Skip to main content
SpringerLink
Log in

Artificial Intelligence and Financial Markets in Smart Cities

  • Chapter
  • First Online:
Data-Driven Mining, Learning and Analytics for Secured Smart Cities

Abstract

In today's financial markets, the increasing volume of data poses a big challenge for investors in the stock market. On the other hand, the weaknesses of traditional mathematical methods in managing financial investments have led investors and financial institutions to apply artificial intelligence algorithms. Therefore, the main objective of this chapter is to present artificial intelligent algorithms applications in financial markets. In this regard, after a brief review of different kinds of machine learning methods, it has focused on their applications. Also, it provides fundamental insights for future machine learning-based financial research.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

Similar content being viewed by others

References

  1. Agarap AF (2017) An architecture combining convolutional neural network (CNN) and support vector machine (SVM) for image classification. arXiv:1712.03541

  2. Al Qaisi F, Tahtamouni A, Al-Qudah M (2016)Factors affecting the market stock price-the case of the insurance companies listed in Amman Stock Exchange. Int J Bus Soc Sci 7(10):81–90

    Google Scholar 

  3. Aladwani J (2017) Relationship between exchange rates and stock prices–GCC perspectives. Int J Econ Financ Issues 7(2):11

    Google Scholar 

  4. Amiri R, Mehrpouyan H, Fridman L, Mallik RK, Nallanathan A, Matolak D(2018) A machine learning approach for power allocation in HetNets considering QoS. In: 2018 IEEE international conference on communications (ICC). IEEE

    Google Scholar 

  5. Asmirantho E, Somantri OK (2018) The effect of financial performance on stock price at pharmaceutical sub-sector company listed in Indonesia Stock Exchange. JIAFE (J Ilm Akunt Fak Ekon) 3(2):94–107

    Google Scholar 

  6. Bekaert, Geert, Robert J Hodrick, and Xiaoyan Zhang. International stock return comovements. The Journal of Finance 64 (6):2591–2626, 2009.

    Google Scholar 

  7. Bhagchandani A, Trivedi D (2020) A machine learning algorithm to predict financial investment. In: Data science and intelligent applications. Springer, Berlin, pp 261–266

    Google Scholar 

  8. Chai J, Li A(2019) Deep learning in natural language processing: a state-of-the-art survey. In: 2019 international conference on machine learning and cybernetics (ICMLC). IEEE

    Google Scholar 

  9. Chakraborty C, Gupta B, Ghosh SK, Das DK, Chakraborty C (2016) Telemedicine supported chronic wound tissue prediction using classification approaches. J Med Syst 40(3):68

    Google Scholar 

  10. Chakraborty U, Banerjee A, Saha JK, Sarkar N, Chakraborty C (2021) Artificial Intelligence and the fourth industrial revolution, Jenny Stanford Publishing Pte. Ltd. ISBN 978-981-4800-79-2 (Hardcover), 978-1-003-00000-0 (eBook)

    Google Scholar 

  11. Chen H, Xiao K, Sun J, Song Wu (2017) A double-layer neural network framework for high-frequency forecasting. ACM Trans Manag Inf Syst (TMIS) 7(4):1–17

    Article  Google Scholar 

  12. Chen L, Qiao Z, Wang M, Wang C, Du R, Stanley HE (2018) Which artificial intelligence algorithm better predicts the Chinese stock market? IEEE Access 6:48625–48633

    Google Scholar 

  13. Cumming J, Alrajeh D, Dickens L (2015) An investigation into the use of reinforcement learning techniques within the algorithmic trading domain. London, UK, Imperial College London

    Google Scholar 

  14. Dingli A, Fournier KS (2017) Financial time series forecasting-a machine learning approach. Mach Learn Appl: Int J 4(1/2):3

    Google Scholar 

  15. Edwards RD, Magee J, Bassetti WC (2012) Technical analysis of stock trends. CRC Press

    Google Scholar 

  16. Endo K, Tomobe K, Yasuoka K (2018) Multi-step time series generator for molecular dynamics. In: Thirty-second AAAI conference on artificial intelligence

    Google Scholar 

  17. Eun CS, Shim S (1989) International transmission of stock market movements. J Financ Quant Anal 24(2):241–256

    Google Scholar 

  18. Fenghua WEN, Jihong XIAO, Zhifang HE, Xu GONG (2014) Stock price prediction based on SSA and SVM. Procedia Comput Sci 31:625–631

    Article  Google Scholar 

  19. Franses PH, Ghijsels H (1999) Additive outliers, GARCH and forecasting volatility. Int J Forecast 15(1):1–9

    Google Scholar 

  20. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: icml: Citeseer

    Google Scholar 

  21. Giannini RO, Irvine PJ, Shu T (2014)Do local investors know more? a direct examination of individual investors’ information set. A Direct Exam Individ Invest

    Google Scholar 

  22. Henrique BM, Sobreiro VA, Kimura H (2018) Stock price prediction using support vector regression on daily and up to the minute prices. J Financ Data Sci 4(3):183–201

    Google Scholar 

  23. Hernández-Álvarez M, Hernández EA, Yoo SG (2019) Stock market data prediction using machine learning techniques. In: International conference on information technology & systems. Springer, Berlin

    Google Scholar 

  24. Hernández-Nieves E, del Canto ÁB, Chamoso-Santos P, de la Prieta-Pintado F, Corchado-Rodríguez JM (2020) A machine learning platform for stock investment recommendation systems. In: International symposium on distributed computing and artificial intelligence. Springer, Berlin

    Google Scholar 

  25. Hernandez J, Abad AG(2018) Learning from multivariate discrete sequential data using a restricted Boltzmann machine model. In: 2018 IEEE 1st Colombian conference on applications in computational intelligence (ColCACI). IEEE

    Google Scholar 

  26. Hosaka T (2019) Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Syst Appl 117:287–299

    Article  Google Scholar 

  27. Huang J, Chai J, Cho S (2020) Deep learning in finance and banking: a literature review and classification. Front Bus Res China 14:1–24

    Article  Google Scholar 

  28. Huertas A (2020) A reinforcement learning application for portfolio optimization in the stock market

    Google Scholar 

  29. Kaelbling LP, Littman ML, Moore AW (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237–285

    Google Scholar 

  30. Kanwar N (2019) Deep reinforcement learning-based portfolio management

    Google Scholar 

  31. Khan ZH, Alin TS, Hussain MA (2011) Price prediction of share market using artificial neural network (ANN). Int J Comput Appl 22(2):42–47

    Google Scholar 

  32. Kim HY, Won CH (2018) Forecasting the volatility of stock price index: a hybrid model integrating LSTM with multiple GARCH-type models. Expert Syst Appl 103:25–37

    Google Scholar 

  33. Kraus M, Feuerriegel S (2017) Decision support from financial disclosures with deep neural networks and transfer learning. Decis Support Syst 104:38–48

    Article  Google Scholar 

  34. Krishnan MM, Banerjee S, Chakraborty C, Chakraborty C, Ray AK (2010) Statistical analysis of mammographic features and its classification using support vector machine. Expert Syst Appl 37(1):470–478

    Google Scholar 

  35. Lee SI, Yoo SJ (2019) Multimodal deep learning for finance: integrating and forecasting international stock markets. J Supercomput1–19

    Google Scholar 

  36. Lokshtein D, Kovaleva AG (2020) Application of methods of machine learning to forecasting the motion of stock indices. Язык в cфepe пpoфeccиoнaльнoй кoммyникaции.—Eкaтepинбypг 60–65

    Google Scholar 

  37. Long J, Chen Z, He W, Wu T, Ren J (2020) An integrated framework of deep learning and knowledge graph for prediction of stock price trend: an application in Chinese stock exchange market. Appl Soft Comput106205

    Google Scholar 

  38. Luckieta M, Amran A, Alamsyah DP (2020) The fundamental analysis of stock prices

    Google Scholar 

  39. Madge S, Bhatt S (2015) Predicting stock price direction using support vector machines. Indep Work Rep Spring

    Google Scholar 

  40. Maglogiannis IG (2007) Emerging artificial intelligence applications in computer engineering: real word ai systems with applications in ehealth, hci, information retrieval and pervasive technologies. Ios Press

    Google Scholar 

  41. Matsubara T, Akita R, Uehara K (2018) Stock price prediction by deep neural generative model of news articles. IEICE Trans Inf Syst 101(4):901–908

    Article  Google Scholar 

  42. Meng TL, Khushi M (2019) Reinforcement learning in financial markets. Data 4(3):110

    Google Scholar 

  43. Minh DL, Sadeghi-Niaraki A, Huy HD, Min K, Moon H (2018) Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. IEEE Access 6:55392–55404

    Google Scholar 

  44. Moody J, Lizhong Wu, Liao Y, Saffell M (1998) Performance functions and reinforcement learning for trading systems and portfolios. J Forecast 17(5–6):441–470

    Article  Google Scholar 

  45. Nahil A, Lyhyaoui A (2018) Short-term stock price forecasting using kernel principal component analysis and support vector machines: the case of Casablanca stock exchange. Procedia Comput Sci 127:161–169

    Article  Google Scholar 

  46. Nian R, Liu J, Huang B (2020) A review on reinforcement learning: Introduction and applications in industrial process control. Comput Chem Eng106886

    Google Scholar 

  47. Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V (2019) Artificial intelligence transforms the future of health care. Am J Med 132(7):795–801

    Article  Google Scholar 

  48. Nti IK, Adekoya AF, Weyori BA (2019) A systematic review of fundamental and technical analysis of stock market predictions. Artif Intell Rev 1–51

    Google Scholar 

  49. Parray IR, Khurana SS, Kumar M, Altalbe AA (2020) Time series data analysis of stock price movement using machine learning techniques. Soft Comput 1–9

    Google Scholar 

  50. Pendharkar PC, Cusatis P (2018) Trading financial indices with reinforcement learning agents. Expert Syst Appl 103:1–13

    Google Scholar 

  51. Perdana MK, Adriana CH (2018) Factors influencing the stock price of banking companies in the Indonesia stock exchange. J Account Strat Financ 1(1):57–68

    Google Scholar 

  52. Plakandaras V, Papadimitriou T, Gogas P, Diamantaras K (2015) Market sentiment and exchange rate directional forecasting. Algorithmic Financ 4(1–2):69–79

    Article  MathSciNet  Google Scholar 

  53. Portugal I, Alencar P, Cowan D (2018) The use of machine learning algorithms in recommender systems: a systematic review. Expert Syst Appl 97:205–227

    Article  Google Scholar 

  54. Rieger MO (2020) Uncertainty avoidance, loss aversion and stock market participation. Glob Financ J 100598

    Google Scholar 

  55. Russell S, Norvig P (2002) Artificial intelligence: a modern approach

    Google Scholar 

  56. Saini A, Sharma A (2019) Predicting the unpredictable: an application of machine learning algorithms in Indian stock market. Ann Data Sci1–9

    Google Scholar 

  57. Sarantis N (2001) Nonlinearities, cyclical behaviour and predictability in stock markets: international evidence. Int J Forecast 17(3):459–482

    Article  Google Scholar 

  58. Schumaker RP, Chen H (2009) Textual analysis of stock market prediction using breaking financial news: the AZFin text system. ACM Trans Inf Syst (TOIS) 27(2):1–19

    Google Scholar 

  59. Sezer OB, Ozbayoglu M, Dogdu E (2017) A deep neural-network based stock trading system based on evolutionary optimized technical analysis parameters. Procedia Comput Sci 114:473–480

    Google Scholar 

  60. Sohangir S, Wang D, Pomeranets A, Khoshgoftaar TM (2018) Big data: deep learning for financial sentiment analysis. J Big Data 5(1):3

    Google Scholar 

  61. Sornmayura S (2019) Robust forex trading with deep q network (dqn). ABAC J 39(1)

    Google Scholar 

  62. Sumiyana S, Baridwan Z, Sugiri S, Hartono J (2010) Accounting fundamentals and the variation of stock price: factoring in the investment scalability. Gadjah Mada Int J Bus 12(2):189–229

    Article  Google Scholar 

  63. Sundermeyer M, Schlüter R, Ney H (2012) LSTM neural networks for language modeling. In: Thirteenth annual conference of the international speech communication association

    Google Scholar 

  64. Tan Z, De G, Li M, Lin H, Yang S, Huang L, Tan Q (2020) Combined electricity-heat-cooling-gas load forecasting model for integrated energy system based on multi-task learning and least square support vector machine. J Clean Prod 248:119252

    Article  Google Scholar 

  65. Ture M, Kurt I (2006) Comparison of four different time series methods to forecast hepatitis a virus infection. Expert Syst Appl 31(1):41–46

    Article  Google Scholar 

  66. Wang S, Shang W (2014) Forecasting direction of China security index 300 movement with least squares support vector machine. Procedia Comput Sci 31:869–874

    Article  Google Scholar 

  67. Weng B, Ahmed MA, Megahed FM (2017) Stock market one-day ahead movement prediction using disparate data sources. Expert Syst Appl 79:153–163

    Google Scholar 

  68. Wieland M, Pittore M (2014) Performance evaluation of machine learning algorithms for urban pattern recognition from multi-spectral satellite images. Remote Sens 6(4):2912–2939

    Article  Google Scholar 

  69. Yan H, Ouyang H (2018) Financial time series prediction based on deep learning. Wireless Pers Commun 102(2):683–700

    Article  Google Scholar 

  70. Zhou Z-H (2009) When semi-supervised learning meets ensemble learning. Springer, In International workshop on multiple classifier systems

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Industrial Management Department, Allameh Tabataba’i University, Tehran, Iran

    Mohammad Ali Nikouei & Maghsoud Amiri

  2. Industrial Management Department, University of Tehran, Tehran, Iran

    Saeid Sadeghi Darvazeh

Authors
  1. Mohammad Ali Nikouei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saeid Sadeghi Darvazeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maghsoud Amiri
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Birla Institute of Technology, Mesra, Jharkhand, India

    Chinmay Chakraborty

  2. Computer Science, Electrical Engineering, Western Norway University of Applied Sci, Bergen, Norway

    Jerry Chun-Wei Lin

  3. Casuarina Campus, Purple 12.3.6, Charles Darwin University, Darwin, NT, Australia

    Mamoun Alazab

Rights and permissions

Reprints and permissions

Copyright information

© 2021 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

Nikouei, M.A., Darvazeh, S.S., Amiri, M. (2021). Artificial Intelligence and Financial Markets in Smart Cities. In: Chakraborty, C., Lin, J.CW., Alazab, M. (eds) Data-Driven Mining, Learning and Analytics for Secured Smart Cities. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-72139-8_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72139-8_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72138-1

  • Online ISBN: 978-3-030-72139-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation