Detecting Fundamental Revaluation Episodes: Volume Spikes and Overnight Price Gaps in Amazon Stock (2000-2025)
K. Kiruthika
International Journal of Web Applications, Vol. 18, No. 1, 2026.
DOI: https://doi.org/10.6025/ijwa/2026/18/1/25-33
Link: https://www.dline.info/ijwa/fulltext/v18n1/ijwav18n1_3.pdf
Abstract:
This study investigates market anomalies in Amazon's stock price behavior through a 25-year analysis
(2000-2025) of daily OHLCV data to identify high probability markers of fundamental revaluation episodes.
Challenging the Efficient Market Hypothesis, the research examines two primary anomaly detection
methodologies: identifying extreme volume spikes and quantifying overnight price gaps. Volume analysis
revealed ten sessions with more than 100 million shares traded, with notable clustering in April 2007 and a
concentration of events prior to 2010 reflecting Amazon's transition from speculative growth stock to
established market leader. Price gap analysis identified ten sessions with absolute overnight gaps exceeding
$9.90, predominantly occurring between 2021-2024 amid heightened macroeconomic volatility and sectorspecific
pressures on technology valuations. The study integrates 20-day, 50-day, and 200-day simple
moving averages to contextualize anomalies within hierarchical trend structures. Key findings demonstrate
that extreme volume events serve as critical inflection markers where market consensus undergoes rapid
recalibration, while overnight price gaps function as sentiment discontinuities frequently initiating multiday
directional sequences. Confluence events where price crosses key moving averages on elevated volume-
exhibited heightened predictive value for trend continuation or reversal. The research establishes that
monitoring deviations from baseline volume and gap distributions provides early warning signals of potential
trend transitions. Limitations include the need for catalyst attribution through correlation with news events
and percentage based normalization to account for stock splits. This framework offers practitioners a robust
methodology for distinguishing noise from signal in price behavior, with implications for algorithmic trading
and risk management in volatile equity markets.