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Green Bond Index Prediction Based on CEEMDAN-LSTM

Jiaqi Wang, Jiulin Tang, Kun Guo
2022 Frontiers in Energy Research  
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]

Yong Shi, Wei Dai, Wen Long, Bo Li
2021 arXiv   pre-print
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

Yi-Lin Ma, Rui-Zhu Han, Wei-Zhong Wang
2020 IEEE Access  
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]

Ye Ma, Lu Zong, Peiwan Wang
2022 arXiv   pre-print
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

Huilin Song, Diyun Peng, Xin Huang
2020 Scientific Programming  
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]

Chaoran Cui, Xiaojie Li, Juan Du, Chunyun Zhang, Xiushan Nie, Meng Wang, Yilong Yin
2022 arXiv   pre-print
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

Jiaming Mai
2021 BCP Business & Management  
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

Hyejung Chung, Kyung-shik Shin
2018 Sustainability  
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]

Faten Subhi Alzazah, Xiaochun Cheng
2020 E-Business [Working Title]  
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

Sondo Kim, Seungmo Ku, Woojin Chang, Jae Wook Song
2020 IEEE Access  
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

Xinpeng Yu, Dagang Li
2021 Applied Sciences  
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

Daiyou Xiao, Jinxia Su, Hangjun Che
2022 Scientific Programming  
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

Dingming Wu, Xiaolong Wang, Jingyong Su, Buzhou Tang, Shaocong Wu
2020 Entropy  
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]

Weiwei Jiang
2020 arXiv   pre-print
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

Xiaobin Tang, Nuo Lei, Manru Dong, Dan Ma, Atila Bueno
2022 Complexity  
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
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