STOCK MARKET: PRICE MOVEMENT FORECASTING USING ELLIOTT THEORY

Abstract

The article is devoted to the analytical capabilities of the Elliott Wave Theory as a tool for predicting price dynamics in the stock market. The relevance of the topic lies in the growing interest among investors and financial analysts in behavioral approaches to forecasting market cycles. In contrast to strictly statistical or fundamental models, Elliott's theory offers a structured vision of price fluctuations based on crowd psychology and the cyclical nature of market sentiment. The aim of the article is to examine the internal logic of wave formation, to determine the conditions under which individual impulse and corrective waves appear, and to reveal how these patterns can be used to make informed investment decisions. The methodological basis of the study includes visual identification of wave sequences, proportional analysis using Fibonacci retracement and extension levels, and confirmation through momentum indicators such as the Awesome Oscillator. The analysis also integrates a case study of Alphabet Inc. (GOOGL) shares, where a wave-based forecast made in 2023 was verified by actual market developments in 2025. The results demonstrate that Elliott Wave analysis, when used together with Fibonacci tools, enables the recognition of reversal points and target levels with reasonable accuracy, especially in trending market conditions. It also allows investors to avoid entering positions during market peaks, where the probability of a significant correction is high. However, the study emphasizes the need for further improvement of the method due to the inherent subjectivity in wave interpretation and the difficulty of automation. The practical value of the article lies in presenting a structured framework for wave-based analysis, which can support medium- and long-term investment strategies. The findings can be applied by private traders, portfolio managers, and algorithmic model developers aiming to enhance the timing and precision of their entry and exit points on financial markets. The proposed analytical approach may also be extended to highly volatile assets such as cryptocurrencies, where traditional models often fail to capture behavioral dynamics.