Gene Expression Data Classification Using Machine Learning with SigFeature: A Novel Significant Feature Selection Method
Keywords:
Data Mining, Feature Recognition, Forecasting Models, Machine Learning, Stock Market Analysis, Wavelets, RMSE, MAE, MAPE, Theil U, Purchase Decision MakingAbstract
In current scenario, machine learning has drawn increasing interest in stock market analysis. To analyse the stock market data for identifying significant feature, data mining and machine learning techniques can be applied. Identifying domain specific feature is a continuous, iterative and logical process. Feature recognition has wide range of applications but no single approach focusses on stock market analysis. Wavelets have its own advantage in tremendous applications however remains less explored in the field of economics and finance. This proposed methodology analysed the new feature, trading interval, for prediction of stock prices which is complex, challenging and need tremendous efforts. This system uses wavelets for identifying domain specific feature in stock market data. General forecasting models were used to forecast the denoised signals. Further the forecast model is selected based upon the performance measure, coefficient of determination with high values. The selected model is used to forecast the share prices. Then performance measures such as RMSE, MAE, MAPE and Theil U were calculated for which trading length consider for analysis. The optimal trading length was found out based on the lowest values of performance measures. Finally, purchase decision making rules were applied to evaluate the accuracy of the selected model and based on that the recommendation of buy or sell was given. This methodology has three proposed approaches; the first approach identifies and removes noise from the stock data efficiently. The second approach involves feature recognition using wavelets on stock market data and the third approach concentrates on analysing stock data, which identifies new feature for economic and financial applications. Finally, to assist investors in making stock market decision, a decision support system with trading interval is presented.
Downloads
References
Cheng, CH, Wei, LY & Chen, YS 2021, ‘Fusion ANFIS models based on multi-stock volatility causality for TAIEX forecasting’, Neuro Computing, pp. 3462-3468.
Chen & Nai-Fu 2018, ‘Financial Investment Opportunities and the Macroeconomy’, Journal finance, vol. 46, no. 2, pp. 529-554.
Christopher M Bilson, Timothy J Brailsford & Vincent J Hooper 2017, ‘Selecting macroeconomic variables as explanatory factors of emerging stock market returns’, Pacific Basin Finance Journal, vol. 9, no. 4, pp. 401-426.
Nithya J & Tamizhchelvan, G 2016, ‘Effectiveness of Technical Analysis in Banking Sector of Equity Market’, IOSR Journal of Business and Management, vol. 16, issue. 7, pp. 20-28
Ntim, CG, Opong, KK, Danbolt, J & Dewotor, F 2018, ‘Testing the weak-form efficiency in African stock markets’, Managerial Finance, vol. 37, no. 3, pp. 195-218. DOI: 10.1108/03074351111113289
Osei 2019, ‘Asset Pricing and Information Efficiency of the Ghanaian Stock Market’, AERC Research Paper 115, Nairobi, Kenya.
Parasuraman, K & Elshorbagy, A, ‘Wavelet networks: an alternative to classical neural networks’, Proceedings of IEEE International Joint Conference on Neural Networks, vol. 5, pp. 2674–2679.
Pradeep Kumar, G & Saravanan, S 2019, ‘Prediction of Stock Option Prices Using Volatility (Garch (1, 1)) Adjusted Black Scholes Option Pricing Model’, Indian Journal of Finance, vol. 7, issue. 9 pp. 36-44
Gencay, R, Selcuk, F & Whitcher, B 2021, ‘An Introduction to Wavelets and Other Filtering Methods in Finance and Economics’, Academic Press, New York.
Sapankevych & Sankar, R 2020, ‘Time Series Prediction Using Support Vector Machines A Survey’, Computational Intelligence Magazine, vol. 4, no. 2, pp. 24-38.
Shiguang Ma & Michelle L Barnes (2020), ‘Are China’s Stock Markets Really Weakform Efficient’, Centre for International Economic Studies, Adelaide Australia Discussion Paper.119, pp.1-47.
Soltani, S 2020, ‘On the use of the wavelet decomposition for time series prediction’, Neurocomputing, vol. 48, pp. 267-277.
Tsung-Jung Hsieh, Hsiao-Fen Hsiao & Wei-Chang Yeh 2019, ‘Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on arti cial bee colony algorithm’, Applied Soft Computing, vol.11, pp. 2510–2525.
Umaprabha, S& Malavika, M 2019, ‘A Study on Technical Analysis of Stocks Listed in NSE with reference to Pharmaceutical Industries’, International Journal of Management (IJM), vol. 6, pp. 79-86.
Vinodkumar P Pathade 2019, ‘Equity research: Fundamental analysis for long term investment’, International Journal of Applied Research, vol. 3, issue 4, pp. 678-682.
Abu-Mostafa, YS & Atiya, AF 2015, ‘Introduction to financial forecasting’, Applied Intelligence, pp. 205–213.
Yao, J, Tan, C & Poh, HL 2017, ‘Neural networks for technical analysis: A study on KLCI’, International Journal of Theoretical and Applied Finance, vol. 2, pp. 1-21.
Refenes, AN, Zapranis, AD & Bentz, Y 2015, ‘Modelling Stock Returns With Neural Networks’, International Conference on 3D Digital Imaging and Modeling , vol.1, pp. 39-50.
Ahmed S Wafia, Hassan Hassana & Adel Mabrouka 2015, ‘Fundamental Analysis Models in Financial Markets – Review Study’, Procedia Economics and Finance, vol. 30 pp. 939 – 947
Amara Graps 2015, ‘An Introduction to wavelets’, IEEE Conference on Computational Science and Engineering, Summer, published by the IEEE Computer Society, 10662 Los Vaqueros Circle, Los Alamitos, CA 90720, USA., vol. 2, no. 2.
Aronson & David, R 2016, ‘Evidence-Based Technical Analysis’, Hoboken, New Jersey: John Wiley and Sons, pp. 357, 355-356, 342. ISBN 978-0-470-00874-4.
Berry, R & Sulochana 2017, ‘Study on Use of Technical Analysis in Forecasting Price Moments of Selected Companies of NSE & BSE’, Imperial Journal of Interdisciplinary Research, vol-3, Issue. 2, pp. 1394-1401.
Blume, Lawrence, Easley, David & Maureen, O'Hara 2014, ‘Market Statistics and Technical Analysis’, The Role of Volume, Journal of Finance, vol. 49, issue. 1, pp. 153-81.
Boopalan, C 2014, ‘Technical Analysis in Select Stocks of Indian Companies’, International Journal of Business and Administration Research Review, vol.2, issue.4, pp. 26-36.
L. Anand et al., “Development of machine learning and medical enabled multimodal for segmentation and classification of brain tumor using MRI images,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–8, Aug. 2022. doi:10.1155/2022/7797094
Deshpande Radhika, Bhalekar Dipali, Mutkule Prasad, Sanjay Pandhare, Nawale Akshay(2015) , “One Stop Solution for Farmer Consumer”, Interaction, IJCA Proceedings on National Conference on Advances in Computing NCAC.
Mutkule Prasad R., “Interactive Clothing based on IoT using QR code and Mobile Application”, International Journal of Scientific Research in Network Security and Communication, vol. 6, issue-6, 2018.
P. Mutkule and M. Ankoshe, “A survey on interactive clothing based on IOT using QR code and Mobile Application,” International Journal of Computer Sciences and Engineering, vol. 6, no. 6, pp. 652–654, Jun. 2018. doi:10.26438/ijcse/v6i6.652654
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
