2020
DOI: 10.1016/j.procs.2020.09.241
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Liquidity prediction on Vietnamese stock market using deep learning

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Cited by 9 publications
(6 citation statements)
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“…Therefore, the more rapid the development of financial markets, the more prosperous we face financial risks. Financial risks are generally characterized by uncertainty, objectivity, and potential [ 20 25 ]. The essential feature of financial risk is risk uncertainty, which refers to the negative effects produced in the process of financial activities and the difficulty of fully predicting and mastering various factors in advance Objectivity means that there is a certain necessity for the occurrence of financial risks.…”
Section: The Basic Theory Of Financial Marketmentioning
confidence: 99%
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“…Therefore, the more rapid the development of financial markets, the more prosperous we face financial risks. Financial risks are generally characterized by uncertainty, objectivity, and potential [ 20 25 ]. The essential feature of financial risk is risk uncertainty, which refers to the negative effects produced in the process of financial activities and the difficulty of fully predicting and mastering various factors in advance Objectivity means that there is a certain necessity for the occurrence of financial risks.…”
Section: The Basic Theory Of Financial Marketmentioning
confidence: 99%
“…e most common and important classification is that financial markets are divided into monetary markets and capital markets according to the duration of financial transactions, and both can be subdivided into smaller markets [17][18][19][20].…”
Section: 1mentioning
confidence: 99%
“…Liquidity sanctions investors and issuers to meet their quotas regarding investment, financing or hedging, reducing investment costs and the cost of capital experimental results, it can be concluded that the LSTM model allows for prediction characterized by lowest value of mean square error (MSE). The aim of [6] is to develop the machine learning models for liquidity prediction. The subject of research is the Vietnamese stock market, focusing on the recent yearsfrom 2011 to 2019.…”
Section: Liquidity Prediction For Learning Modelsmentioning
confidence: 99%
“…Following baselines are used in this model: Sentiment, Event Tensor, Event Tensor-CS, TGT-CTX-LSTM, Conditional Encoding, TGT-HN, TGT-HAN, GCN, GAT. The paper[6] covers the sample period from January 2011 to December 2019, which contains 2,242 trading days. The eligible stocks in the sample consist of 378 companies in two stock exchanges, which included 179 stocks on the HOSE and 199 stocks on the HNX.…”
mentioning
confidence: 99%
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