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International Journal of Sensors, Wireless Communications and Control

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ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

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Research Article

Big Data Analysis for Trend Recognition Using Machine Learning Techniques

Author(s): Cerene Mariam Abraham*, Mannathazhathu Sudheep Elayidom and Thankappan Santhanakrishnan

Volume 10, Issue 4, 2020

Page: [540 - 550] Pages: 11

DOI: 10.2174/2210327910666200304141238

Price: $65

Abstract

Background: Machine learning is one of the most popular research areas today. It relates closely to the field of data mining, which extracts information and trends from large datasets.

Aims: The objective of this paper is to (a) illustrate big data analytics for the Indian derivative market and (b) identify trends in the data.

Methods: Based on input from experts in the equity domain, the data are verified statistically using data mining techniques. Specifically, ten years of daily derivative data is used for training and testing purposes. The methods that are adopted for this research work include model generation using ARIMA, Hadoop framework which comprises mapping and reducing for big data analysis.

Results: The results of this work are the observation of a trend that indicates the rise and fall of price in derivatives , generation of time-series similarity graph and plotting of frequency of temporal data.

Conclusion: Big data analytics is an underexplored topic in the Indian derivative market and the results from this paper can be used by investors to earn both short-term and long-term benefits.

Keywords: Big data, derivative market, open interest, deliverable quantity, ARIMA, temporal data mining, machine learning.

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[1]
Rubani M. A study of derivative market in India. Int J Bus Adm Manag 2017; 7(1): 203-15.
[2]
Shalini HS, Raveendra PV. A study of derivatives market in India and its current position in global financial derivatives markets. IOSR J Econom Finan 2014; 2014: 2321-5933.
[3]
Chui M. Derivatives markets, products and participants: An overview 2012.
[4]
Lin S. The quantitative analytic research of extenics by VAR on the risks of the financial derivatives markets. 2010 International Conference on Internet Technology and Applications. Wuhan, China, 2010..
[http://dx.doi.org/10.1109/ITAPP.2010.5566130]
[5]
Vashishtha A, Kumar S. Development of financial derivatives market in India- A case study. Int Res J Finance Econ 2010; 37(37): 15-29.
[6]
Kumar DA, Murugan S. Performance analysis of Indian stock market index using neural network time series model. 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering. Salem, India. 2013.
[http://dx.doi.org/10.1109/ICPRIME.2013.6496450]
[7]
Dattasharma A, Tripathi PK. Practical inter-stock dependency indicators using time series and derivatives. 2008 IEEE/ACS International Conference on Computer Systems and Applications. Doha, Qatar. 2008.
[http://dx.doi.org/10.1109/AICCSA.2008.4493533]
[8]
Saxena P, Pant B, Goudar RH, Srivastav S, Garg V, Pareek S. Future predictions in Indian stock market through linguistictemporal approach. 2013 7th International Conference on Intelligent Systems and Control (ISCO). Coimbatore, India, 2013..
[http://dx.doi.org/10.1109/ISCO.2013.6481191]
[9]
Gupta A, Dhingra B. Stock market prediction using hidden markov models. 2012 Students Conference on Engineering and Systems. Allahabad, Uttar Pradesh, India. 2012.
[http://dx.doi.org/10.1109/SCES.2012.6199099]
[10]
Banerjee D. Forecasting of Indian stock market using time-series ARIMA model. 2014 2nd International Conference on Business and Information Management (ICBIM). Durgapur, India,. 2014.
[http://dx.doi.org/10.1109/ICBIM.2014.6970973]
[11]
Perwej Y, Perwej A. Prediction of the Bombay Stock Exchange (BSE) market returns using artificial neural network and genetic algorithm. J Intell Learn Syst Appl 2012; 4(2): 108-19.
[12]
Nayyar A, Puri V. Comprehensive analysis & performance comparison of clustering algorithms for big data. Review Comput Eng Res 2017; 4(2): 54-80.
[http://dx.doi.org/10.18488/journal.76.2017.42.54.80]
[13]
Babu CN, Reddy BE. Selected Indian stock predictions using a hybrid ARIMA-GARCH model. 2014 International Conference on Advances in Electronics Computers and Communications. Bangalore, India. 2014.
[http://dx.doi.org/10.1109/ICAECC.2014.7002382]
[14]
Bollen J, Mao H. Twitter mood as a stock market predictor. Computer 2011; (10): 91-4.
[http://dx.doi.org/10.1109/MC.2011.323]
[15]
Solanki VKCHNH. Zonghyu opinion mining: Using machine learning techniques extracting knowledge from opinion mining. IGI Global 2018; 2018: 66-82..
[16]
Hagenau M, Hauser M, Liebmann M, Neumann D. Reading all the news at the same time: Predicting mid-term stock price developments based on news momentum. In 2013 46th Hawaii International Conference on System Sciences, 2013..
[17]
National Stock Exchange of India Ltd. Available from. https://www.nseindia.com/
[18]
Alzubi J, Nayyar A, Kumar A. Machine learning from theory to algorithms: An overview. 2018.
[http://dx.doi.org/10.1088/1742-6596/1142/1/012012]
[19]
Abraham CM, Elayidom MS, Santhanakrishnan T. Analysis and Design of an Efficient Temporal Data Mining Model for the Indian Stock Market Emerging Technologies in Data Mining and Information Security. Singapore: Springer 2019; pp. 615-28.
[20]
Abraham CM., Elayidom MS., Santhanakrishnan T. A novel prediction window based closing value analysis technique for derivative market. J Adv Res Dynamic Control Sys. 2018; 10(15) (pp. 269-275).

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