The research investigates the performance of four machine learning algorithms ‘Linear Regression, Support Vector Machine, Gradient Boost and Random Forest’ in predicting stock prices in Pakistan during the COVID-19 pandemic. Utilizing 15 trading factors sourced from the Pakistan Stock Exchange website and analyzed using Python programming language, the study calculates volatility during the pandemic period. Results reveal that the Random Forest model consistently exhibits robust predictive capabilities across various metrics, outperforming other algorithms by accurately predicting stock prices based on factors such as the previous day’s closing price, open price, average price, turnover and P/E ratio, with over 90% accuracy. Notably, Random Forest also surpasses in predicting the lowest price, highest price, P/B ratio, P/S ratio, fluctuations in currency (in Rupees), turnover rate, total market capitalization and total equity. This study contributes to the literature on stock return algorithms, uniquely examining the effectiveness of different financial factors with various machine learning algorithms in the context of Pakistan’s stock market during a challenging period. The findings hold significance for investors, traders and policymakers, offering insight for decision-making and regulation formulation amid market instability.
AkitaR., YoshiharaA., MatsubaraT., & UeharaK. (2016). Deep learning for stock prediction using numerical and textual information. 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS).
2.
AkmaludheenK., & PankajD. S. (2023). Forecasting the market: A survey of techniques for stock market prediction. 2023 International Conference on Control, Communication and Computing (ICCC).
3.
AlshammariR. K., AlrwaisO., & AksoyM. S. (2022). Machine learning applications to dust storms: A meta-analysis. Aerosol and Air Quality Research, 22, 220183.
4.
AltmanE. I., & HotchkissE. (1993). Corporate financial distress and bankruptcy (Vol. 1998). John Wiley & Sons.
5.
BalochS., BalochM. A., ZhengT., & PeiX. (2020). The coronavirus disease 2019 (COVID-19) pandemic. The Tohoku Journal of Experimental Medicine, 250(4), 271–278.
6.
BeheraC., & RathB. N. (2023). The interconnectedness between COVID-19 uncertainty and stock market returns in selected ASEAN countries. Emerging Markets Finance and Trade, 59(2), 515–527.
7.
BenthabetS., & BenthabetA. (2023). Testing of weak form efficient market hypothesis: Evidence from the hat bourse (2010-2019). les cahiers du cread, 39(1), 225–247.
8.
BluwsteinK., BuckmannM., JosephA., KapadiaS., & ŞimşekÖ. (2023). Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach. Journal of International Economics, 103773.
9.
Boru İpekA. (2022). Stock price prediction using improved extreme learning machine methods during the Covid-19 pandemic and selection of appropriate prediction method. Kybernetes, 52(10), 4081–4109.
10.
ChongE., HanC., & ParkF. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187–205.
11.
FanA., & PalaniswamiM. (2000). Selecting bankruptcy predictors using a support vector machine approach. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.
12.
Halls-MooreM. (2014). Support vector machines: A guide for beginners.
13.
JiangY. (2022). Application and comparison of multiple machine learning models in finance. Scientific Programming, 9613554.
14.
JothimaniD., ShankarR., & YadavS. S. (2017). A PCA-DEA framework for stock selection in Indian stock market. Journal of Modelling in Management, 12(3), 386–403.
15.
Kumar ChandarS. (2019). Fusion model of wavelet transform and adaptive neuro fuzzy inference system for stock market prediction. Journal of Ambient Intelligence and Humanized Computing, 1–9.
16.
KumarI., DograK., UtrejaC., & YadavP. (2018). A comparative study of supervised machine learning algorithms for stock market trend prediction (pp. 1003-1007). 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT).
17.
LeCunY., & BengioY. (1995). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10).
18.
LiangD., LuC.-C., TsaiC.-F., & ShihG.-A. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research, 252(2), 561–572.
19.
LincolnM. (1984). An empirical study of the usefulness of accounting ratios to describe levels of insolvency risk. Journal of Banking & Finance, 8(2), 321–340.
20.
López-RoblesJ. R., CoboM. J., Gutiérrez-SalcedoM., Martínez-SánchezM., Gamboa-RosalesN. K., & Herrera-ViedmaE. (2021). 30th anniversary of Applied Intelligence: A combination of bibliometrics and thematic analysis using SciMAT. Applied Intelligence, 51, 6547–6568.
21.
MalikJ. A., AhmedS., MirA., ShindeM., BenderO., AlshammariF., AnsariM., & AnwarS. (2022). The SARS-CoV-2 mutations versus vaccine effectiveness: New opportunities to new challenges. Journal of Infection and Public Health, 15(2), 228–240.
22.
MehmoodW., Mohd-RashidR., & AhmadA. H. (2020). The variability of IPO issuance: Evidence from Pakistan Stock Exchange. Global Business Review, 24(5), 1025–1040.
23.
MijailovicO., & MizdrakovićV. M. (2023). Behavioural finance for efficient corporate governance: Testing the efficient market hypothesis at the Belgrade stock exchange. The European Journal of Applied Economics, 20(1), 1–21.
24.
Mikołajek-GocejnaM., & UrbaśT. (2023). Rational investors or rational expectations in efficient market hypothesis?International Journal of Finance, Insurance and Risk Management, XIII(2), 167–188.
25.
MinJ. H., & LeeY.-C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603–614.
26.
MinK.-J., & TatarM. (2006). Restriction of amino acids extends lifespan in Drosophila melanogaster. Mechanisms of Ageing and Development, 127(7), 643–646.
27.
MuramatsuR., VartanianP. R., & de Andrade MoraesG. (2023). A behavioral interpretation of volatility patterns in Brazilian stock market: Analysis of pre and post-COVID-19 periods from 2019 to 2021. International Journal of Business and Management, 18(4), 1–24.
28.
NaeemM. A., YousafI., KarimS., TiwariA. K., & FaridS. (2023). Comparing asymmetric price efficiency in regional ESG markets before and during COVID-19. Economic Modelling, 118, 106095.
29.
NamdariA., & LiZ. S. (2018). Integrating fundamental and technical analysis of stock market through multi-layer perceptron. 2018 IEEE Technology and Engineering Management Conference (TEMSCON).
30.
NanA., PerumalA., & ZaianeO. R. (2022). Sentiment and knowledge based algorithmic trading with deep reinforcement learning. International Conference on Database and Expert Systems Applications.
31.
NazliogluS., PazarciS., KarA., & VarolO. (2023). Efficient market hypothesis in emerging stock markets: Gradual shifts and common factors in panel data. Applied Economics Letters, 31(18), 1773–1779.
OhlsonJ. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131.
34.
PallathadkaH., MustafaM., SanchezD. T., SajjaG. S., GourS., & NavedM. (2023). Impact of machine learning on management, healthcare and agriculture. Materials Today: Proceedings, 80, 2803–2806.
35.
RaiP., MohapatraB. B., MeiteiA. J., & JainV. (2023). Major determinants of bank profitability in India: A machine learning approach. Global Business Review. https://doi.org/10.1177/09721509231184763
36.
RiazS., AhmedR., ParkashR., & AhmadM. J. (2020). Determinants of stock market investors’ behavior in COVID-19: A study on the Pakistan Stock Exchange.
37.
SamuiP. (2008). Support vector machine applied to settlement of shallow foundations on cohesionless soils. Computers and Geotechnics, 35(3), 419–427.
38.
SchiilkopP., BurgestC., & VapnikV. (1995). Extracting support data for a given task. Proceedings, First International Conference on Knowledge Discovery & Data Mining. AAAI Press, Menlo Park, CA.
39.
SchumakerR. P., & ChenH. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS), 27(2), 1–19.
40.
ShahzadF., YannanD., KamranH. W., SuksatanW., HashimA. N., AlifN., & RazzaqA. (2022). Outbreak of epidemic diseases and stock returns: An event study of emerging economy. Economic Research—Ekonomska istraživanja, 35(1), 2313–2332.
41.
ShahzadA., ZafarB., AliN., JamilU., AlghadhbanA. J., AssamM., GhamryN. A., & EldinE. T. (2022). COVID-19 vaccines related user’s response categorization using machine learning techniques. Computation, 10(8), 141.
42.
ShereenM. A., KhanS., KazmiA., BashirN., & SiddiqueR. (2020). COVID-19 infection: Emergence, transmission, and characteristics of human coronaviruses. Journal of Advanced Research, 24, 91–98.
43.
ShinK.-S., LeeT. S., & KimH.-j. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(1), 127–135.
44.
ShynkevichY., McGinnityT. M., ColemanS. A., & BelatrecheA. (2016). Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning. Decision Support Systems, 85, 74–83.
45.
SinghS., MadanT. K., KumarJ., & SinghA. K. (2019). Stock market forecasting using machine learning: Today and tomorrow. 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT).
46.
SinhaP. C. (2021). Noise of investors’ attention mania in the twenty-first-century Indian stock markets: ARDL and augmented GARCH-X models. Global Business Review, 25(5), 1171–1221.
47.
UllahN., & AsgharU. (2023). Efficient market hypothesis: An exploratory study of FTSE-100 stock market. Journal of Business & Tourism, 9(01), 12–20.
48.
VatsP., & SamdaniK. (2019). Study on machine learning techniques in financial markets. 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN).
49.
VijhM., ChandolaD., TikkiwalV. A., & KumarA. (2020). Stock closing price prediction using machine learning techniques. Procedia Computer Science, 167, 599–606.
50.
WaheedH., HassanS.-U., AljohaniN. R., HardmanJ., AlelyaniS., & NawazR. (2020). Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104, 106189.
51.
WangC., WangZ., WangG., LauJ. Y.-N., ZhangK., & LiW. (2021). COVID-19 in early 2021: Current status and looking forward. Signal Transduction and Targeted Therapy, 6(1), 114.
52.
WengS. F., RepsJ., KaiJ., GaribaldiJ. M., & QureshiN. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data?PLoS One, 12(4), e0174944.
53.
YangF., ChenJ., & LiuY. (2023). Improved and optimized recurrent neural network based on PSO and its application in stock price prediction. Soft Computing, 27(6), 3461–3476.
54.
ZhongN., WangC., YaoW., ChenP., KangJ., HuangS., ChenB., WangC., NiD., & ZhouY. (2007). Prevalence of chronic obstructive pulmonary disease in China: A large, population-based survey. American Journal of Respiratory and Critical Care Medicine, 176(8), 753–760.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
