A Comparative Analysis of Stock Return for Amazon and Alibaba Based on Time Series Model and Control Chart

Abstract

E-commerce giants Amazon and Alibaba significantly impact the global economy, but their stock return volatility presents challenges for investors and analysts. Traditional volatility models often fail to capture sudden fluctuations, necessitating systematic monitoring through control charts. This study aims to (1) evaluate the performance of the  Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model in representing stock returns, (2) detect stock return volatility using residual-based control charts (RBCC), (3) compare the fluctuation patterns of stock returns of Amazon and Alibaba. Using daily opening prices from August 1, 2023, to July 31, 2024, stock returns are analyzed with a GARCH (1,1) model to estimate volatility. Residuals are then examined with Exponential Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) control charts to detect anomalies. By linking these anomalies to economic events, key drivers of stock return fluctuations are identified. Findings indicate that residual-based control charts effectively detect unusual stock return behaviors, highlighting their potential in financial market analysis. This study offers investors a systematic approach to monitoring stock volatility and contributes to financial research by demonstrating the effectiveness of RBCC in detecting structural market shifts and inefficiencies.