Quantitative Analysis

Principal Component Analysis (PCA) is a key technique in unsupervised learning widely used in machine learning and statistics to analyze data and reduce data dimensionality. Its core idea is to break down the original data into representative principal components, achieving dimensionality reduction and providing a new description of the data.

Quantitative Investing requires historical data to perform backtesting, examining the feasibility of our investing strategy. Therefore, it is pivotal to ensure that the data used for analyzing always aligns with the information the investors have when making their decisions. For instance, in order to pursue correctness, accounting databases update new numbers when financial restatements occur. However, using the data after financial restatement for backtesting will prevent investors from accurately reconstructing the actual market conditions at that time, and will lead to information bias, reducing the credibility of the backtesting result.

The alpha obtained from the Fama&French three-factor model is used to construct a long-short strategy and backtest the performance against the market return. Highlights： Difficulty：★★☆☆☆ The Fama&French three-factor model is used to calculate the alpha of Taiwan-listed stocks, and the top 20% of stocks with the highest alpha and the bottom 20% of stocks with […]