Stock Price Prediction Using Artificial Intelligence: A Survey

Sonkiya, Priyank; Bajpai, Vikas; Bansal, Anukriti

doi:10.1109/i2ct54291.2022.9825415

Cited by 7 publications

(13 citation statements)

References 77 publications

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“…Regarding the technical aspect of future work, possible improvements can be realized using the combination of GANs and FinBERT (Sonkiya et al ., 2021). However, future avenues of this work may also involve analysis of filtered news, that is news that might possibly significantly improve the accuracy for predicting stock, i.e.…”

Section: Discussionmentioning

confidence: 99%

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Predicting stock market using natural language processing

Puh

Babac

2023

AJB

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PurposePredicting the stock market's prices has always been an interesting topic since its closely related to making money. Recently, the advances in natural language processing (NLP) have opened new perspectives for solving this task. The purpose of this paper is to show a state-of-the-art natural language approach to using language in predicting the stock market.Design/methodology/approachIn this paper, the conventional statistical models for time-series prediction are implemented as a benchmark. Then, for methodological comparison, various state-of-the-art natural language models ranging from the baseline convolutional and recurrent neural network models to the most advanced transformer-based models are developed, implemented and tested.FindingsExperimental results show that there is a correlation between the textual information in the news headlines and stock price prediction. The model based on the GRU (gated recurrent unit) cell with one linear layer, which takes pairs of the historical prices and the sentiment score calculated using transformer-based models, achieved the best result.Originality/valueThis study provides an insight into how to use NLP to improve stock price prediction and shows that there is a correlation between news headlines and stock price prediction.

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Section: Discussionmentioning

confidence: 99%

“…Recently, Sonkiya et al . (2021) proposed a state-of-the-art method for stock market price prediction.…”

Section: Review Of Literaturementioning

confidence: 99%

Predicting stock market using natural language processing

Puh

Babac

2023

AJB

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“…Liu YL, et al [7] addressed imbalanced text token distribution issues in convolutional neural network sentiment analysis for stock price prediction through a sentiment analysis and GAN-based approach. Furthermore, Sonkiya P, et al [8] proposed an ensemble approach incorporating sentiment analysis and GANs to predict stock prices and Asgarian S, et al [9] introduced three GAN-based models for market trend prediction by analyzing public sentiment. Polamuri SR, et al [10] innovatively applied reinforcement learning and Bayesian optimization, overcoming hyperparameter challenges in a hybrid prediction algorithm based on Generative Adversarial Networks (GAN-HPA).…”

Section: Related Researchmentioning

confidence: 99%

“…As articulated in Zhao T, et al [1], nonlinear prediction methods encompass approaches grounded in BP neural networks, support vector machines, recurrent neural networks, generative adversarial networks, and reinforcement learning. Among these, generative adversarial network-based prediction methods [2][3][4][5][6][7][8][9][10], trained through adversarial learning, stand out for their adaptability to the nonlinearity, instability, and complexity of the stock market. They generate data samples aligning more closely with actual market conditions, offering valuable support for stock prediction.…”

Section: Introductionmentioning

confidence: 99%

Improved Generative Adversarial Network Stock Price Prediction Method

2024

OAJDA

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In response to the challenges posed by the nonlinearity, instability, and complexity of the stock market in the insurance industry, we propose an enhanced generative adversarial neural network-based stock prediction model termed CAL-WGAN-GP. The model's generator incorporates components such as the CNN-BiLSTM model and a self-attention mechanism, employed to generate precise predictions for stock closing prices. The discriminator, comprising a multi-layer convolutional neural network, is tasked with distinguishing between the stock closing prices generated by the generator and actual stock closing prices. To assess the model's generalization capability, stock data from China Ping an, China Life, XinHua Insurance, and Pacific Insurance is selected. During the dataset construction, relevant features, including technical indicators, are incorporated to facilitate the model in better learning hidden data information. Experimental results demonstrate that CAL-WGAN-GP urpasses baseline models across four evaluation metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2), achieving the highest degree of data fitting

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“…The utility of sentiment analysis is vast, ranging from businesses assessing customer reviews and feedback to gauge public opinion, to social media platforms monitoring user content to understand prevailing attitudes and trends. [4,5] The challenge lies in the subtleties of human language, including sarcasm, irony, and context-dependent meanings, which can skew straightforward computational interpretations.…”

Section: Introductionmentioning

confidence: 99%

Domain Adaptation with Sentiment Domain Adapter

Carter,

Nasir,

Parker

2024

Preprint

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The field of domain adaptation, particularly in cross-domain sentiment classification, leverages labeled data from a source domain alongside unlabeled or sparsely labeled data from a target domain. The objective is to enhance performance in the target domain by mitigating the discrepancies in data distributions. Traditional methods in this area have focused on identifying and differentiating between pivotal sentiment words (shared across domains) and domain-specific sentiment words. In our work, we introduce a novel framework called Sentiment Domain Adapter (SDA), which incorporates a Category Attention Network (CAN) alongside a Convolutional Neural Network (CNN). This approach treats pivotal and domain-specific words as part of a collective category of attributes, which SDA learns to discern automatically, thereby enhancing domain adaptation. Additionally, SDA seeks to provide interpretative insights by learning these category attributes. Our model's optimization targets three main goals: (1) supervised classification accuracy, (2) minimizing the disparity in category feature distribution, and (3) maintaining domain invariance. Evaluations on three benchmark datasets for sentiment analysis affirm that SDA surpasses several established baselines.

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Stock Price Prediction Using Artificial Intelligence: A Survey

Cited by 7 publications

References 77 publications

Predicting stock market using natural language processing

Predicting stock market using natural language processing

Improved Generative Adversarial Network Stock Price Prediction Method

Domain Adaptation with Sentiment Domain Adapter

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