Deep learning for Stock Market Prediction

• URL: http://arxiv.org/abs/2004.01497v1
• Date: Tue, 31 Mar 2020 22:50:01 GMT
• Title: Deep learning for Stock Market Prediction
• Authors: Mojtaba Nabipour, Pooyan Nayyeri, Hamed Jabani, Amir Mosavi
• Abstract summary: This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prediction of stock groups' values has always been attractive and challenging for shareholders. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms utilized for prediction of future values of stock market groups. We employed Decision Tree, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting and eXtreme Gradient Boosting (XGBoost), and Artificial neural network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all the algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.

Related papers

• Phikon-v2, A large and public feature extractor for biomarker prediction [42.52549987351643]
  We train a vision transformer using DINOv2 and publicly release one iteration of this model for further experimentation, coined Phikon-v2. While trained on publicly available histology slides, Phikon-v2 surpasses our previously released model (Phikon) and performs on par with other histopathology foundation models (FM) trained on proprietary data.
  arXiv  Detail & Related papers  (2024-09-13T20:12:29Z)
• GraphCNNpred: A stock market indices prediction using a Graph based deep learning system [0.0]
  We give a graph neural network based convolutional neural network (CNN) model, that can be applied on diverse source of data, in the attempt to extract features to predict the trends of indices of textS&textP 500, NASDAQ, DJI, NYSE, and RUSSEL. Experiments show that the associated models improve the performance of prediction in all indices over the baseline algorithms by about $4% text to 15%$, in terms of F-measure.
  arXiv  Detail & Related papers  (2024-07-04T09:14:24Z)
• Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms [3.2732602885346576]
  This research paper compares four machine learning models and their accuracy in forecasting three well-known stocks traded in the NYSE from March 2020 to May 2022. We deploy, develop, and tune XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression models. Using a training data set of 240 trading days, we find that XGBoost gives the highest accuracy despite running longer.
  arXiv  Detail & Related papers  (2023-05-17T07:04:32Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Boosted Dynamic Neural Networks [53.559833501288146]
  A typical EDNN has multiple prediction heads at different layers of the network backbone. To optimize the model, these prediction heads together with the network backbone are trained on every batch of training data. Treating training and testing inputs differently at the two phases will cause the mismatch between training and testing data distributions. We formulate an EDNN as an additive model inspired by gradient boosting, and propose multiple training techniques to optimize the model effectively.
  arXiv  Detail & Related papers  (2022-11-30T04:23:12Z)
• Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction [7.231134145443057]
  This paper proposes an attention-based CNN-LSTM and XGBoost hybrid model to predict the stock price. The model can fully mine the historical information of the stock market in multiple periods. The results show that the hybrid model is more effective and the prediction accuracy is relatively high.
  arXiv  Detail & Related papers  (2022-04-06T07:06:30Z)
• Machine Learning for Stock Prediction Based on Fundamental Analysis [13.920569652186714]
  We investigate three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) RF model achieves the best prediction results, and feature selection is able to improve test performance of FNN and ANFIS. Our findings demonstrate that machine learning models could be used to aid fundamental analysts with decision-making regarding stock investment.
  arXiv  Detail & Related papers  (2022-01-26T18:48:51Z)
• Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study [100.27567794045045]
  Training deep graph neural networks (GNNs) is notoriously hard. We present the first fair and reproducible benchmark dedicated to assessing the "tricks" of training deep GNNs.
  arXiv  Detail & Related papers  (2021-08-24T05:00:37Z)
• Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction [45.74513775015998]
  We present a collaborative temporal-relational modeling framework for end-to-end stock trend prediction. A novel hypergraph tri-attention network (HGTAN) is proposed to augment the hypergraph convolutional networks. In this manner, HGTAN adaptively determines the importance of nodes, hyperedges, and hypergraphs during the information propagation among stocks.
  arXiv  Detail & Related papers  (2021-07-22T02:16:09Z)
• How Powerful are Performance Predictors in Neural Architecture Search? [43.86743225322636]
  We give the first large-scale study of performance predictors by analyzing 31 techniques. We show that certain families of predictors can be combined to achieve even better predictive power.
  arXiv  Detail & Related papers  (2021-04-02T17:57:16Z)
• Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning [3.42658286826597]
  Post-Earnings-Announcement Drift (PEAD) is one of the most studied stock market anomalies. We use a machine learning based approach instead, and aim to capture the PEAD dynamics using data from a large group of stocks.
  arXiv  Detail & Related papers  (2020-09-07T13:27:06Z)

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.