Abstract

The prediction of the stock market was one of the greatest obstacles. Since there are many reserves whose values are extremely unpredictable, good returns from investment must be calculated and determined. We tried our best to address these challenges by picking an index (a collection of stocks in the same sector) and developed an arbitration style trading mechanism in which the leading stock of that index could be chosen by correlation analyzes and technical analyses. When people hear the trade in similarities, they will wonder of arbitration. And that’s why we’re dreaming about investing in arbitrage. Although this is not true: arbitrariness is when two entities that are similar or rather connected are out of whack. So, we will look at a stock and its index and they are not identical, so they’re never out of whack. There are rules to define the entries and exits in a chosen stock in the trading system. The additional advantage of this method is to forecast the movement of one stock using its relationship with another stock of the same index. Established method is split into two phases: stock correlation and technological analysis. Experiments between July 2017 and June 2020 indicate that the know-how could include an index and its entries and exits.

Details

Title
Arbitrage-Type Trade Using Correlation Analysis
Author
Dhyani, Bijesh 1 ; Nigam, Pushkar 1 ; Kumar, Ankit 2 ; Kumar, Abhishek 3 ; Venkatesan, K 4 ; Ambeth Kumar, V D 5 

 Graphic Era hill University, Dehradun, Uttarakhand, India 
 Department of Computer Science & Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, Rajasthan, India 
 JAIN (Deemed to be University), Bangalore, India 
 Sanjivini College of Engineering, Savitribai Phule University, Pune, India 
 PEC, Anna University, Chennai, India 
Publication year
2021
Publication date
Mar 2021
Publisher
IOP Publishing
ISSN
17578981
e-ISSN
1757899X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2512943644
Copyright
© 2021. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.