A project based on principle component analysis also known as PCA, which is used to reduce the data set of all the stock returns and give us the most suited stock returns. The technique assumes that the data is normal and turns the numourous data into a linear map. The following are some features of the same.
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Data Preprocessing: Cleaning and preprocessing of financial data, including handling missing values, normalization, and scaling.
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Principal Component Analysis (PCA): Implementing PCA to identify the principal components that explain the variance in the dataset.
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Eigen Portfolio Construction: Utilizing the eigenvectors obtained from PCA to construct the eigen portfolio, which is a weighted combination of assets.
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Risk Management: Assessing and managing risk associated with the eigen portfolio, including measures like volatility, covariance, and correlation.
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Portfolio Optimization: Optimizing the eigen portfolio by adjusting weights to achieve desired risk-return profiles or other objectives.
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Performance Evaluation: Evaluating the performance of the eigen portfolio using metrics such as Sharpe ratio, cumulative returns, and drawdown analysis.
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Visualization: Visualizing the results of PCA, eigen portfolio composition, and performance metrics through charts, graphs, and plots.
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Backtesting: Backtesting the eigen portfolio strategy to assess its historical performance under various market conditions.
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Parameter Tuning: Fine-tuning parameters of the PCA and portfolio optimization process to enhance performance and robustness.
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Deployment: Deploying the eigen portfolio project into a usable application or system for real-world investment decision-making.