Applications of deep learning in stock market prediction: recent progress

• URL: http://arxiv.org/abs/2003.01859v1
• Date: Sat, 29 Feb 2020 03:37:34 GMT
• Title: Applications of deep learning in stock market prediction: recent progress
• Authors: Weiwei Jiang
• Abstract summary: 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.
• Score: 5.780772209241294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stock market prediction has been a classical yet challenging problem, with the attention from both economists and computer scientists. With the purpose of building an effective prediction model, both linear and machine learning tools have been explored for the past couple of decades. Lately, deep learning models have been introduced as new frontiers for this topic and the rapid development is too fast to catch up. Hence, our motivation for 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 evaluation metrics, but also the implementation and reproducibility. 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. Base on the summary, we also highlight some future research directions in this topic.

Related papers

• Human Action Anticipation: A Survey [86.415721659234]
  The literature on behavior prediction spans various tasks, including action anticipation, activity forecasting, intent prediction, goal prediction, and so on. Our survey aims to tie together this fragmented literature, covering recent technical innovations as well as the development of new large-scale datasets for model training and evaluation.
  arXiv  Detail & Related papers  (2024-10-17T21:37:40Z)
• State-Space Modeling in Long Sequence Processing: A Survey on Recurrence in the Transformer Era [59.279784235147254]
  This survey provides an in-depth summary of the latest approaches that are based on recurrent models for sequential data processing. The emerging picture suggests that there is room for thinking of novel routes, constituted by learning algorithms which depart from the standard Backpropagation Through Time.
  arXiv  Detail & Related papers  (2024-06-13T12:51:22Z)
• On the Resurgence of Recurrent Models for Long Sequences -- Survey and Research Opportunities in the Transformer Era [59.279784235147254]
  This survey is aimed at providing an overview of these trends framed under the unifying umbrella of Recurrence. It emphasizes novel research opportunities that become prominent when abandoning the idea of processing long sequences.
  arXiv  Detail & Related papers  (2024-02-12T23:55:55Z)
• A Survey of Deep Learning and Foundation Models for Time Series Forecasting [16.814826712022324]
  Deep learning has been successfully applied to many application domains, yet its advantages have been slow to emerge for time series forecasting. Foundation models with extensive pre-training allow models to understand patterns and acquire knowledge that can be applied to new related problems. There is ongoing research examining how to utilize or inject such knowledge into deep learning models.
  arXiv  Detail & Related papers  (2024-01-25T03:14:07Z)
• A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects [58.746820564288846]
  Route&Time Prediction (RTP) aims to estimate the future service route as well as the arrival time of a worker. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route&time prediction; (ii) model architecture, which encompasses sequence-based and graph-based models; and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement
  arXiv  Detail & Related papers  (2023-09-03T14:43:33Z)
• Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022 [6.05458608266581]
  Deep learning models are replacing traditional statistical and machine learning models for price forecasting tasks. This review delves deeply into deep learning-based forecasting models, presenting information on model architectures, practical applications, and their respective advantages and disadvantages. The present contribution also includes potential directions for future research, such as examining the effectiveness of deep learning models with complex structures for price forecasting.
  arXiv  Detail & Related papers  (2023-04-21T03:46:09Z)
• Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectives [3.553493344868414]
  This bulletin is based on the summary of the author's dissertation. Deep learning models have much higher prediction performance than traditional machine learning models. The specific prediction process is still difficult to interpret and/or explain.
  arXiv  Detail & Related papers  (2023-03-24T16:24:08Z)
• Deep networks for system identification: a Survey [56.34005280792013]
  System identification learns mathematical descriptions of dynamic systems from input-output data. Main aim of the identified model is to predict new data from previous observations. We discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks.
  arXiv  Detail & Related papers  (2023-01-30T12:38:31Z)
• Stock Market Prediction via Deep Learning Techniques: A Survey [24.88558334340833]
  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.
  arXiv  Detail & Related papers  (2022-12-24T11:32:17Z)
• Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification [83.23129279407271]
  We propose a method to efficiently retain the knowledge available in a neural network pre-trained on a set of securities. In our method, the prior knowledge encoded in a pre-trained neural network is maintained by keeping existing connections fixed. This knowledge is adjusted for the new securities by a set of augmented connections, which are optimized using the new data.
  arXiv  Detail & Related papers  (2022-07-23T18:54:10Z)
• Deep Learning for Road Traffic Forecasting: Does it Make a Difference? [6.220008946076208]
  This paper focuses on critically analyzing the state of the art in what refers to the use of Deep Learning for this particular ITS research area. A posterior critical analysis is held to formulate questions and trigger a necessary debate about the issues of Deep Learning for traffic forecasting.
  arXiv  Detail & Related papers  (2020-12-02T15:56:11Z)

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.