Importance of Machine Learning in Making Investment Decision in Stock Market

Executive Summary Predicting stock trends in the financial market is always demanding but satisfying as well. With the growing power of computing and the recent development of graphics processing unit and tensor processing unit, analysts and researchers are applying advanced techniques such as machine learning techniques more and more to predict stock price trends. In recent years, researchers have developed several algorithms to predict stock trends. To assist investors interested in investing in the stock market, preferably for a short period, it has become necessary to review research papers dealing on machine learning and analyse the importance of their findings in the context of how stock price trends generate trading signals. In this article, to achieve the stated task, authors scrutinized more than 50 research papers focusing on various machine learning algorithms with varied levels of input variables and found that though the performance of models measured by root-mean-square error (RMSE) for regression and accuracy score for classification models varied greatly, long short-term memory (LSTM) model displayed higher accuracy amongst the machine and deep learning models reviewed. However, reinforcement learning algorithm performance measured by profitability and Sharpe ratio outperformed all. In general, traders can maximize their profits by using machine learning instead of using technical analysis. Technical analysis is very easy to implement, but the profit based on it can vanish too soon or making a profit using technical analysis is almost difficult because of its simplicity. Hence, studying machine, deep and reinforcement learning algorithms is vital for traders and investors. These findings were based on the literature review consolidated in the result section.

Download Full-text

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.

Download Full-text

ANALYSIS OF MACHINE LEARNING METHODS FOR PREDICTIONS OF STOCK EXCHANGE SHARE PRICES

Scientific Journal of Astana IT University ◽

10.37943/aitu.2021.47.22.009 ◽

2021 ◽

pp. 94-100

Author(s):

V. Serbin ◽

U. Zhenisserov

Keyword(s):

Machine Learning ◽

Stock Market ◽

Financial Markets ◽

Stock Prices ◽

Stock Price ◽

Learning Algorithms ◽

Support Vector ◽

Learning Methods ◽

Machine Learning Methods ◽

Price Patterns

Since the stock market is one of the most important areas for investors, stock market price trend prediction is still a hot subject for researchers in both financial and technical fields. Lately, a lot of work has been analyzed and done in the field of machine learning algorithms for analyzing price patterns and predicting stock prices and index changes. Currently, machine-learning methods are receiving a lot of attention for predicting prices in financial markets. The main goal of current research is to improve and develop a system for predicting future prices in financial markets with higher accuracy using machine-learning methods. Precise predicting stock market returns is a very difficult task due to the volatile and non-linear nature of financial stock markets. With the advent of artificial intelligence and machine learning, forecasting methods have become more effective at predicting stock prices. In this article, we looked at the machine learning techniques that have been used to trade stocks to predict price changes before an actual rise or fall in the stock price occurs. In particular, the article discusses in detail the use of support vector machines, linear regression, and prediction using decision stumps, classification using the nearest neighbor algorithm, and the advantages and disadvantages of each method. The paper introduces parameters and variables that can be used to recognize stock price patterns that might be useful in future stock forecasting, and how the boost can be combined with other learning algorithms to improve the accuracy of such forecasting systems.

Download Full-text

Analytics of stock market prices based on machine learning algorithms

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v16.i2.pp1050-1058 ◽

2019 ◽

Vol 16 (2) ◽

pp. 1050

Author(s):

Puteri Hasya Damia Abd Samad ◽

Sofianita Mutalib ◽

Shuzlina Abdul-Rahman

Keyword(s):

Machine Learning ◽

Stock Market ◽

Stock Price ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Data Types ◽

Market Prices ◽

Financial News ◽

Svm Algorithm

This study focuses on the use of machine learning algorithms to analyse financial news on stock market prices. Stock market prediction is a challenging task because the market is known to be very volatile and dynamic. Investors face these kinds of problems as they do not properly understand which stock product to subscribe or when to sell the product with an optimum profit. Analyzing the information individually or manually is a tedious task as many aspects have to be considered. Five different companies from Bursa Malaysia namely CIMB, Sime Darby, Axiata, Maybank and Petronas were chosen in this study. Two sets of experiments were performed based on different data types. The first experiment employs textual data involving 6368 articles, extracted from financial news that have been classified into positive or negative using Support Vector Machine (SVM) algorithm. Bags of words and bags of combination words are extracted as the features for the first experiment. The second experiment employs the numeric data type extracted from historical data involving 5321 records to predict whether the stock price is going up (positive) or down (negative) using Random Forest algorithm. The Rain Forest algorithm gives better accuracy in comparison with SVM algorithm with 99% and 68% accuracy respectively. The results demonstrate the complexities of the textual-based data and demand better feature extraction technique.

Download Full-text

IS STOCK PRICES REFLECTED IN MARKET RATIOS?

International Journal of Contemporary Accounting ◽

10.25105/ijca.v2i2.8224 ◽

2020 ◽

Vol 2 (2) ◽

pp. 123

Author(s):

Tan Kwang En

Keyword(s):

Stock Market ◽

Stock Prices ◽

Stock Price ◽

Technical Analysis ◽

Composite Indicator ◽

Financial Accounting ◽

Financial Ratios ◽

Macroeconomic Factor ◽

Price Trends ◽

Almost All

The most fascinating thing in stock market world is forecasting stock prices. Almost all players in stock market race to find the best method for forecast stock prices. After years of researching and practicing, we can divide all methods into two main methods, fundamental and technical analysis. Fundamental analysis based its forecasting method on macroeconomic factor, industry analysis, and company internal factors, while technical analysis based on studying financial accounting numbers and stock price trends in the past and present. This study will be focusing in the uses of technical analysing in forecasting stock prices.There are many ways in technical analysis to forecast stock prices. Investors and analysts usually use stock price trends or financial ratios to do that. The latest is the most simple and powerful tools that almost everyone can use it, regardless to its limitations. When it comes to use financial ratios, there are a lot of contradicting results that make its users need to make a comparation between ratios and make a decision. This paper try to use another solution to overcome those problem with using a composite indicators. The composite indicator will be compared with another market ratio to find out which method is the best on forecasting stock prices.The result is composite indicator is the best method on forecasting stock prices compared with price to sales ratio, price to book value ratio, price to earnings per share ratio, and price to operating cash flow ratio.

Download Full-text

MOMENTUM OSCILLATORS - A TOOL TO PREDICT STOCK PRICE BEHAVIOUR

Management Insight - The Journal of Incisive Analysers ◽

10.21844/mijia.v12i1.11394 ◽

2016 ◽

Vol 12 (1) ◽

Author(s):

Shishir Kumar Gujrati

Keyword(s):

Stock Price ◽

Moving Average ◽

Technical Analysis ◽

Relative Strength ◽

Future Price ◽

Range Data ◽

Strength Index ◽

Price Trends ◽

Past Data ◽

Price Movements

Stock markets are always taken as the barometer of the economy. The price movement of their indices reflects every ups and downs of the economy. Although seem to be random, these price movements do follow a certain track which can be identified using appropriate tool over long range data. One such method is of Technical Analysis wherein future price trends are forecasted using past data. Momentum Oscillators are the important tools of technical analysis. The current paper aims to identify the previous price movements of sensex by using Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) tools and also aims to check whether these tools are appropriate in forecasting the price trends or not.

Download Full-text

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.

Download Full-text

Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator

Investment Management and Financial Innovations ◽

10.21511/imfi.17(4).2020.05 ◽

2020 ◽

Vol 17 (4) ◽

pp. 44-60

Author(s):

Alberto Antonio Agudelo Aguirre ◽

Ricardo Alfredo Rojas Medina ◽

Néstor Darío Duque Méndez

Keyword(s):

Machine Learning ◽

Genetic Algorithms ◽

Stock Market ◽

Moving Average ◽

Asset Price ◽

Technical Analysis ◽

Investment Strategies ◽

Historical Data Analysis ◽

Financial Asset Returns ◽

Technical Indicator

The implementation of tools such as Genetic Algorithms has not been exploited for asset price prediction despite their power, robustness, and potential application in the stock market. This paper aims to fill the gap existing in the literature on the use of Genetic Algorithms for predicting asset pricing of investment strategies into stock markets and investigate its advantages over its peers Buy &amp; Hold and traditional technical analysis. The Genetic Algorithms strategy applied to the MACD was carried out in two different validation periods and sought to optimize the parameters that generate the buy-sell signals. The performance between the machine learning-based approach, technical analysis with the MACD and B&amp;H was compared. The results suggest that it is possible to find optimal values of the technical indicator parameters that result in a higher return on investment through Genetic Algorithms, beating the traditional technical analysis and B&amp;H by around 4%. This study offers a new insight for practitioners, traders, and finance researchers to take advantage of Genetic Algorithms for trading rules application in forecasting financial asset returns under a more efficient and robust methodology based on historical data analysis.

Download Full-text