Short-term stock market price trend prediction using a comprehensive deep learning system

Shen, Jingyi; Shafiq, Omair

doi:10.1186/s40537-020-00333-6

Cited by 225 publications

(80 citation statements)

References 43 publications

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“…The rapid development of machine learning models tools and technologies always provides opportunities for the researcher to find the hidden truths of the market and analyse the market in their own ways [14]. Identification of proper feature selection increase the performance of prediction of machine learning models [15]. Only a few studies have attempted to identify significant input features [16].…”

Section: The Inspiration Is As Per the Followingmentioning

confidence: 99%

A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators

et al. 2021

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People continuously hunt for a precise and productive strategy to control the stock exchange because the monetary trade is recognised for its unbelievably different character and unpredictability. Even a minor gain in predicting performance will be extremely profitable and significant. Our novel study implemented six boosting techniques, i.e., XGBoost, AdaBoost, Gradient Boosting, LightGBM, CatBoost, and Histogram-based Gradient Boosting, and these boosting techniques were hybridised using a stacking framework to find out the direction of the stock market. Five different stock datasets were selected from four different countries and were used for our experiment. We used two-way overfitting protection during our model building process, i.e., dynamic reduction technique and cross-validation technique. For model evaluation purposes, we used the performance metrics, i.e., accuracy, ROC curve (AUC), F-score, precision, and recall. The aim of our study was to propose and select a predictive model whose training and testing accuracy difference was minimal in all stocks. The findings revealed that the meta-classifier Meta-LightGBM had training and testing accuracy differences that were very low among all stocks. As a result, a proper model selection might allow investors the freedom to invest in a certain stock in order to successfully control risk and create short-term, sustainable profits.

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Section: The Inspiration Is As Per the Followingmentioning

confidence: 99%

A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators

et al. 2021

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“…For example, the drop of the S&P 500 index in the USA during the COVID-19 crisis, where it lost one-third of its value during only one month [62]. Figure 11 shows the comparison between the drop of the S&P 500 index during the dotcom crisis (which peaked on March 24, 2000), the subprime crisis (peaked on Oct. 9,2007), and the COVID-19 crisis (peaked on Feb 19, 2020) [65]. As it can be seen from the figure, in March 2020, it took only one month for the S&P 500 to lose one-third of its value, while it took one year for the subprime crisis to decline the same amount and one year and a half for the dotcom bust.…”

Section: Comparison Of Ann-psocog With Ann Spsp Spsocog and Ann-spsomentioning

confidence: 99%

“…e traditional stock forecasting methods could not fit and analyze the highly nonlinear and multifactor stock market well, so there are low prediction accuracy and slow training speed problems [7]. erefore, recent studies such as fusion in stock market prediction [6], financial trading strategy system [8], and short-term stock market price trend predictions [9] try to find solutions to problems. Some of these problems are (1) inaccurate prediction and (2) taking a long time as a result of incorrectly selecting the hyperparameters values of the neural network.…”

Section: Introductionmentioning

confidence: 99%

A New ANN-Particle Swarm Optimization with Center of Gravity (ANN-PSOCoG) Prediction Model for the Stock Market under the Effect of COVID-19

Jamous

ALRahhal

El-Darieby

2021

Scientific Programming

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Since the declaration of COVID-19 as a pandemic, the world stock markets have suffered huge losses prompting investors to limit or avoid these losses. The stock market was one of the businesses that were affected the most. At the same time, artificial neural networks (ANNs) have already been used for the prediction of the closing prices in stock markets. However, standalone ANN has several limitations, resulting in the lower accuracy of the prediction results. Such limitation is resolved using hybrid models. Therefore, a combination of artificial intelligence networks and particle swarm optimization for efficient stock market prediction was reported in the literature. This method predicted the closing prices of the shares traded on the stock market, allowing for the largest profit with the minimum risk. Nevertheless, the results were not that satisfactory. In order to achieve prediction with a high degree of accuracy in a short time, a new improved method called PSOCoG has been proposed in this paper. To design the neural network to minimize processing time and search time and maximize the accuracy of prediction, it is necessary to identify hyperparameter values with precision. PSOCoG has been employed to select the best hyperparameters in order to construct the best neural network. The created network was able to predict the closing price with high accuracy, and the proposed model ANN-PSOCoG showed that it could predict closing price values with an infinitesimal error, outperforming existing models in terms of error ratio and processing time. Using S&P 500 dataset, ANN-PSOCoG outperformed ANN-SPSO in terms of prediction accuracy by approximately 13%, SPSOCOG by approximately 17%, SPSO by approximately 20%, and ANN by approximately 25%. While using DJIA dataset, ANN-PSOCoG outperformed ANN-SPSO in terms of prediction accuracy by approximately 18%, SPSOCOG by approximately 24%, SPSO by approximately 33%, and ANN by approximately 42%. Besides, the proposed model is evaluated under the effect of COVID-19. The results proved the ability of the proposed model to predict the closing price with high accuracy where the values of MAPE, MAE, and RE were very small for S&P 500, GOLD, NASDAQ-100, and CANUSD datasets.

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“…Jingyi Shen and M. Omair Shafiq [16] proposed a unique customized LSTM model with comprehensive feature engineering procedure to forecast the stock market. Their feature engineering procedure included feature expansion approach, recursive fea-ture elimination and principle component analysis.…”

Section: Long Short Term Memory (Lstm)mentioning

confidence: 99%

A review of stock market predictions using deep learning models

Barwal¹,

Gupta²

2021

JUSST

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Stock trading has always been an appealing option in the world of financial market to make significant profits. As a popular saying goes, “Trading is not gambling”. It is true for the case of stock trading also. Successful traders always do a lot of research and analysis before buying and selling their stocks. This analysis usually includes looking at past record of a stock and finding patterns for future predictions. As machines are very good at processing huge amount of data and finding patterns from the data, we can see why the use of deep learning models can be beneficial in this case. In this paper, we are going to study various deep learning models used by researchers in the past to predict stock prices.

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Short-term stock market price trend prediction using a comprehensive deep learning system

Cited by 225 publications

References 43 publications

A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators

A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators

A New ANN-Particle Swarm Optimization with Center of Gravity (ANN-PSOCoG) Prediction Model for the Stock Market under the Effect of COVID-19

A review of stock market predictions using deep learning models

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