Navigating Inflation Challenges: AI-Based Portfolio Management Insights

Tibor Bareith, Tibor Tatay () and László Vancsura
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Tibor Bareith: HUN-REN, Centre for Economic and Regional Studies, Institute of Economics, 1097 Budapest, Hungary
Tibor Tatay: Department of Statistics, Finances and Controlling, Széchenyi István University, 9026 Győr, Hungary
László Vancsura: Doctoral School of Economics and Regional Sciences, Hungarian University of Agriculture and Life Sciences, 7400 Kaposvár, Hungary

Risks, 2024, vol. 12, issue 3, 1-16

Abstract: After 2010, the consumer price index fell to a low level in the EU. In the euro area, it remained low between 2010 and 2020. The European Central Bank has even had to take action against the emergence of deflation. The situation changed significantly in 2021. Inflation jumped to levels not seen for 40 years in the EU. Our study aims to use artificial intelligence to forecast inflation. We also use artificial intelligence to forecast stock index changes. Based on the forecasts, we propose portfolio reallocation decisions to protect against inflation. The forecasting literature does not address the importance of structural breaks in the time series, which, among other things, can affect both the pattern recognition and prediction capabilities of various machine learning models. The novelty of our study is that we used the Zivot–Andrews unit root test to determine the breakpoints and partitioned the time series into training and testing datasets along these points. We then examined which database partition gives the most accurate prediction. This information can be used to re-balance the portfolio. Two different AI-based prediction algorithms were used (GRU and LSTM), and a hybrid model (LSTM–GRU) was also included to investigate the predictability of inflation. Our results suggest that the average error of the inflation forecast is a quarter of that of the stock market index forecast. Inflation developments have a fundamental impact on equity and government bond returns. If we obtain a reliable estimate of the inflation forecast, we have time to rebalance the portfolio until the inflation shock is incorporated into government bond returns. Our results not only support investment decisions at the national economy level but are also useful in the process of rebalancing international portfolios.

Keywords: portfolio management; inflation; time series forecast; neural networks; deep learning; Zivot–Andrews unit root test (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2024
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