LAUSR

Recently, machine-learning algorithms and existing financial data-analysis methods have been actively studied. Although the term structure of government bonds has been well-researched, the majority of studies only analyze the characteristics of one country in detail using one method. In this paper, we analyze the term structure and determine the common factors using principal component analysis (PCA) and an autoencoder (AE). We collected data on the government bonds of three countries with major currencies (the US, the UK, and Japan), extracted features, and compared them. In the PCA-based analysis, we reduced the number of dimensions by converting the normalized data into a covariance matrix and checked the first five principal components visually using graphs. In the AE-based analysis, the model consisted of two encoder layers, one middle layer, and two decoder layers, and the number of nodes in the middle layer was adjusted from one to five. As a result, no significant similarity was found for each country in the dataset, and it was appropriate to extract three features in both methods. Each feature extracted by PCA and the AE had a completely different form, and this appears to be due to the differences in the feature extraction methods. In the case of PCA, the volatility of the datasets affected the features, but in the case of AE, the results seemed to be more affected by the size of the dataset. Based on the findings of this study, this topic can be expanded to compare the results of other machine-learning algorithms or countries.