Comparative analysis on forecasting methods and how to choose a suitable one: case study in financial time series

Forecasting in the financial markets is vital for informed decision-making, risk management, efficient capital allocation, asset valuation, and economic stability. This study thoroughly examines forecasting techniques to predict the ۳۰-day closing prices of APPLE in a select group of ۱۰۰ prominent companies chosen based on their revenue profiles. list of ۱۰۰ big Companies published by The Fortune Global ۵۰۰. The evaluated forecasting methods encompass a broad spectrum of approaches, including Moving Average (MA), Exponential Smoothing, Autoregressive Integrated Moving Average (ARIMA), Simple Linear Regression, Multiple Regression, Decision Trees, Random Forests, Neural Networks, and Support Vector Regression (SVR). The information on the dataset was downloaded from Yahoo Finance, and all methods were evaluated in Python. The MAPE method is used to measure the accuracy of the examined methods. Based on the selected dataset, Our findings reveal that SVR, Simple Linear Regression, Neural Networks, and ARIMA consistently outperform other methods in accurately predicting the ۳۰-day APPLE closing prices. In contrast, the Moving Average method exhibits subpar performance, primarily due to its inherent limitations in accommodating the intricate dynamics of financial data, such as trends, seasonality, and unexpected shocks. In conclusion, this comprehensive analysis enhances our understanding of forecasting techniques and paves the way for more informed and precise decision-making in the ever-evolving realm of financial markets.

Keywords:

forecasting methods , financial assets , Time series , Mean Absolute Percentage Error (MAPE)