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NJIT CS 610 Stock Market Prediction Lecture Notes
- Course
- CS 610
- Institution
- New Jersey Institute Of Technology
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electronicsReview
Stock Market Prediction Using Machine Learning Techniques:
A Decade Survey on Methodologies, Recent Developments, and
Future Directions
Nusrat Rouf 1 , Majid Bashir Malik 2 , Tasleem Arif 3 , Sparsh Sharma 4 , Saurabh Singh 5 , Satyabrata Aich 6, *
and Hee-Cheol Kim 7, *
1
Research Lab., Department of Computer Sciences, BGSB University, Rajouri 185234, India;
2
Department of Computer Sciences, BGSB University, Rajouri 185234, India;
3
Department of Information Technology, BGSB University, Rajouri 185234, India;
4
Department of Computer Science and Engineering, NIT Srinagar 190001, India;
5
Department of Industrial and System Engineering, Dongguk University, Seoul 04620, Korea;
6
Department of Computer Engineering, Institute of Digital Anti-Aging Healthcare, Inje University,
Gimhae 50834, Korea
7
College of AI Convergence, Institute of Digital Anti-Aging Healthcare, u-AHRC, Inje University,
Gimhae 50834, Korea
* Correspondence: (S.A.); (H.-C.K.); Tel.: +82-55-320-3720 (H.-C.K.)
Abstract: With the advent of technological marvels like global digitization, the prediction of the stock
Citation: Rouf, N.; Malik, M.B.; Arif, market has entered a technologically advanced era, revamping the old model of trading. With the
T.; Sharma, S.; Singh, S.; Aich, S.; Kim, ceaseless increase in market capitalization, stock trading has become a center of investment for many
H.-C. Stock Market Prediction Using financial investors. Many analysts and researchers have developed tools and techniques that predict
Machine Learning Techniques: A stock price movements and help investors in proper decision-making. Advanced trading models
Decade Survey on Methodologies, enable researchers to predict the market using non-traditional textual data from social platforms.
Recent Developments, and Future The application of advanced machine learning approaches such as text data analytics and ensemble
Directions. Electronics 2021, 10, 2717.
methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction
https://doi.org/10.3390/
of stock markets continue to be one of the most challenging research areas due to dynamic, erratic,
electronics10212717
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
Academic Editor: George Angelos
Papadopoulos
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
Received: 17 October 2021 was carried out to identify the direction of significance. The study would be helpful for emerging
Accepted: 5 November 2021 researchers to understand the basics and advancements of this emerging area, and thus carry-on
Published: 8 November 2021 further research in promising directions.
Publisher’s Note: MDPI stays neutral Keywords: generic review; machine learning; stock market prediction; support vector machine
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
1. Introduction
An advancement in the fundamental aspects of information technology over the
last few decades has altered the route of businesses. As one of the most captivating
Copyright: © 2021 by the authors. inventions, financial markets have a pointed effect on the nation’s economy [1]. The
Licensee MDPI, Basel, Switzerland. World Bank reported in 2018 that the stock market capitalization worldwide has surpassed
This article is an open access article
68.654 trillion US$ [2]. Over the last few years, stock trading has become a center of
distributed under the terms and
attention, which can largely be attributed to technological advances. Investors search for
conditions of the Creative Commons
tools and techniques that would increase profit and reduce the risk [3]. However, Stock
Attribution (CC BY) license (https://
Market Prediction (SMP) is not a simple task due to its non-linear, dynamic, stochastic,
creativecommons.org/licenses/by/
and unreliable nature [4]. SMP is an example of time-series forecasting that promptly
4.0/).
Electronics 2021, 10, 2717. https://doi.org/10.3390/electronics10212717 https://www.mdpi.com/journal/electronics
,Electronics 2021, 10, 2717 2 of 25
examines previous data and estimates future data values. Financial market prediction
has been a matter of worry for analysts in different disciplines, including economics,
mathematics, material science, and computer science. Driving profits from the trading
of stocks is an important factor for the prediction of the stock market [5]. The stock
market is dependent on various parameters, such as the market value of a share, the
company’s performance, government policies, the country’s Gross Domestic Product
(GDP), the inflation rate, natural calamities, and so on [6]. The Efficient Market Hypothesis
explains that stock market costs are significantly determined by new information, and
follow a random walk pattern, such that they cannot be predicted solely based on past
information [7]. This was a widely accepted theory in the past. With the advent of
technology, researchers demonstrated that stock market prices could be predicted to a
certain extent. Historical market data, combined with the data extracted from social media
platforms, can be analyzed to predict the changes in the economic and business sectors [8].
The performance of stock market prediction systems relies intensely on the quality of the
features it is using [9]. While researchers have used some strategies for enhancing the
stock-explicit features, more attention needs to be paid to feature extraction and selection
mechanisms. Figure 1 presents the outline of this article.
Figure 1. Article outline.
, Electronics 2021, 10, 2717 3 of 25
1.1. Classical Approaches for SMP
According to [10], there exist two main traditional approaches to the analysis of the
stock markets: (1) fundamental analysis and (2) technical analysis.
1.1.1. Fundamental Analysis
Fundamental analysis calculates a genuine value of a sector/company and determines
the amount that one share of that company should cost. A supposition is made that, if
given sufficient time, the company will move to a cost agreeing with the prediction. If
a sector/company is undervalued, then the market value of that company should rise,
and conversely, if a company is overvalued, then the market price should fall [11]. The
analysis is performed considering various factors, such as yearly fiscal summaries and
reports, balance sheets, a future prospectus, and the company’s work environment [12]. If
stocks are overvalued, then the market price will fall [13], e.g., the Dotcom bubble burst
in the year 2000 [14]. The two most common metrics used to predict long-term price
movements yearly for fundamental analysis are (a) the Price to Earnings ratio (P/E) and
(b) the Price by Book ratio (P/B). The P/E ratio is used as a predictor. The companies with
a lower P/E ratio yield higher returns than companies with a high P/E ratio [15]. Financial
analysts also use this to prove their stock recommendations [16]. Fundamental analysis
can be used for the consideration of financial ratios to distinguish poor stocks from quality
stocks [17]. The P/B ratio compares the company value specified by the market to the
company value specified on paper. If the ratio is high, the company may be overvalued,
and the company’s value might fall with time. Conversely, if the ratio is low, the company
may be underestimated, and the price may rise with time. Of course, fundamental analysis
is a powerful method. Still, it has some drawbacks. Fundamental analysis, firstly, lacks
adequate knowledge of the rules governing the workings of the system, and secondly, there
is non-linearity in the system [18].
1.1.2. Technical Analysis
Technical analysis is the study of stock prices to make a profit, or to make better
investment decisions [19]. Technical analysis predicts the direction of the future price
movements of stocks based on their historical data, and helps to analyze financial time
series data using technical indicators to forecast stock prices. Meanwhile, it is assumed that
the price moves in a trend and has momentum [20]. Technical analysis uses price charts
and certain formulae, and studies patterns to predict future stock prices; it is mainly used
by short-term investors. The price would be considered high, low or open, or the closing
price of the stock, where the time points would be daily, weekly, monthly, or yearly. Dow
theory puts forward the main principles for technical analysis, which are that the market
price discounts everything, prices move in trends, and historic trends usually repeat the
same patterns [21 ]. There are several technical indicators, such as the Moving Average
(MA), Moving Average Convergence/Divergence (MACD), the Aroon indicator, and the
money flow index, etc. The evident flaws of technical analysis as per [18] are that expert’s
opinions define rules in technical analysis, which are fixed and are reluctant to change.
Various parameters that affect stock prices are ignored.
The prerequisite is to overcome the deficiencies of fundamental and technical anal-
ysis, and the evident advancement in the modelling techniques has motivated various
researchers to study new methods for stock price prediction. A new form of collective
intelligence has emerged, and new innovative methods are being employed for stock value
forecasting. The methodologies incorporate the work of machine learning algorithms for
stock market analysis and prediction.
1.2. Modern Approaches for SMP
There are some modern approaches that can be functional and fruitful for SMP that
would enhance prediction accuracies. In this review, we will highlight some modern
functional approaches.
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