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Volume 2919, Issue 1
25 March 2024
SECOND INTERNATIONAL CONFERENCE ON COMPUTING AND COMMUNICATION NETWORKS (ICCCN 2022)
19–20 November 2022
Manchester, UK
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Research Article| March 25 2024
Tejinder Singh;
Mahendra Pandey;
Mahendra Pandey b)
1
University School of Business (Commerce), Chandigarh University
, Mohali,
India
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Vikas Sharma;
Vikas Sharma c)
1
University School of Business (Commerce), Chandigarh University
, Mohali,
India
c)Corresponding author: vikas.sharma62@yahoo.com
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Ksh*tiz Jangir;
Ksh*tiz Jangir d)
1
University School of Business (Commerce), Chandigarh University
, Mohali,
India
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Nitin Pathak
Nitin Pathak e)
1
University School of Business (Commerce), Chandigarh University
, Mohali,
India
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Author & Article Information
c)Corresponding author: vikas.sharma62@yahoo.com
a)
rednijetchd@gmail.com
b)
pandeymahendra9@gmail.com
d)
jangirksh*tiz@gmail.com
e)
nitin17pathak@gmail.com
AIP Conf. Proc. 2919, 090014 (2024)
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Citation
Tejinder Singh, Mahendra Pandey, Vikas Sharma, Ksh*tiz Jangir, Nitin Pathak; The role of algorithmic trading in the strengthening of financial markets. AIP Conf. Proc. 25 March 2024; 2919 (1): 090014. https://doi.org/10.1063/5.0184463
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Algo trading is the manifested form of artificial intelligence. Algo trading, also called algorithmic trading, is used in financial markets to place orders using computer programming. However, retail investors are perplexed by the implications of artificial intelligence on their investment portfolios. This article aims to find out how AI has deepened the effect of open interest and options volumes on monthly future prices. The share prices of 30 companies, with ten companies each from the small, mid, and large-cap listed on the National Stock Exchange, were considered. The time period taken for the study was from 2017 to 2021. The ordinary least square regression statistical technique was used, and the analysis was carried out by regressing two independent variables, namely open interest and options volume, against monthly future prices. The results were consistent with the existing studies. The coefficients of open interest predictors were found to be statistically significant during the period of study. The contemporary findings suggest that the use of algo trading has resulted in a deeper reflection of the relationship between option’s open interest, volumes, and future prices.
Topics
Computer programming, Financial economics, Artificial intelligence, Regression analysis
REFERENCES
1.
T. Singh N. Pathak
Emerging Role of Artificial Intelligence
,”
J. Crit. Rev.
, Vol.
7
, no.
16
, pp.
1370
–
1373
,
2020
.
2.
T. Singh
Artificial intelligence-application in the field of Indian banking sector Artificial Intelligence-Application in the Field of Indian Banking Sector
,”
2022
, Vol.
020002
, no. October.
3.
B. C. L. P. L. Hermes Investment Management, Marsh, Wyman Oliver
,
ARTIFICIAL INTELLIGENCE APPLICATIONS IN FINANCIAL SERVICES
.
Marsh & Mclennan Companies
,
2019
.
4.
D. K. Sharma H. S. Hota K. Brown R. Handa
Integration of genetic algorithm with artificial neural network for stock market forecasting
,”
Int. J. Syst. Assur. Eng. Manag.
,
2021
, doi:
.
5.
M. A. M. Al Janabi
International Series in Operations Research and Management Science
, Vol.
305
.
Springer, Tecnologico de Monterrey
,
EGADE Business School, Mexico City, Mexico
, pp.
155
–
188
,
2021
. doi:
.
6.
J. Arifovic X. He L. Wei
High Frequency Trading in FinTech age: AI with Speed.
2019
.
7.
E. Micheler A. Whaley
Regulatory Technology: Replacing Law with Computer Code
,”
Eur. Bus. Organ. Law Rev.
, Vol.
21
, no.
2
, pp.
349
–
377
,
2020
, doi:
.
8.
A. Ashta H. Herrmann
Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance
,”
Strateg. Chang.
, Vol.
30
, no.
3
, pp.
211
–
222
,
2021
, doi:
.
9.
D. N. P. Dr. Vikas Sharma Dr. Pooja Sharma
Stock Return and Trade Volume Analysis of Selected Indian Cement Companies’ Stocks
,”
J. Contemp. Issues Bus. Gov.
, Vol.
28
, no.
1
, pp.
59
–
69
, Jan.
2022
, doi:
.
10.
M. A. Boden
Artificial intelligence: A very short introduction.
2018
.
11.
W. Li F. Mei
Asset returns in deep learning methods: An empirical analysis on SSE 50 and CSI 300
,”
Res. Int. Bus. Financ.
, Vol.
54
,
2020
, doi:
.
12.
T. A. Siddiqui
Non-Linear Relationship between Macroeconomic Variables and Stock Prices in India
:
An Artificial Neural Networks Approach NON-LINEAR RELATIONSHIP BETWEEN MACROECONOMIC VARIABLES AND STOCK PRICES IN INDIA : AN ARTIFICIAL
,” Vol.
14
, no.
2
,
2014
.
13.
K. J. Oh T. Y. Kim
Financial market monitoring by case-based reasoning
,”
Expert Syst. Appl.
, Vol.
32
, no.
3
, pp.
789
–
800
,
2007
, doi:
.
14.
K. T. Latos Christian L. Dunis Peter W. Middleton A. Karathanasopoulos
Artifi cial Intelligence in Financial Markets
.
Palgrave Macmillan
,
2016
.
15.
M. S. Farahani A. Esfahani
Opportunities and Challenges of Applying Artificial Intelligence in the Financial Sectors and Startups during the Coronavirus Outbreak
,” Vol.
2
, no.
4
, pp.
33
–
55
,
2022
.
16.
R. H. Huang C. C. Wang O. X. Zhang
The development and regulation of robo-advisors in Hong Kong: empirical and comparative perspectives
,”
J. Corp. Law Stud.
,
2022
, doi:
.
17.
M. Rahman T. H. Ming T. A. Baigh M. Sarker
Adoption of artificial intelligence in banking services: an empirical analysis
,”
Int. J. Emerg. Mark.
,
2022
, doi:
.
18.
E. Dijmărescu
Towards a Fiduciary Digital Currency
,”
Rom. J. Eur. Aff.
, Vol.
21
, no.
2
, pp.
5
–
18
,
2021
.
19.
C. Milana A. Ashta
Artificial intelligence techniques in finance and financial markets: A survey of the literature
,”
Strateg. Chang.
, Vol.
30
, no.
3
, pp.
189
–
209
,
2021
, doi:
.
20.
P. Roszkowska
Fintech in financial reporting and audit for fraud prevention and safeguarding equity investments
,”
J. Account. Organ. Chang.
, Vol.
17
, no.
2
, pp.
164
–
196
,
2021
, doi:
.
21.
T. Hendershott X. Zhang J. Leon Zhao Z. Zheng
Fintech as a game changer: Overview of research frontiers
,”
Inf. Syst. Res.
, Vol.
32
, no.
1
, pp.
1
–
17
,
2021
, doi:
.
22.
F. J. Fabozzi A. Leccadito R. S. Tunaru
Extracting market information from equity options with exponential Lévy processes
,”
J. Econ. Dyn. Control
, Vol.
38
, pp.
125
–
141
,
2014
, doi:
.
23.
T. G. Andersen V. Todorov M. Ubukata
Tail risk and return predictability for the Japanese equity market
,”
J. Econom.
, Vol.
222
, no.
1
, Part B, pp.
344
–
363
,
2021
, doi:
.
24.
D. K. Hayunga R. D. Holowczak P. P. Lung T. Nishikawa
Derivatives traders’ reaction to mispricing in the underlying equity
,”
J. Bank. Financ.
, Vol.
36
, no.
9
, pp.
2438
–
2454
,
2012
, doi:
.
25.
D. Liu Q. Qiu J. C. Hughen P. Lung
Price discovery in the price disagreement between equity and option markets: Evidence from SSE ETF50 options of China
,”
Int. Rev. Econ. Financ.
, Vol.
64
, pp.
557
–
571
,
2019
, doi:
.
26.
T. Griffith B. Roseman D. Shang
The effects of an increase in equity tick size on stock and option transaction costs
,”
J. Bank. Financ.
, Vol.
114
, p. 105782,
2020
, doi:
.
27.
R. Ruiz-Cruz
Portfolio modeling for an algorithmic trading based on control theory
,”
IFAC-PapersOnLine
, Vol.
51
, no.
13
, pp.
390
–
395
,
2018
, doi:
.
28.
J. Omole A. Sensoy G. Gulay
Order imbalance and commonality: Evidence from the options market
,”
Borsa Istanbul Rev.
, Vol.
22
, no.
1
, pp.
1
–
11
,
2022
, doi:
.
29.
C. Evans K. Pappas F. Xhafa
Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation
,”
Math. Comput. Model.
, Vol.
58
, no.
5
-6, pp.
1249
–
1266
,
2013
, doi:
.
30.
I. Blanco D. Wehrheim
The bright side of financial derivatives: Options trading and firm innovation
,”
J. financ. econ.
, Vol.
125
, no.
1
, pp.
99
–
119
,
2017
, doi:
.
31.
H. Berkman H. Malloch
Stock valuation during the COVID-19 pandemic: An explanation using option-based discount rates
,”
J. Bank. Financ.
, no. xxxx, p.
106386
,
2021
, doi:
.
32.
R. Faff D. Hillier
Complete markets, informed trading and equity option introductions
,”
J. Bank. Financ.
, Vol.
29
, no.
6
, pp.
1359
–
1384
,
2005
, doi:
.
33.
R. Pathak N. Rastogi
Informational Role of Options Open Interests and Volume in Forecasting Future Prices: A Study on Indian Market
,”
IUP J. Financ. Econ.
, Vol.
VIII
, no.
3
, pp.
49
–
61
,
2010
.
34.
S. P. and A. S. C, “
Futures Trading: Informational Content of Open Interest and Trading Volume on Futures Price
,”
Asian J. Econ. Empir. Res.
, Vol.
3
, no.
2
, pp.
156
–
162
,
2016
, doi:
.
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