Forecasting India's NIFTY IT Index
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Sprache:Englisch
Fr. 37.90
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Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
27.01.2022
Verlag
GRINSeitenzahl
92
Maße (L/B/H)
21/14.8/0.7 cm
Gewicht
146 g
Auflage
1. Auflage
Sprache
Englisch
ISBN
978-3-346-52446-1
Master's Thesis from the year 2021 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 1, , language: English, abstract: The purpose of this research is to forecast the following day's closing price for a specific share of a company in the stock market using the "Hidden Markov Model". In this paper, the "Hidden Markov Model" is used to predict some of the stocks of interconnected airline markets. The researchers have developed the "Hidden Markov Model" for forecasting time series. As a result of its ability to model dynamic systems, this model is widely used for the recognition of model and problem classifications. In this article, the researchers examined trends in the historical data set. They inserted the appropriate neighboring prices to the datasets and predicted the next day's exchange. Data collection was secondary. The secondary market was collected from Southwest Airlines for 1.5 years (approximately) from September 17, 2002, to December 16, 2004. The observations of the input data are continuous rather than discrete. The sample size is 4 airline firms (British Airlines, Delta Airlines, Southwest Airlines, and Ryanair Holdings Ltd.) The NIFTY IT index captures the performance of the Indian Information Technology (IT) companies. The NIFTY IT index consists of 10 companies listed on the National Stock Exchange (NSE). The IT sector in India has been recording tremendous growth over the years, where it accounts for a growth rate of 7.5 percent per annum. Time series analysis is a statistical tool that can be used in forecasting the prices of financial assets. In the current study, the NIFTY IT index was forecasted from past data collected over a 10 year period spanning from 2011 to 2020. An ARIMA model is fit and used to forecast the NIFTY IT index. Forecasted values were different from actual prices, suggesting that more influencing independent variables must be include, to improve the model accuracy.
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