Please use this identifier to cite or link to this item: https://dspace.fsm.ac.in/jspui/handle/123456789/4791
Title: NETFLIX Stocks risk analysis and future prediction
Authors: Arora, Ayushi
Tripathi, Rakhi
Keywords: NETFLIX;Risk Analysis;Stocks;025010
Issue Date: 2023
Publisher: FORE School of Management
Series/Report no.: ;2023
Type: Technical Report
Abstract: The study provides brief information about the stock performance of Netflix and the impact of covid-19 in the world of digital finance. Earlier people were satisfied with only one source of income but Covid totally changed that scenario. People now know that they need more sources of income to fulfill their needs so they are moving towards stock market investments which will generate their passive source of income and will also benefit the stock market as the number of investors are increasing rapidly. The reason behind investing in the stock market is the return this market offers in the short term, so if the bidding is done right one can earn a lot. Moreover, the stock market has a lot of future scope. As is well known, the stock market is prone to swings. It is challenging to forecast nature and the same.Stock prices are influenced by a variety of important physical, psychological, intellectual, and other important aspects in addition to economic variables. On published stock data that has been obtained from Yahoo Finance, stock price prediction models have been created and tested. The experimental findings support the claim that the various model may reasonably predict stock values over a short time horizon. This project analyzes the stock market performance of Netflix, how much return it offers, and should investors invest in Netflix if they are willing to invest in the stock market. Various machine learning and deep learning algorithms are used to train the model for prediction of the closing price of Netflix stocks.
Description: The first author of this project report is student and rest is /are guide /supervisor.
URI: http://10.10.10.29:8080/jspui/handle/123456789/4791
Appears in Collections:PGDM_BDA_2021-23

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