Forecasting Stock Prices: Exploring the Potential of ARIMA Model for Short-Term Predictions.

Predicting stock prices is a difficult and mysterious task that necessitates substantial effort considering the stock market's unpredictable and uncertain nature. The valuation of stocks is extremely important in the fields of business, economics, and finance, encouraging scholars to investigate the development of effective forecasting models. Given the uncontrollable behavior of stock market investment performance, the short-term picture remains vulnerable to unforeseen difficulties, a reality that is difficult to accept. The aim of the given study is to understand how ARIMA model can help investors and financial managers make informed decisions regarding Meta Stocks. Now a day’s investors are interested in putting their money into the fields which are emerging like tech-based businesses. In this paper we have used the historic data of Meta stocks to predict its future movement in the short run. Academics use scientific approaches to forecast stock values, providing vital tools for investors seeking profit accumulation and expansion. One of the models which have gained popularity in the arena of time series prediction analysis was an autoregressive integrated moving average (ARIMA). In this given study authors want to expound impact of the ARIMA model on building a better stocks price prediction. The authors of the study systematically formulated a prediction model by using timeseries data of Meta Platform Inc. stocks closing prices. We concluded that Meta Inc. data ARIMA model AR (4), AR (20), AR (30), AR (31), MA (4), MA (20), MA. (30), MA. (31) is white noise, it fits the data. The recent ending price of capital stock of Meta Platforms Inc. depends on the previous shocks of 4, 20, 30 and 31 days and the usual unpredictability of the recent price of stock was contingent on the instability in prior 4, 20, 30, and 31 days. The findings express the ARIMA model's ability to address conventional stocks price foretelling techniques, highlighting significant potential for Meta Stocks in terms of near-future forecasting given its severe volatility. This study can be especially helpful for trading near-term planning or changing long-term budgetary conditions in anticipated markets.