Başlıklardan öngörülere: VADER kullanarak duygu temelli bir piyasa endeksi oluşturmak

This thesis introduces a sentiment-based decision-support model that transforms daily financial news headlines into structured quantitative signals for investment analysis. The study focuses on 43 actively traded stocks, primarily listed on the S\&P 500 index, using a two-year dataset of daily headlines collected from publicly available sources. Sentiment scores are generated through a custom framework based on the VADER lexicon and converted into time series. These scores are then evaluated in three main areas: assessing predictive power using Ordinary Least Squares (OLS) regression models, ranking assets for sentiment-driven portfolio strategies, and serving as explanatory variables in asset pricing models such as \ac{CAPM} and Fama-French. Results show that the model provides both statistical and practical value, outperforming market benchmarks under basic portfolio construction methods.