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Open Access Journal Article

Time Series Forecasting in Financial Markets

by Michael Smith 1,*
1
Michael Smith
*
Author to whom correspondence should be addressed.
ORS  2023 5(2):45; https://doi.org/10.69610/j.ors.20231219
Received: 19 October 2023 / Accepted: 23 November 2023 / Published Online: 19 December 2023
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Abstract

This paper explores the application of time series forecasting techniques in the context of financial markets. The objective is to analyze how predictive models can be utilized to anticipate future market trends and behaviors. The study begins by reviewing various methods of time series analysis, including autoregressive models, moving averages, exponential smoothing, and advanced techniques such as ARIMA and neural networks. We then delve into the practical implementation of these models using historical data from stock markets, focusing on the United States and Europe. The analysis evaluates the accuracy and reliability of the forecasts generated by these models, considering factors such as market volatility and the influence of external economic indicators. The findings suggest that while time series forecasting can provide valuable insights into market dynamics, it is crucial to adapt the models to the specific characteristics of the financial instruments and to be cautious of the inherent limitations and assumptions involved in the process.

Keywords: Time Series Forecasting; Financial Markets; Autoregressive Models; ARIMA; Neural Networks.;
Copyright: © 2023 by Smith. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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APA Style
Smith, M. (2023). Time Series Forecasting in Financial Markets. Operations Research and Statistics, 5(2), 45. doi:10.69610/j.ors.20231219
ACS Style
Smith, M. Time Series Forecasting in Financial Markets. Operations Research and Statistics, 2023, 5, 45. doi:10.69610/j.ors.20231219
AMA Style
Smith M. Time Series Forecasting in Financial Markets. Operations Research and Statistics; 2023, 5(2):45. doi:10.69610/j.ors.20231219
Chicago/Turabian Style
Smith, Michael 2023. "Time Series Forecasting in Financial Markets" Operations Research and Statistics 5, no.2:45. doi:10.69610/j.ors.20231219

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ACS Style
Smith, M. Time Series Forecasting in Financial Markets. Operations Research and Statistics, 2023, 5, 45. doi:10.69610/j.ors.20231219
AMA Style
Smith M. Time Series Forecasting in Financial Markets. Operations Research and Statistics; 2023, 5(2):45. doi:10.69610/j.ors.20231219
Chicago/Turabian Style
Smith, Michael 2023. "Time Series Forecasting in Financial Markets" Operations Research and Statistics 5, no.2:45. doi:10.69610/j.ors.20231219
APA style
Smith, M. (2023). Time Series Forecasting in Financial Markets. Operations Research and Statistics, 5(2), 45. doi:10.69610/j.ors.20231219

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