The model was created to find correlations between Google trends and a company’s stock price in an attempt to predict future stock prices and create a well-diversified portfolio. Optimal Portfolio – Google Trends & Time Series This model attempts to predict the prices of individual commodities, commodity ETFs, and/or individual stocks of commodity dependent companies using global shipping data. Lake’s Leveragers: Global Shipping Data Quant Model It uses historical trends, market sentiment, and key financial indicators to predict future price movements. Synergistic Intelligence Forecasting System Modelīy using a combination of quantamental insider buying, sentiment analysis, and forecasting models, the Synergistic Intelligence Forecasting System Model attempts to outperform the benchmark. Please see below for a short summary of the students’ final projects from this semester: All of the teams included future strategies to improve their models even further. They were able to create and refine their model throughout the semester with the feedback from the teaching team and alumni advisors. Students applied their finance skills and delivered their final models in Python (high-level, general-purpose programming language). This is the fourth time we have run the Quantitative Investing class and we are impressed by the quality of our students’ work and the improvements they made over the semester. The teams learn how to analyze real data including time-series data, then build and test predictive models.
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