Comparing Moving Averages And Polynomial Regression In Financial Trend Analysis

Trend analysis forms the backbone of studies related to financial markets as they give investors and market participants insights into expected market behavior, aiding in investment decisions. This paper compares two methods for determining financial trends: Moving Averages (MAs) and Polynomial Regression (PR). Widely used technical indicators like Simple Moving Averages (SMA) and Exponential Moving Averages (EMA) are popular due to their ease of understanding and effectiveness in smoothing price data by removing noise. Although simpler models like linear or logistic regression can be used if the data is suitable, polynomial regression is considered more flexible as it fits a non-linear curve to the data. This study aims to examine the effectiveness of these two approaches in detecting and predicting financial trends accurately. The research involves calculating SMA and EMA on historical stock price data and applying polynomial regressions of various degrees. A comprehensive comparison will focus on the fit, precision, and predictability of each method in financial trend analysis