Prediction Stock Price Using Time Series Analysis

EasyChair Preprint 10306

Abstract

Predicting stock prices is a complex and challenging task that has garnered significant interest from investors and researchers alike. Time series analysis is a powerful technique that can be used to forecast future stock prices based on historical data. This approach involves analyzing trends, patterns, and other statistical features of stock price data over time to identify underlying relationships and make predictions.

One of the primary benefits of time series analysis for stock price prediction is its ability to account for the inherent volatility and unpredictability of financial markets. By modeling and forecasting changes in stock prices over time, investors can make more informed decisions about when to buy, sell, or hold investments.

However, time series analysis also presents some challenges, such as the need to carefully select appropriate statistical models, deal with missing or incomplete data, and account for other factors that may influence stock prices, such as economic trends and political events.

Despite these challenges, time series analysis has proven to be a valuable tool for predicting stock prices and informing investment decisions. As financial markets continue to evolve and become increasingly complex, the use of time series analysis is likely to become even more important for investors seeking to maximize returns while managing risk.

Keyphrases: Investment Decisions, Maximizing Returns, Predicting, Volatility, economic trends, financial markets, historical data, missing data, patterns, political events, risk management, statistical models, stock price, time series analysis, trends

@booklet{EasyChair:10306,
  author    = {Neeraj Larhgotra and Anup Lal Yadv},
  title     = {Prediction Stock Price Using Time Series Analysis},
  howpublished = {EasyChair Preprint 10306},
  year      = {EasyChair, 2023}}