Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ)
For selecting and interpreting appropriate behaviour of proportion between buy/neutral/sell patterns and high/moderate/low returns, the prediction error reduction index is a very useful tool. It is operationally interpretable in terms of the proportional reduction in error of estimation. We first ob...
Main Authors: | , , |
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Format: | Article |
Language: | English |
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Hindawi-Wiley
2022-01-01
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Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2022/8196436 |
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author | Parmod Kumar Paul Om Prakash Mahela Baseem Khan |
author_facet | Parmod Kumar Paul Om Prakash Mahela Baseem Khan |
author_sort | Parmod Kumar Paul |
collection | DOAJ |
description | For selecting and interpreting appropriate behaviour of proportion between buy/neutral/sell patterns and high/moderate/low returns, the prediction error reduction index is a very useful tool. It is operationally interpretable in terms of the proportional reduction in error of estimation. We first obtain the buy/sell pattern using an Optimal Band. The analysis of the association between patterns and returns is based on the Goodman–Kruskal prediction error reduction index (λ). Empirical analysis suggests that the prediction of returns from patterns is more impressive or of less error as compared to the prediction of patterns from returns. We demonstrated the prediction index for Index NIFTY 50, BANK-NIFTY, and NIFTY-IT of NSE (National Stock Exchange), for the period 2010–2020. |
first_indexed | 2024-04-11T18:13:02Z |
format | Article |
id | doaj.art-953ce076cb814b72a34127fec3951d1f |
institution | Directory Open Access Journal |
issn | 1099-0526 |
language | English |
last_indexed | 2024-04-11T18:13:02Z |
publishDate | 2022-01-01 |
publisher | Hindawi-Wiley |
record_format | Article |
series | Complexity |
spelling | doaj.art-953ce076cb814b72a34127fec3951d1f2022-12-22T04:10:02ZengHindawi-WileyComplexity1099-05262022-01-01202210.1155/2022/8196436Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ)Parmod Kumar Paul0Om Prakash Mahela1Baseem Khan2Dept. of MathematicsPower System Planning DivisionDepartment of Electrical and Computer EngineeringFor selecting and interpreting appropriate behaviour of proportion between buy/neutral/sell patterns and high/moderate/low returns, the prediction error reduction index is a very useful tool. It is operationally interpretable in terms of the proportional reduction in error of estimation. We first obtain the buy/sell pattern using an Optimal Band. The analysis of the association between patterns and returns is based on the Goodman–Kruskal prediction error reduction index (λ). Empirical analysis suggests that the prediction of returns from patterns is more impressive or of less error as compared to the prediction of patterns from returns. We demonstrated the prediction index for Index NIFTY 50, BANK-NIFTY, and NIFTY-IT of NSE (National Stock Exchange), for the period 2010–2020.http://dx.doi.org/10.1155/2022/8196436 |
spellingShingle | Parmod Kumar Paul Om Prakash Mahela Baseem Khan Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ) Complexity |
title | Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ) |
title_full | Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ) |
title_fullStr | Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ) |
title_full_unstemmed | Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ) |
title_short | Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ) |
title_sort | analyzing the association between pattern and returns using goodman kruskal prediction error reduction index λ |
url | http://dx.doi.org/10.1155/2022/8196436 |
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