| Autor: |
Kaninde Sumedh, Mahajan Manish, Janghale Aditya, Joshi Bharti |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
ITM Web of Conferences, Vol 44, p 03060 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2271-2097 |
| DOI: |
10.1051/itmconf/20224403060 |
| Popis: |
Estimating stock prices has always been a challenging task for researchers in the financial sector. Although the Efficient Market Hypothesis states that it is impossible to accurately predict stock prices, there is work in the literature that has shown that stock price movements can be predicted with the right level of accuracy, if the right variables are selected and appropriate predictor models are developed. those that are flexible. The Stock Market is volatile in nature and the prediction of the same is a cumbersome task. Stock prices depend upon not only economic factors, but they relate to various physical, psychological, rational and other important parameters. In this research work, the stock prices are predicted using Facebook Prophet. Stock price predictive models have been developed and run-on published stock data acquired from Yahoo Finance. Prophet is capable of generating daily, weekly and yearly seasonality along with holiday effects, by implementing regression models. The experimental results lead to the conclusion that Facebook Prophet can be used to predict stock prices for a long period of time with reasonable accuracy. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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