Skip to main content
SpringerLink
Log in

An Intelligent Model for Identifying Fluctuations in the Stock Market and Predicting Investment Policies with Guaranteed Returns

  • Chapter
  • First Online:
Data Analytics for Internet of Things Infrastructure

Part of the book series: Internet of Things ((ITTCC))

  • 246 Accesses

Abstract

The stock market is a tough forum for investment and requires ample deliberation before investing hard-earned money into buying stocks. The stock market is one of a number of sectors that buyers are committed to. For this reason, the inventory forecast is a hotly debated topic for researchers from each economic and technical domain. In this chapter, the primary goal is to construct a country-of-art-work prediction for pricing that focuses on quick changes in price predictions. The cryptocurrency market is nowhere near as stable as traditional commodity markets. The stock market can be plagued by numerous technical, emotional, and challenging factors, though, making it extremely volatile, risky, uncertain, and unpredictable. This chapter analyses the shortcomings of the current market tendencies and constructs a time-series version for mitigating most of them by using greater-efficient algorithms. An expert machine is proposed to predict the uncertainty of market risk and to predict the guaranteed amount of return. Fuzzy inference is deployed to predict uncertainty. A real-time data set, the Nifty 50 stock list records (2000–2021), from Kaggle, is used as a test bed to validate the proposed version. Finally, fourfold cross validation is carried out to assess the overall outcome or performance of the proposed model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Shen, J. & Omair Shafiq, M. (2020). Short-term stock market price trend prediction using a comprehensive deep learning system. Springer Open, Journal of Big Data, 7, Open access, Article number: 66.

    Google Scholar 

  2. Tanwar, S., Patel, N. P., Patel, S. N., Patel, J. R., Sharma, G., & Davidson, I. E. (2020). Deep learning-based cryptocurrency price prediction scheme with inter-dependent relations. IEEE Access, 9, 1345–1356.

    Google Scholar 

  3. Liu, H., Qi, L., & Sun, M. (2022, June). Short-term stock price prediction based on CAE-LSTM method, ResearchGate. Hindawi access, Wireless Communications and Mobile Computing, 2022(S1), 1–7. https://doi.org/10.1155/2022/4809632

  4. Banerjee, S., Neha Dabeeru, R., & Lavanya. (2020). Stock market prediction. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(9), 506–509. ISSN: 2278–3075.

    Article  Google Scholar 

  5. Li, H., Dagli, C.H., & Enke, D. (2007, May). Short-term stock market timing prediction under reinforcement learning schemes. In IEEE Xplore Conference on Approximate Dynamic Programming and Reinforcement Learning. ADPRL 2007. IEEE International Symposium. pp. 233–237. https://doi.org/10.1109/ADPRL.2007.368193

  6. Rao, P. S., Srinivas, K., & Krishna Mohan, A. (2020, May). A survey on stock market prediction using machine learning techniques. https://doi.org/10.1007/978-981-15-1420-3_101

  7. Sarkar, M., Roy, A., Badr, Y., Gaur, B., & Gupta, S. (2021). An intelligent music recommendation framework for multimedia big data: A journey of entertainment industry. Studies of Big Data, Springer Nature Singapore, 2, 39–67.

    Google Scholar 

  8. Roy, A., Banerjee, S., Sarkar, M., Darwish, A., Elhosen, M., & Hassanieen, A. E. (2018). Exploring New Vista of intelligent collaborative filtering: A restaurant recommendation paradigm. Journal of Computational Science, Elsevier, 27, 168–182.

    Article  Google Scholar 

  9. Berra, D. (2018, January). Cross-validation, ResearchGate. Reference module in life sciences, pp. 1–3. https://doi.org/10.1016/B978-0-12-809633-8.20349-X

  10. Yang, W. (2007). Stock price prediction based on fuzzy logic. In 2007 International Conference on Machine Learning and Cybernetics, pp. 1309–1314. https://doi.org/10.1109/ICMLC.2007.4370347

  11. Vo, M. T., Vo, A. H., Nguyen, T., Sharma, R., & Le, T. (2021). Dealing with the class imbalance problem in the detection of fake job descriptions. Computers, Materials & Continua, 68(1), 521–535.

    Article  Google Scholar 

  12. Sachan, S., Sharma, R., & Sehgal, A. (2021). Energy efficient scheme for better connectivity in sustainable mobile wireless sensor networks. Sustainable Computing: Informatics and Systems, 30, 100504.

    Google Scholar 

  13. Ghanem, S., Kanungo, P., Panda, G., et al. (2021). Lane detection under artificial colored light in tunnels and on highways: An IoT-based framework for smart city infrastructure. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-021-00381-2

  14. Sachan, S., Sharma, R., & Sehgal, A. (2021). SINR based energy optimization schemes for 5G vehicular sensor networks. Wireless Personal Communications. https://doi.org/10.1007/s11277-021-08561-6

  15. Priyadarshini, I., Mohanty, P., Kumar, R., et al. (2021). A study on the sentiments and psychology of twitter users during COVID-19 lockdown period. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-021-11004-w

  16. Azad, C., Bhushan, B., Sharma, R., et al. (2021). Prediction model using SMOTE, genetic algorithm and decision tree (PMSGD) for classification of diabetes mellitus. Multimedia Systems. https://doi.org/10.1007/s00530-021-00817-2

  17. Priyadarshini, I., Kumar, R., Tuan, L. M., et al. (2021). A new enhanced cyber security framework for medical cyber physical systems. SICS Software-Intensive Cyber-Physical Systems. https://doi.org/10.1007/s00450-021-00427-3

  18. Priyadarshini, I., Kumar, R., Sharma, R., Singh, P. K., & Satapathy, S. C. (2021). identifying cyber insecurities in trustworthy space and energy sector for smart grids. Computers & Electrical Engineering, 93, 107204.

    Article  Google Scholar 

  19. Singh, R., Sharma, R., Akram, S. V., Gehlot, A., Buddhi, D., Malik, P. K., & Arya, R. (2021). Highway 4.0: Digitalization of highways for vulnerable road safety development with intelligent IoT sensors and machine learning. Safety Science, 143, 105407, ISSN 0925-7535.

    Article  Google Scholar 

  20. Sahu, L., Sharma, R., Sahu, I., Das, M., Sahu, B., & Kumar, R. (2021). Efficient detection of Parkinson's disease using deep learning techniques over medical data. Expert Systems, e12787. https://doi.org/10.1111/exsy.12787

  21. Suvarnapathaki, S. (2022). Using unstructured data with structured data for segmentation of nifty 50 stocks. JETIR, 9(6).

    Google Scholar 

  22. Sanger, W., & Warin, T. (2016). High frequency and unstructured data in finance: An exploratory study of Twitter. JGRCS 2016, 7(4).

    Google Scholar 

  23. Tetlock, P. C., Saar-Tsechansky, M., & Macskassy, S. (2008). More than words: Quantifying language to measure firms’ fundamentals. The Journal of Finance, 63(3), 1437–1467.

    Article  Google Scholar 

  24. Peji’c Bach, M., Krsti, Ž., Seljan, S., & Turulja, L. (2019). Text mining for big data analysis in financial sector - A literature review. Sustainability, 11, 1277. https://doi.org/10.3390/su11051277

    Article  Google Scholar 

  25. Lima, L., Portela, F., Santos, M. F., Abelha, A., & Machado, J. (2015). Big data for stock market by means of mining techniques. Springer Science and Business Media. https://doi.org/10.1007/978-3-319-16486-1_67

    Book  Google Scholar 

  26. Santhosh Baboo, L., & Renjith Kumar, P. (2013). Next generation data warehouse design with big data for big analytics and better insights. Global Journal of Computer Science and Technology, 13(7).

    Google Scholar 

  27. Morshadul Hasan, M., Popp, J., & Olah, J. (2020). Current landscape and influence of big data on finance. Journal of Big Data, 7, Article No: 21.

    Article  Google Scholar 

  28. Choi, T.-M., & Lambert, J. H. (2017, August 11). Advances in risk analysis with big data. https://doi.org/10.1111/risa.12859

  29. Razin, E. (2015, December 3). Big buzz about big data: 5 ways big data is changing finance. Forbes.

    Google Scholar 

  30. Jaweed, M. D., & Jebathangam, J. (2018). Analysis of stock market by using Big Data Processing Environment. International Journal of Pure and Applied Mathematics, 119(10), 81–86.

    Google Scholar 

  31. Mallon, S. Big data analytics has potential to massively disrupt the stock market. https://www.smartdatacollective.com/big-data-analytics-has-potential-to-massively-disrupt-stock-market/

  32. The Pipeline, The ZoomInfo. https://pipeline.zoominfo.com/marketing/dynamic-data

  33. Yang, P., & Hou, X. (2022). Research on dynamic characteristics of stock market based on big data analysis. ResearchGate, Hindawi, Discrete Dynamics in Nature and Society, 2022, Article ID 8758976, 1–8. https://doi.org/10.1155/2022/8758976

  34. John, J., & Joseph, B. (2022). Stock price prediction using LSTM with dynamic data sets. Proceedings of the National Conference on Emerging Computer Applications (NCECA), 4(1), 625–628. https://doi.org/10.5281/zenodo.6938228

    Article  Google Scholar 

  35. Shah, A., Patel, P., & Vora, D. (2020). Dynamic approach to stock trades using ML techniques. International Research Journal of Engineering and Technology (IRJET), 07(12), 608–610, e-ISSN: 2395-0056.

    Google Scholar 

  36. Akhtar, M. M., Zamani, A. S., Khan, S., AliShatat, A. S., Dilshan, S., & Samdan, F. (2022). Stock market prediction based on statistical data using machine learning algorithms. Journal of King Saud University – Science, Science Direct, 34(4), 101940.

    Google Scholar 

  37. Karim, R., Alam, M. K., & Hossain, M. R. (2021, August). Stock market analysis using linear regression and decision tree regression. In IEEE Conference: 2021 1st International Conference on Emerging Smart Technologies and Applications (eSmarTA). https://doi.org/10.1109/eSmarTA52612.2021.9515762

  38. Adusumilli, R. (2019). Machine learning to predict stock prices, Published in towards Data Science, Dec 26.

    Google Scholar 

  39. Abbasi, E., & Abouec, A. (2008). Stock price forecast by using neuro-fuzzy inference system. World Academy of Science, Engineering and Technology, 46, 320–323.

    Google Scholar 

  40. Visa, S., Ramsay, B., Ralescuand, A., & van der Knaap, E. (2011). Confusion matrix-based feature selection. In Proceedings of the 22nd Midwest Artificial Intelligence and Cognitive Science Conference, Cincinnati, pp. 16–17.

    Google Scholar 

  41. Jung, Y., & Hu, J. (2015). A k-fold averaging cross-validation procedure. Journal of Nonparametric Statistics, 27(2), 1–13.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Atria Institute of Technology, Bengaluru, Karnataka, India

    Manash Sarkar

  2. Expert Data Scientist, Limendo, Bolzano, Italy

    Manash Sarkar

  3. Department of Computer Science & Engineering, Atria Institute of Technology, Bengaluru, Karnataka, India

    M. N. Pratima & R. Darshan

  4. Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur, India

    Debkanta Chakraborty

  5. LAMIH UMR CNRS 8201, Department of Computer Science, Université Polytechnique Hauts-de-France, Valenciennes, France

    Maroi Agrebi

Authors
  1. Manash Sarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. N. Pratima
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Darshan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Debkanta Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maroi Agrebi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manash Sarkar .

Editor information

Editors and Affiliations

  1. SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, Uttar Pradesh, India

    Rohit Sharma

  2. Incheon National University, Incheon, Korea (Republic of)

    Gwanggil Jeon

  3. Xi’an Shiyou University, Xi'An, China

    Yan Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sarkar, M., Pratima, M.N., Darshan, R., Chakraborty, D., Agrebi, M. (2023). An Intelligent Model for Identifying Fluctuations in the Stock Market and Predicting Investment Policies with Guaranteed Returns. In: Sharma, R., Jeon, G., Zhang, Y. (eds) Data Analytics for Internet of Things Infrastructure. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-031-33808-3_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-33808-3_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33807-6

  • Online ISBN: 978-3-031-33808-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation