The thesis addresses the challenges posed by data quality and quantity in financial time series analysis, particularly in emerging markets. Given the need for robust methodologies to handle data limitations, the study investigates the effectiveness of Generative Adversarial Networks (GANs) in augmenting financial market data to improve the predictive and classification capabilities of machine learning models. The thesis aims to answer the following research questions: (1) How different is the correlation between the features in simulated data to the correlation between the features in real data? and (2) To what extend does the performance of classification models for sovereign yield spreads (aggregation of bond prices) in emerging markets improve when adding generated data to the training of the model? By applying the experiment as the research strategy, the performance of the GAN was assessed in three ways, the first one by measuring the average distance between each correlation in the correlation matrices of the original data and the synthetic data, resulting in an average distance of 0.19 with the best sequence length of the data. The second one by measuring the performance of a classification model, a feed-forward network that labels the data by either increase, decrease or insignificant change, using different ratios of synthetic data in the training set, resulting in a decrease of performance when adding the augmented data. The third way is by applying different analyses such as PCA and t-SNE, and plotting the results of the original data and the generated data against each other in order to check how the data is distributed. The study concludes with limitations of GANs in data augmentation, such as hardware limitation, and type of possible data generated along with suggestions for future research to improve the classification model and the authenticity of the synthetic data.