A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Filters
Green Bond Index Prediction Based on CEEMDAN-LSTM
2022
Frontiers in Energy Research
Preserved Fulltext
In this paper, a hybrid model combining CEEMDAN and LSTM is introduced to predict green bond market in China (represented by CUFE-CNI High Grade Green Bond Index). ...
Meanwhile, we find that indices from the crude oil market and green stock market are both effective predictors, which also provides ground on the correlations between the green bond market and other financial ...
In that case, they are sure to become important financial tools in the future. Therefore, it is of great significance to study the returns and volatility of the green bond market. ...
doi:10.3389/fenrg.2021.793413
fatcat:5pdq4ahnhbfuhna3lak2567oxm
Deep Kernel Gaussian Process Based Financial Market Predictions
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this paper, grid search algorithm, used for performing hyper-parameter optimization, is integrated with GP-LSTM to predict both the conditional mean and volatility of stock returns, which are then combined ...
Based on empirical results, we find that the GP-LSTM model can provide more accurate forecasts in stock returns and volatility, which are jointly evaluated by the performance of constructed portfolios. ...
However, the studies for volatility prediction utilizing ANN or SVR usually choose a proxy variable as an approximation for the real volatility (variance of the conditional return). ...
arXiv:2105.12293v1
fatcat:7v5stgyrpba63d2dvdk7scmobm
Prediction-based portfolio optimization models using deep neural networks
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
This paper applies component stocks of China securities 100 index in Chinese stock market as experimental data. ...
These models first use DNNs to predict each stock's future return. Then, predictive errors of DNNs are applied to measure the risk of each stock. ...
By using the A-share market of China as experimental data, the results showed that the proposed model outperformed traditional methods, which suggested the promising effect of stock prediction for stock ...
doi:10.1109/access.2020.3003819
fatcat:diescpf6yrf2rebcyc2cmazpba
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. ...
Two stock market prediction tasks, namely the short-term stock movement prediction and stock crises early warning, are implemented in the framework of the attention-based Long Short Term-Memory (LSTM) ...
KSF-A-14). ...
arXiv:2005.11706v2
fatcat:svx2tnvwivaapfysn4ybmvfyaq
Incorporating Research Reports and Market Sentiment for Stock Excess Return Prediction: A Case of Mainland China
2020
Scientific Programming
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The prediction of stock excess returns is an important research topic for quantitative trading, and stock price prediction based on machine learning is receiving more and more attention. ...
This article takes the data of Chinese A-shares from July 2014 to September 2017 as the research object, and proposes a method of stock excess return forecasting that combines research reports and investor ...
Introduction Stock price prediction is a method of predicting stock prices in the future based on stock price information at the past or current moment. ...
doi:10.1155/2020/8894757
fatcat:hw7h4dko6nenth3ezx5zhg2qlu
Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 pre-print arXiv:2107.14033v1 fatcat:sksa3ppmpnhh5dorhfmuegvhbu
In this paper, we present a collaborative temporal-relational modeling framework for end-to-end stock trend prediction. ...
Predicting the future price trends of stocks is a challenging yet intriguing problem given its critical role to help investors make profitable decisions. ...
ACKNOWLEDGEMENT We would like to thank the authors of the works [8] , [12] , and [13] for sharing their codes. ...
arXiv:2107.14033v2
fatcat:piqqzuv7sreejgtbndjyxa2zqm
CSI 300 Prediction Using LSTM Model
2021
BCP Business & Management
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
common variables include open, close, high and low price) will have a better result; The result is also compared with predictions using SVR and the GBDT model, and the MSE of the LSTM and SVR test are ...
LSTM is used in the article to forecast CSI 300, and consider if adding volume (the number of shares transacted every day) and p_change (amount of increase and amount of decrease) on the basis test (the ...
But the stock market in China is extremely volatile and instability due to some policies or other reasons, making it difficult for shareholders to invest in stocks for profit. ...
doi:10.54691/bcpbm.v13i.102
fatcat:ybdtatemofb2bkcq2yiskwdqou
Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction
2018
Sustainability
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
This research investigates the temporal property of stock market data by suggesting a systematic method to determine the time window size and topology for the LSTM network using GA. ...
Accordingly, this study intends to develop a novel stock market prediction model using the available financial data. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/su10103765
fatcat:7nvn6c74rjdudey67jpfmhqota
Recent Advances in Stock Market Prediction Using Text Mining: A Survey
[chapter]
2020
E-Business [Working Title]
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In contrast to the other current review articles that concentrate on discussing many methods used for forecasting the stock market, this study aims to compare many machine learning (ML) and deep learning ...
(DL) methods used for sentiment analysis to find which method could be more effective in prediction and for which types and amount of data. ...
Section 4 includes a review of the machine learning main methods used for stock market prediction based on textual resources. ...
doi:10.5772/intechopen.92253
fatcat:ptioootolbg4fb5kzuovz2af5m
Predicting the direction of US stock prices using effective transfer entropy and machine learning techniques
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
This study aims to predict the direction of US stock prices by integrating time-varying effective transfer entropy (ETE) and various machine learning algorithms. ...
Meanwhile, we suggest utilizing the adjusted accuracy derived from the risk-adjusted return in finance as a prediction performance measure. ...
In particular, research has been actively conducted to analyze and predict the stock market based on relationships among the dynamics of stock prices and returns. ...
doi:10.1109/access.2020.3002174
fatcat:xmpfuhavlbbipgtrtor7dkhlgi
Important Trading Point Prediction Using a Hybrid Convolutional Recurrent Neural Network
2021
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Our proposed method ITPP-HCRNN achieves an annualized return that is 278.46% more than that of the market. ...
Inspired by the process of human stock traders looking for trading opportunities, we propose a deep learning framework based on a hybrid convolutional recurrent neural network (HCRNN) to predict the important ...
Based on these principles, this study proposes a new deep learning framework-a hybrid convolutional recurrent neural network (HCRNN)-for important trading point prediction in the stock market. ...
doi:10.3390/app11093984
fatcat:bzzxwzxwlbaxbpj3sovnb3iuqi
Research on Stock Price Time Series Prediction Based on Deep Learning and Autoregressive Integrated Moving Average
2022
Scientific Programming
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Therefore, our proposed method provides theoretical support and method reference for investors about stock trading in China stock market. ...
In the field of finance, machine learning is mainly used to study the future trend of capital market price. ...
. erefore, in our study, we will make a combination prediction based on ARIMA and LSTM, respectively, for different characteristics of stock market data, in order to obtain better prediction effect. ...
doi:10.1155/2022/4758698
fatcat:pp3y6ucygzc7fcbraglbpazuzu
A Labeling Method for Financial Time Series Prediction Based on Trends
2020
Entropy
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification ...
The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data. ...
There have been a lot of studies on the prediction of financial market. In particular, we focus on the efficient market hypothesis [7] . There are many studies on market efficiency [8] . ...
doi:10.3390/e22101162
pmid:33286931
pmcid:PMC7597331
fatcat:zj6lk5hsarb67pw54bgigyplsy
Applications of deep learning in stock market prediction: recent progress
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Hence, our motivation for this survey is to give a latest review of recent works on deep learning models for stock market prediction. ...
Stock market prediction has been a classical yet challenging problem, with the attention from both economists and computer scientists. ...
We give a general workflow for stock market prediction, based on which the previous studies can be easily classified and summarized. ...
arXiv:2003.01859v1
fatcat:rdwsi5xpozfyjeg7oatfzwbg7a
Stock Price Prediction Based on Natural Language Processing1
2022
Complexity
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
This study designs a new text mining method for keywords augmentation based on natural language processing models including Bidirectional Encoder Representation from Transformers (BERT) and Neural Contextualized ...
The keywords used in traditional stock price prediction are mainly based on literature and experience. ...
, securities, securities market, equity, a shares, A-share market, Hong Kong stocks, deposit rates, finance, GDP, CPI, bull market, rate of return, fund company, inflation rate, market conditions, rise ...
doi:10.1155/2022/9031900
fatcat:qstlgv5dnre2jbcird3t2l6pou
« Previous
Showing results 1 — 15 out of 383 results