A Model of Investment Trend Functions: Principal Component Analysis
Abstract
The applications of matrix method with stochastic terms were established in modeling share prices through quadratic and seasonal trend functions. In particular, the methods of rate of change were adopted to assess the value of share prices on two trend functions. From the results, Mean Squared Error (MSE) was used as a criterion for selection, based on this trend functions: the results show that the Seasonal trend function overtook Quadratic trend. More so, this study also applies Principal Component Analysis (PCA) to analyze the share prices of Access Bank, Fidelity and Merged Bank. The results show that the first principal component explains 81.5% of the variance in the data, indicating a high degree of correlation between the share prices of the three banks. The study provides insight into the relationships between the share prices of the three banks and identifies the underlying factors driving the variance in the data.
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References
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