Stock market prediction analysis by incorporating financial industry using machine learning

DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

Financial Innovation ◽

10.1186/s40854-021-00269-7 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Suppawong Tuarob ◽

Poom Wettayakorn ◽

Ponpat Phetchai ◽

Siripong Traivijitkhun ◽

Sunghoon Lim ◽

...

Keyword(s):

Machine Learning ◽

Social Media ◽

Data Collection ◽

Stock Market ◽

Real Time ◽

Stock Prices ◽

Contextual Information ◽

Online News ◽

Stock Prediction ◽

Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

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Analysis of various machine learning algorithm and hybrid model for stock market prediction using python

2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE) ◽

10.1109/icstcee49637.2020.9276859 ◽

2020 ◽

Author(s):

Sahil Vazirani ◽

Abhishek Sharma ◽

Pavika Sharma

Keyword(s):

Machine Learning ◽

Stock Market ◽

Hybrid Model ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Stock Market Prediction

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Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

Electronics ◽

10.3390/electronics10212717 ◽

2021 ◽

Vol 10 (21) ◽

pp. 2717

Author(s):

Nusrat Rouf ◽

Majid Bashir Malik ◽

Tasleem Arif ◽

Sparsh Sharma ◽

Saurabh Singh ◽

...

Keyword(s):

Machine Learning ◽

Stock Market ◽

Digital Libraries ◽

Stock Price ◽

Ensemble Methods ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Stock Market Prediction ◽

Research Areas ◽

Recent Developments

With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.

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Stock Market Prediction using Machine Learning

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35755 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 3628-3632

Author(s):

Prof. Gowrishankar B S

Keyword(s):

Machine Learning ◽

Stock Market ◽

Stock Price ◽

Predictive Analytics ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Stock Market Prediction ◽

The Future ◽

Complicated Model

Stock market is one of the most complicated and sophisticated ways to do business. Small ownerships, brokerage corporations, banking sectors, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithms to predict the future stock price for exchange by using pre-existing algorithms to help make this unpredictable format of business a little more predictable. The use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The data has to be cleansed before it can be used for predictions. This paper focuses on categorizing various methods used for predictive analytics in different domains to date, their shortcomings.

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