted, the genetic algorithm provides immortality. The management decision structure has been applied and validated in a smart investment Fintech application: an intelligent banker that makes buy and sell asset decisions with an associated market and risk that entirely transmits itself to a future generation. Results are rewarding; the management decision structure with genetics and machine learning based on the random neural network algorithm that emulates the human brain and biology transmits information to future generations and learns autonomously, gradually and continuously while adapting to the environment. Keywords Genetic learning Á Deep learning clusters Á Reinforcement learning Á Random neural network Á Fintech Á Smart investment & Will Serrano

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“…Machine learning has been applied to solve nonlinear models in continuous time in economics and finance by Duarte, V. [24] and forecasting the volatility of asset prices by Stefani, J. et al [25]. Deep Learning has also recently incorporated in long short term memory Neural Networks for financial market predictions by Fischer, T. et al [26] and Hasan, A. et al [27].…”

Section: Related Workmentioning

confidence: 99%

The Random Neural Network with a Genetic Algorithm and Deep Learning Clusters in Fintech: Smart Investment

Serrano¹

2018

IFIP Advances in Information and Communication Technology

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This paper presents the Random Neural Network in a Deep Learning Cluster structure with a new learning algorithm based on the genetics according to the genome model, where information is transmitted in the combination of genes rather than the genes themselves. The proposed genetic model transmits information to future generations in the network weights rather than the neurons. The innovative genetic algorithm is implanted in a complex deep learning structure that emulates the human brain: Reinforcement Learning takes fast local current decisions, Deep Learning Clusters provide identity and memory, Deep Learning Management Clusters take final strategic decisions and finally Genetic Learning transmits the information learned to future generations. This proposed structure has been applied and validated in Fintech; a Smart Investment application: an Intelligent Banker that performs Buy and Sell decisions on several Assets with an associated market and risk. Our results are promising; we have connected the human brain and genetics with Machine Learning based on the Random Neural Network model where biology; similar as Artificial Intelligence is learning gradually and continuously while adapting to the environment.

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Predicting financial market in big data: Deep learning

Cited by 19 publications

References 0 publications

An Efficient Word Embedding and Deep Learning Based Model to Forecast the Direction of Stock Exchange Market Using Twitter and Financial News Sites: A Case of Istanbul Stock Exchange (BIST 100)

An Efficient Word Embedding and Deep Learning Based Model to Forecast the Direction of Stock Exchange Market Using Twitter and Financial News Sites: A Case of Istanbul Stock Exchange (BIST 100)

Genetic and deep learning clusters based on neural networks for management decision structures

The Random Neural Network with a Genetic Algorithm and Deep Learning Clusters in Fintech: Smart Investment

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