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An algorithmic approach to predicting US Sector ETF price movement using machine learning techniques. The goal was to create an asset allocation framwrok using ETFs as proxies for secotr behavior, and to test different clustering and predictive models to accomplish this goal using the R programming language.
We undertook an in-depth exploration of asset allocation techniques on selected stocks from the Italian stock market. Our primary goal was to optimize investment portfolios, focusing on effective diversification for enhanced risk management and returns.