A predictive analytics framework for opportunity sensing in stock market

Large volume, random fluctuations and distractive patterns in raw price data lead to overfitting in stock price prediction. Thus research papers in this area suffer from multiple limitations: Very short prediction period from one day to one week, consideration of few stocks only instead of whole of...

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Main Authors: Shruti Mittal, C.K. Nagpal
Format: Article
Language:English
Published: Elsevier 2022-06-01
Series:Kuwait Journal of Science
Online Access:https://journalskuwait.org/kjs/index.php/KJS/article/view/18993
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author Shruti Mittal
C.K. Nagpal
author_facet Shruti Mittal
C.K. Nagpal
author_sort Shruti Mittal
collection DOAJ
description Large volume, random fluctuations and distractive patterns in raw price data lead to overfitting in stock price prediction. Thus research papers in this area suffer from multiple limitations: Very short prediction period from one day to one week, consideration of few stocks only instead of whole of stock market spectrum, exploration of more suitable machine learning algorithms. By overcoming the problems of raw data these limitations can be conquered. Proposed work uses a supervised machine learning approach on statistically learned macrofeatures obtained from gist of input data, free from raw data drawbacks, to predict the price band for the upcoming month and a half for almost all NIFTY50 stocks. The predicted bands are tested for precision in comparison with actual stock price bands.  Motivating outcomes so obtained were used to sense opportunity for buying / selling / wait. The results showed that the proposed strategy is quite effective and can be successfully monetized. 
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publisher Elsevier
record_format Article
series Kuwait Journal of Science
spelling doaj-art-a99067a9f7a34ec59a0afdeff0546f692025-08-20T02:30:32ZengElsevierKuwait Journal of Science2307-41082307-41162022-06-0110.48129/kjs.splml.18993A predictive analytics framework for opportunity sensing in stock marketShruti Mittal0C.K. NagpalJCBUST YMCA, Faridabad Large volume, random fluctuations and distractive patterns in raw price data lead to overfitting in stock price prediction. Thus research papers in this area suffer from multiple limitations: Very short prediction period from one day to one week, consideration of few stocks only instead of whole of stock market spectrum, exploration of more suitable machine learning algorithms. By overcoming the problems of raw data these limitations can be conquered. Proposed work uses a supervised machine learning approach on statistically learned macrofeatures obtained from gist of input data, free from raw data drawbacks, to predict the price band for the upcoming month and a half for almost all NIFTY50 stocks. The predicted bands are tested for precision in comparison with actual stock price bands.  Motivating outcomes so obtained were used to sense opportunity for buying / selling / wait. The results showed that the proposed strategy is quite effective and can be successfully monetized.  https://journalskuwait.org/kjs/index.php/KJS/article/view/18993
spellingShingle Shruti Mittal
C.K. Nagpal
A predictive analytics framework for opportunity sensing in stock market
Kuwait Journal of Science
title A predictive analytics framework for opportunity sensing in stock market
title_full A predictive analytics framework for opportunity sensing in stock market
title_fullStr A predictive analytics framework for opportunity sensing in stock market
title_full_unstemmed A predictive analytics framework for opportunity sensing in stock market
title_short A predictive analytics framework for opportunity sensing in stock market
title_sort predictive analytics framework for opportunity sensing in stock market
url https://journalskuwait.org/kjs/index.php/KJS/article/view/18993
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AT cknagpal apredictiveanalyticsframeworkforopportunitysensinginstockmarket
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