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In the 21st century, with the advent of big data, data will emerge as the most powerful tool, enabling investment thoughts or ideas to be transformed into mathematical models. Backed by comprehensive data on real situations for analysis, employing large data sets will become a reasonable and effective approach for investment analysis, in particular, portfolio investment. The aim of this study is to gain a relatively more comprehensive understanding of the application of statistics to investment portfolios. It focuses on the principle of the factor model. The advantages and disadvantages of different factors in different situations are compared. In the experimental part, the same index, such as adjusted R2, is applied to the same data in different models for comparison. The experimental findings demonstrate that the three-factor model can more accurately fit the data and is more useful than the CAPM model. The three-factor model is inferior to the five-factor model, which is better suited to some specific interactions.
Siming Yi (Fri,) studied this question.