Machine learning for liquidity prediction on Vietnamese stock market

Khang, Pham Quoc; Kaczmarczyk, Klaudia; Tutak, Piotr; Golec, Paweł; Kuziak, Katarzyna; Depczyński, Radosław; Hernes, Marcin; Rot, Artur

doi:10.1016/j.procs.2021.09.132

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…Therefore, he excels in modeling financial data in the stock market. For instance, one study uses a sample of 179 stocks from the HOSE, produces MLP findings, and the model with five hidden layers yields the best result of 0.0268, as shown in Table 1 [6].…”

Section: Basic Descriptions Of Neural Networkmentioning

confidence: 99%

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Prediction of Stock Price by Neural Network Based on CNN, LSTM, ANN

Wen

2024

AEMPS

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The stock market's complexity and volatility have long posed significant challenges for prediction models. This study delves into the comparative efficacy of three machine learning models, i.e., Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANN), in forecasting stock prices. Empirical evidence from a range of research papers is synthesized in the study, which emphasizes the advantages and limitations of each model when applied to the analysis of financial time series. CNNs, while adept at feature extraction, may not fully leverage the temporal dynamics of stock data. LSTMs, with their ability to capture long-term dependencies, have shown a propensity for enhanced prediction accuracy, yet they are computationally intensive. ANNs, despite their simplicity, offer a robust baseline for comparison but often fall short in capturing intricate market patterns. Based on the analysis, LSTMs generally outperform the other models, suggesting their suitability for complex financial forecasting tasks. The decision to select a model should be dictated by the demands of the forecasting endeavor, encompassing available computational assets and the preferred equilibrium between precision and intricacy. This investigation emphasizes the critical role that model choice plays in achieving dependable prognostications of stock prices and lays down a groundwork for future studies aiming to merge these models with a broader spectrum of financial metrics.

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Section: Basic Descriptions Of Neural Networkmentioning

confidence: 99%

“…Results of MLP for HOSE[6]. CNNs) provide an advanced method for leveraging the temporal patterns in financial data for stock price prediction.…”

mentioning

confidence: 99%

Prediction of Stock Price by Neural Network Based on CNN, LSTM, ANN

Wen

2024

AEMPS

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“…Tree ensembles typically outperform them for traditional ML issues. Furthermore, they are computationally intensive to train and require significantly more expertise to tune (i.e., setting the architecture and hyper-parameters) [37].…”

Section: Introductionmentioning

confidence: 99%

Accurate Wheat Yield Prediction Using Machine Learning and Climate-NDVI Data Fusion

Ashfaq,

Khan,

Alzahrani

et al. 2024

IEEE Access

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Due to exponential population growth, climate change, and an increasing demand for food, there is an unprecedented need for a timely, precise, and dependable assessment of crop yield on a large scale. Wheat, a staple crop worldwide, requires accurate and prompt prediction of its output for global food security. Traditionally, the development of empirical models for crop yield forecasting has relied on climate data, satellite data, or a combination of both. Despite the enhanced performance achieved by integrating satellite and climate data, the contributions from various sources (Climate, Soil, Socioeconomic, and Remote sensing) remain unclear. The lack of well-defined comparisons between the performance of regression-based approaches and different Machine Learning (ML) methods in yield prediction necessitates further investigation. This study addresses the gaps by combining data from multiple sources to forecast wheat yield in the Multan region in the Punjab province of Pakistan. The findings are compared to the benchmark provided by Crop Report Services (CRS) Punjab, with three widely used ML techniques (support vector machine (SVM), Random Forest (RF), and Least Absolute Shrinkage and Selection Operator (LASSO)) by integrating publicly available data within the GEE (Google Earth Engine) platform, including climate, satellite, soil properties, and spatial information data to develop alternative empirical models for yield prediction using data from 2017 to 2022, selecting the best attribute subset related to crop output. The data set of district-level simulated yields was analyzed with three Machin Learning models (SVM, RF, and LASSO) as a function of seasonal weather, satellite, and soil. The results indicate that combining all datasets using three ML algorithms achieves better yield prediction performance (R 2 : 0.74-0.88). Incorporating spatial information and other properties into benchmark models can improve the prediction from 0.08 to 0.12. Random forest outperformed the competitor models with a Root Mean Square Error (RMSE) of 0.05 q/ha and R 2 of 0.88. Comparative analysis shows that random forest with 97% and SVM with 93% yielded better results in the study area.

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“…The author in [16] compared linear regression model, multilayer perceptron, and Long Short Term Memory (LSTM). Based on daily data of companies listed on the Ho Chi Min and Hanoi Stock Exchanges in Vietnam from January 2011 to December 2019, the authors conclude that the LSTM model has the lowest Mean Square Error (MSE).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach of Wavelet Transform, Convolutional Neural Networks and Gated Recurrent Units for Stock Liquidity Forecasting

Houad¹,

Mestari²,

Bentaleb³

et al. 2022

IJACSA

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Stock liquidity forecasting is critical for investors, issuers, and financial market regulators. The object of this study is to propose a method capable of accurately predicting the liquidity of stocks. The few studies on stock liquidity forecasting have focused on single models such as Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors, the nonlinear autoregressive network with exogenous input, and Deep Learning. A new trend in forecasting which attempts to combine several approaches is emerging at the moment. Inspired by this new trend, we propose a hybrid approach of Wavelet Transform, Convolutional Neural Networks, and Gated Recurrent Units to predict stock liquidity. Our model is tested on daily data of companies listed on the Casablanca Stock Exchange from 2000 to 2021. Its forecasting performances are evaluated based on the Mean Absolute Error, the Root Mean Square Error, the Mean Absolute Percentage Error, Theil's U statistic, and the correlation coefficient. Finally, the outperformance of the proposed model is confirmed by comparison with other reference forecasting models. This study contributes to the enrichment of the field of prediction of financial risks and can constitute a framework of analysis allowing to help the stakeholders of the financial markets to forecast the liquidity of the actions.

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Machine learning for liquidity prediction on Vietnamese stock market

Cited by 9 publications

References 28 publications

Prediction of Stock Price by Neural Network Based on CNN, LSTM, ANN

Prediction of Stock Price by Neural Network Based on CNN, LSTM, ANN

Accurate Wheat Yield Prediction Using Machine Learning and Climate-NDVI Data Fusion

A Hybrid Approach of Wavelet Transform, Convolutional Neural Networks and Gated Recurrent Units for Stock Liquidity Forecasting

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