  This paper introduces a novel methodology for index return forecasting,
blending highly correlated stock prices, advanced deep learning techniques, and
intricate factor integration. Departing from conventional cap-weighted
approaches, our innovative framework promises to reimagine traditional
methodologies, offering heightened diversification, amplified performance
capture, and nuanced market depiction. At its core lies the intricate
identification of highly correlated company clusters, fueling predictive
accuracy and robustness. By harnessing these interconnected constellations, we
unlock a profound comprehension of market dynamics, bestowing both investment
entities and individual enterprises with invaluable performance insights.
Moreover, our methodology integrates pivotal factors such as indexes and ETFs,
seamlessly woven with Hierarchical Risk Parity (HRP) portfolio optimization, to
elevate performance and fortify risk management. This comprehensive
amalgamation refines risk diversification, fortifying portfolio resilience
against turbulent market forces. The implications reverberate resoundingly.
Investment entities stand poised to calibrate against competitors with surgical
precision, tactically sidestepping industry-specific pitfalls, and sculpting
bespoke investment strategies to capitalize on market fluctuations.
Concurrently, individual enterprises find empowerment in aligning strategic
endeavors with market trajectories, discerning key competitors, and navigating
volatility with steadfast resilience. In essence, this research marks a pivotal
moment in economic discourse, unveiling novel methodologies poised to redefine
decision-making paradigms and elevate performance benchmarks for both
investment entities and individual enterprises navigating the intricate
tapestry of financial realms.
