Machine learning for liquidity prediction on Vietnamese stock market
Pham Quoc Khang,Klaudia Kaczmarczyk,Piotr Tutak,Paweł Golec,Katarzyna Kuziak,Radosław Depczyński,Marcin Hernes,Artur Rot +7 more
TLDR
In this article, the authors developed the machine learning models for predicting the stock market liquidity in the Vietnamese stock market, focusing on the recent years from 2011 to 2019, and the results of research can be used for developing the methods for decision support on stock markets.About:
This article is published in Procedia Computer Science.The article was published on 2021-01-01 and is currently open access. It has received 4 citations till now. The article focuses on the topics: Market liquidity & Stock market.read more
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The Role of Algorithmic Trading in Stock Liquidity and Commonality in Electronic Limit Order Markets
TL;DR: In this article, the authors investigated the effect of algorithmic trading on stock market liquidity and commonality in liquidity in different market conditions in an electronic limit order market, and they found that algorithmic trade increases stock liquidity by narrowing quoted and effective bid-ask spreads.
Journal ArticleDOI
ARIMA vs LSTM on NASDAQ stock exchange data
TL;DR: In this paper , the results of two completely different models: statistical one (ARIMA) and deep learning one (LSTM) based on a chosen set of NASDAQ data were compared using the relative metric mean square error and mean absolute percentage error (MAPE).
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A Hybrid Approach of Wavelet Transform, Convolutional Neural Networks and Gated Recurrent Units for Stock Liquidity Forecasting
TL;DR: In this article , a hybrid approach of Wavelet Transform, Convolutional Neural Networks, and Gated Recurrent Units is proposed to predict stock liquidity in the Casablanca Stock Exchange from 2000 to 2021.
References
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Journal ArticleDOI
Illiquidity and stock returns: cross-section and time-series effects $
TL;DR: In this article, the authors show that expected market illiquidity positively affects ex ante stock excess return, suggesting that expected stock ex ante excess return partly represents an illiquid price premium, which complements the cross-sectional positive return-illiquidity relationship.
Book ChapterDOI
Market Liquidity: Illiquidity and Stock Returns Cross-Section and Time-Series Effects＊
TL;DR: In this paper, the effects of stock illiquidity on stock return have been investigated and it was shown that expected market illiquidities positively affects ex ante stock excess return (usually called risk premium) over time.
Journal ArticleDOI
Does sample size matter in qualitative research?: A review of qualitative interviews in IS research
TL;DR: Little or no rigor for justifying sample size was shown for virtually all of the IS studies in this dataset, implying the subjective nature of sample size in qualitative IS studies.
Proceedings ArticleDOI
A neural network model for bankruptcy prediction
Marcus D. Odom,Ramesh Sharda +1 more
TL;DR: A comparison of the predictive abilities of both the neural network and the discriminant analysis method for bankruptcy prediction shows that neural networks might be applicable to this problem.
Journal ArticleDOI
Bankruptcy prediction using neural networks
Rick L. Wilson,Ramesh Sharda +1 more
TL;DR: The study indicates that neural networks perform significantly better than discriminant analysis at predicting firm bankruptcies, and implications for the accounting professional, neural networks researcher and decision support system builders are highlighted.