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

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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Overview of Machine Learning Approaches for Wireless Communication

Research Anthology on Artificial Intelligence Applications in Security ◽

10.4018/978-1-7998-7705-9.ch069 ◽

2021 ◽

pp. 1579-1597

Author(s):

Tolga Ensari ◽

Melike Günay ◽

Yağız Nalçakan ◽

Eyyüp Yildiz

Keyword(s):

Machine Learning ◽

Wireless Communications ◽

Smart Phones ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Research Areas ◽

Learning Techniques ◽

Recent Developments ◽

Security And Reliability ◽

Day By Day

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is continuously changing. Recent developments in smart phones and other devices like drones, wearable devices, machines with sensors need reliable communication within internet of things (IoT) systems. For this purpose, artificial intelligence can increase the security and reliability and manage the data that is generated by the wireless systems. In this chapter, the authors investigate several machine learning techniques for wireless communications including deep learning, which represents a branch of artificial neural networks.

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Overview of Machine Learning Approaches for Wireless Communication

Advances in Wireless Technologies and Telecommunication - Next-Generation Wireless Networks Meet Advanced Machine Learning Applications ◽

10.4018/978-1-5225-7458-3.ch006 ◽

2019 ◽

pp. 123-140 ◽

Cited By ~ 1

Author(s):

Tolga Ensari ◽

Melike Günay ◽

Yağız Nalçakan ◽

Eyyüp Yildiz

Keyword(s):

Machine Learning ◽

Wireless Communications ◽

Smart Phones ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Research Areas ◽

Learning Techniques ◽

Recent Developments ◽

Security And Reliability ◽

Day By Day

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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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A Machine Learning View on Momentum and Reversal Trading

Algorithms ◽

10.3390/a11110170 ◽

2018 ◽

Vol 11 (11) ◽

pp. 170 ◽

Cited By ~ 2

Author(s):

Zhixi Li ◽

Vincent Tam

Keyword(s):

Neural Network ◽

Machine Learning ◽

Stock Market ◽

Short Term Memory ◽

Predictive Ability ◽

Trading Strategies ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Learning Techniques

Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns.

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Machine Learning Techniques and Use of Event Information for Stock Market Prediction: A Survey and Evaluation

International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06) ◽

10.1109/cimca.2005.1631572 ◽

2006 ◽

Cited By ~ 19

Author(s):

P.D. Yoo ◽

M.H. Kim ◽

T. Jan

Keyword(s):

Machine Learning ◽

Stock Market ◽

Machine Learning Techniques ◽

Stock Market Prediction ◽

Learning Techniques ◽

Event Information ◽

Survey And Evaluation

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Stock Market Prediction Using Machine Learning

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8405 ◽

2020 ◽

Vol 17 (4) ◽

pp. 1584-1589

Author(s):

J. Shiva Nandhini ◽

Chitrak Bari ◽

Gareja Pradip

Keyword(s):

Machine Learning ◽

Stock Market ◽

Learning Algorithm ◽

Market Price ◽

Technical Analysis ◽

Weak Form ◽

Efficient Market Hypothesis ◽

Machine Learning Techniques ◽

Fundamental Analysis ◽

Stock Market Prediction

The basic tool aimed at increasing the rate of investor’s interest in stock markets is by developing a vibrant application for analyzing and predicting stock market prices. In this report we explain, the development and implementation of a stock market price prediction application using machine learning algorithm. In this report, we try to analyze existing and new methods of stock market prediction. We take three different approaches for solving the problem: Fundamental analysis, Technical Analysis and The application of Machine Learning. We found evidence in support of the weak form of the Efficient Market Hypothesis. We can use Fundamental Analysis and Machine Learning to guide an investor’s decisions. We demonstrate a common flaw in Technical Analysis methodology to show that it produces limited useful information. Based on our findings, algorithmic trading programs are developed and simulated using Quant. During the past few decades, various machine learning techniques have been applied to study the highly theoretical and speculative nature of stock market by capturing and using repetitive patterns. Different companies use different types of analysis tools for forecasting and the main aim is the accuracy, with which they predict which set of stocks would yield the maximum amount of profit.

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Stock market prediction using machine learning techniques

2016 3rd International Conference on Computer and Information Sciences (ICCOINS) ◽

10.1109/iccoins.2016.7783235 ◽

2016 ◽

Cited By ~ 16

Author(s):

Mehak Usmani ◽

Syed Hasan Adil ◽

Kamran Raza ◽

Syed Saad Azhar Ali

Keyword(s):

Machine Learning ◽

Stock Market ◽

Machine Learning Techniques ◽

Stock Market Prediction ◽

Learning Techniques

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Stock Price Prediction Using Machine Learning

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-1644 ◽

2021 ◽

pp. 386-389

Author(s):

Warade Kalyani Gopal ◽

Jawale Mamta Pandurang ◽

Tayade Pratiksha Devaram ◽

Dr. Dinesh D. Patil

Keyword(s):

Machine Learning ◽

Stock Market ◽

Stock Prices ◽

Stock Price ◽

Mathematical Functions ◽

Stock Market Prediction ◽

Stock Price Prediction ◽

Future Value ◽

A Company ◽

Stock Values

In Stock Market Prediction, the aim is to predict for future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market by training on their previous values. Machine learning itself employs different models to make prediction easier. The paper focuses on Regression and LSTM based Machine learning to predict stock values. Factors considered are open, close, low, high and volume. In order to predict market movement, the stock prices and stock indicators in addition to the news related to these stocks. Most of the previous work in this industry focused on either classifying the released market news and demonstrating their effect on the stock price or focused on the historical price movement and predicted their future movement. In this work, we propose an automated trading system that integrates mathematical functions, machine learning, and other external factors such as news’ sentiments for the purpose of a better stock prediction accuracy and issuing profitable trades. The aim to determine the price of a certain stock for the coming end-of-day considering the first several trading hours of the day.

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