Liquidity prediction on Vietnamese stock market using deep learning

Khang, Pham Quoc; Hernes, Marcin; Kuziak, Katarzyna; Rot, Artur; Gryncewicz, Wiesława

doi:10.1016/j.procs.2020.09.241

Cited by 8 publications

(6 citation statements)

References 18 publications

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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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Deep Learning for Intelligent Assessment of Financial Investment Risk Prediction

Sun

2022

Computational Intelligence and Neuroscience

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Financial investment promotes the market’s fast economic growth and gradually becomes a new trend of social development in the contemporary era. From the national level, financial risk investment activities directly affect the development process of the information technology industry and social and economic benefits. The management of financial risk investment has received more attention, and the types and difficulties of risks are also gradually increasing. Financial regulatory agencies urgently need to establish a sensitive and scientific economic hazard early alarm system. The perfect earlier alarm system stands based on in-depth scientific theoretical research, so studying financial security evaluation and systemic economic earlier alarm systems is of great practical significance. Taking the systemic financial risk as the research object, this paper analyzes the mechanism of financial systemic risk. After that, deep learning technology in financial investment has been used for the first time to reconstruct the index system of financial security evaluation and early warning. The application of deep learning technology in the early warning of systemic financial risks is realized, which provides a reliable basis for the regulatory authorities to build a financial risk early warning system and makes empirical research.

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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%

Deep Learning for Intelligent Assessment of Financial Investment Risk Prediction

Sun

2022

Computational Intelligence and Neuroscience

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“…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.…”

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confidence: 99%

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Stock Market Prediction using Natural Language Processing -a Survey

mane¹,

kandasamy²

2022

Computer Science and Information Technology Trends

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The stock market is a network which provides a platform for almost all major economic transactions. While investing in the stock market is a good idea, investing in individual stocks may not be, especially for the casual investor. Smart stock-picking requires in-depth research and plenty of dedication. Predicting this stock value offers enormous arbitrage profit opportunities. This attractiveness of finding a solution has prompted researchers to find a way past problems like volatility, seasonality, and dependence on time. This paper surveys recent literature in the domain of natural language processing and machine learning techniques used to predict stock market movements. The main contributions of this paper include the sophisticated categorizations of many recent articles and the illustration of the recent trends of research in stock market prediction and its related areas.

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A Comparative Study of Stock Market Prediction Models

Jain

2024

Deep Learning Tools for Predicting Stock Market Movements

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Liquidity prediction on Vietnamese stock market using deep learning

Cited by 8 publications

References 18 publications

Deep Learning for Intelligent Assessment of Financial Investment Risk Prediction

Deep Learning for Intelligent Assessment of Financial Investment Risk Prediction

Stock Market Prediction using Natural Language Processing -a Survey

A Comparative Study of Stock Market Prediction Models

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