Something old, something new : a hybrid approach with ARIMA and LSTM to increase portfolio stability
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- Master Thesis 
In this thesis we seek to examine how modern forecasting approaches can improve estimations of stock pair correlations, and derived from this, contribute to making portfolios more stable. Volatility of financial markets have experienced increases due to the ongoing global pandemic. This amplifies the issues that investors face when assessing the risk related to their investments. We construct a hybrid model consisting of an ARIMA component to explain the linear tendencies of correlation, and a Long Short-Term Memory component to explain the non-linear tendencies. Our approach is populated by data from constituents of Oslo Stock Exchange ranging a time span from 2006 through the third quarter of 2020. Our results indicate that modern approaches to forecasting accrue stronger predictive performances than the conventional methods. Across all test periods our proposed hybrid model achieves an RMSE of 0.186 compared to an average benchmark RMSE of 0.237. However, the implications of these findings are ambiguous as the increase in predictive performance cannot be said to definitively outweigh the increase in cost of implementation. Our thesis contributes to the existing literature by exhibiting the untapped potential of how modern approaches to forecasting can improve accuracy of quantitative inputs for decision making.