Stock Market Prediction via Deep Learning Techniques: A Survey

• URL: http://arxiv.org/abs/2212.12717v1
• Date: Sat, 24 Dec 2022 11:32:17 GMT
• Title: Stock Market Prediction via Deep Learning Techniques: A Survey
• Authors: Jinan Zou, Qingying Zhao, Yang Jiao, Haiyao Cao, Yanxi Liu, Qingsen Yan, Ehsan Abbasnejad, Lingqiao Liu, Javen Qinfeng Shi
• Abstract summary: This paper provides a structured overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models. In addition, we provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market.
• Score: 24.88558334340833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction.

Related papers

• Beyond Trend Following: Deep Learning for Market Trend Prediction [49.89480853499917]
  We advocate for the use of Artificial Intelligence and Machine Learning techniques to predict future market trends. These predictions, when done properly, can improve the performance of asset managers by increasing returns and reducing drawdowns.
  arXiv  Detail & Related papers  (2024-06-10T11:42:30Z)
• BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights [0.0]
  We employ BERTopic, an advanced NLP technique, to analyze the sentiment of topics derived from stock market comments. Our methodology integrates this sentiment analysis with various deep learning models, renowned for their effectiveness in time series and stock prediction tasks. The results indicate that topics in stock market comments provide implicit, valuable insights into stock market volatility and price trends.
  arXiv  Detail & Related papers  (2024-04-02T15:50:10Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Stock Market Prediction using Natural Language Processing -- A Survey [5.669677041792239]
  This paper surveys recent literature in the domain of natural language processing and machine learning techniques used to predict stock market movements. The main contributions of this paper include the sophisticated categorizations of many recent articles and the illustration of the recent trends of research in stock market prediction and its related areas.
  arXiv  Detail & Related papers  (2022-08-26T10:36:48Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information [73.40830291141035]
  Several methods were recently proposed to mine the shared information through stock concepts extracted from the Web to improve the forecasting results. Previous work assumes the connections between stocks and concepts are stationary, and neglects the dynamic relevance between stocks and concepts. We propose a novel stock trend forecasting framework that can adequately mine the concept-oriented shared information from predefined concepts and hidden concepts.
  arXiv  Detail & Related papers  (2021-10-26T14:04:04Z)
• A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments [0.0]
  The proposed novel model achieves remarkable results for stock market prediction task. In order to demonstrate the effectiveness of the proposed model, Garanti Bank (GARAN), Akbank (AKBNK), T"urkiye.Ics Bankasi (ISCTR) stocks in Istanbul Stock Exchange are used as a case study.
  arXiv  Detail & Related papers  (2021-07-02T09:39:41Z)
• Stock Market Analysis with Text Data: A Review [7.789019365796933]
  Stock market movements are influenced by public and private information shared through news articles, company reports, and social media discussions. The majority of studies in the literature are based on traditional approaches that come short in analyzing unstructured, vast textual data. This study is to survey the main stock market analysis models, text representation techniques for financial market prediction, shortcomings of existing techniques, and propose promising directions for future research.
  arXiv  Detail & Related papers  (2021-06-23T04:31:56Z)
• Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders [47.32228513808444]
  We present a realistic scenario in which an attacker influences algorithmic trading systems by using adversarial learning techniques. We show that when added to the input stream, our perturbation can fool the trading algorithms at future unseen data points.
  arXiv  Detail & Related papers  (2020-10-19T06:28:05Z)
• Applications of deep learning in stock market prediction: recent progress [5.780772209241294]
  This survey is to give a latest review of recent works on deep learning models for stock market prediction. We not only category the different data sources, various neural network structures, and common used metrics, but also the implementation and evaluation. Our goal is to help the interested researchers to synchronize with the latest progress and also help them to easily reproduce the previous studies as baselines.
  arXiv  Detail & Related papers  (2020-02-29T03:37:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.