Logistics forum based prediction on stock index using intelligent data analysis and processing of online web posts

Jing-hua, Zhao; Sun, Na; Cheng, Wanyun

doi:10.1007/s12652-019-01520-x

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…Also, [ 9 ] the authors proposed a prediction model where the authors proposed a prediction model for stock price based on sentiment analysis of news. In 2019, [ 35 ] the authors proposed a model that extracts the relationship between online posts and stock movement. Finally, in [ 14 ] the authors proposed a model with a combination of specialized and crucial examination through the application of information science and machine learning methods for stock advertising forecast.…”

Section: Related Workmentioning

confidence: 99%

An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis

Sharaf

Hemdan

El‐Sayed

et al. 2022

Multimed Tools Appl

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The coronavirus is an irresistible virus that generally influences the respiratory framework. It has an effective impact on the global economy specifically, on the financial movement of stock markets. Recently, an accurate stock market prediction has been of great interest to investors. A sudden change in the stock movement due to COVID -19 appearance causes some problems for investors. From this point, we propose an efficient system that applies sentiment analysis of COVID-19 news and articles to extract the final impact of COVID-19 on the financial stock market. In this paper, we propose a stock market prediction system that extracts the stock movement with the COVID spread. It is important to predict the effect of these diseases on the economy to be ready for any disease change and protect our economy. In this paper, we apply sentimental analysis to stock news headlines to predict the daily future trend of stock in the COVID-19 period. Also, we use machine learning classifiers to predict the final impact of COVID-19 on some stocks such as TSLA, AMZ, and GOOG stock. For improving the performance and quality of future trend predictions, feature selection and spam tweet reduction are performed on the data sets. Finally, our proposed system is a hybrid system that applies text mining on social media data mining on the historical stock dataset to improve the whole prediction performance. The proposed system predicts stock movement for TSLA, AMZ, and GOOG with average prediction accuracy of 90%, 91.6%, and 92.3% respectively.

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Section: Related Workmentioning

confidence: 99%

An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis

Sharaf

Hemdan

El‐Sayed

et al. 2022

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…In [16], the influence between different machine learning algorithms and multi-feature methods in time series was studied. In [17], an algorithm was presented that combined artificial neural networks and genetic algorithms. In [18], a look-back period was used for accurate forecasting.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Stock Closing Prices using Transformer Learning

Mian

2023

Eng. Technol. Appl. Sci. Res.

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Predicting stock markets remains a critical and challenging task due to many factors, such as the enormous volume of generated price data, instant price data changes, and sensitivity to human sentiments, wars, and natural disasters. Since the previous three years of the COVID-19 pandemic, forecasting stock markets is more difficult, complex, and problematic for stock market analysts. However, technical analysts of the stock market and academic researchers are continuously trying to develop innovative and modern methods for forecasting stock market prices, using statistical techniques, machine learning, and deep learning-based algorithms. This study investigated a Transformer sequential-based approach to forecast the closing price for the next day. Ten sliding window timesteps were used to forecast next-day stock closing prices. This study aimed to investigate reliable techniques based on stock input features. The proposed Transformer-based method was compared with ARIMA, Long-Short Term Memory (LSTM), and Random Forest (RF) algorithms, showing its outstanding results on Yahoo Finance data, Facebook Intra data, and JPMorgan's Intra data. Each model was evaluated using Mean Absolute Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE).

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“…Li et al (2019) proposes an intrinsic image decomposition method based on depth learning and probability graph model, in order to extract image information more accurately. Zhao et al (2019) construct four logistics forums metrics on the degree of busyness and emotional states of the logistics staffs based through hypertext analysis techniques. Liu et al (2019) employ the VRP with different maturities and the ADL-MIDAS regression model to forecast the expected stock return in Standard & Poor 500 market.…”

mentioning

confidence: 99%

Special issue on multi-modal information learning and analytics for smart city

Zhou

2020

J Ambient Intell Human Comput

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Logistics forum based prediction on stock index using intelligent data analysis and processing of online web posts

Cited by 9 publications

References 20 publications

An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis

An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis

Evaluation of Stock Closing Prices using Transformer Learning

Special issue on multi-modal information learning and analytics for smart city

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