Macro-Financial Condition Index Construction and Forecasting Based on Machine Learning Techniques: Empirical Evidence from China

Identifying and forecasting macro-financial conditions is critical to stabilizing the economy. This study aims to develop a novel methodology for constructing China’s Financial Conditions Index, utilizing monthly data from six major Chinese financial markets (comprising 33 key financial indicators) along with 25 external macroeconomic variables from both China and the United States, spanning January 2002 to June 2024. Although the traditional TVP-FAVAR model can capture the linear relationship in the financial market, it cannot adequately characterize the nonlinear or asymmetric nature of the macro-financial conditions exhibited when major risk events occur at home and abroad. In this paper, we propose an innovative kernel factor-augmented time-varying parameter vector autoregressive model (TVP-KFAVAR), which can better capture the nonlinear nature of the macro-financial situation. It is shown that the TVP-KFAVAR model successfully reflects the impact of major domestic and international risk events on China’s Financial Conditions Index. Meanwhile, the ARIMA model and five machine learning techniques (GRU, LSTM, BiLSTM, TCN and Transformer) are used in this study to predict the Macro-Financial Conditions Index, and it is found that the vast majority of the machine learning techniques outperform the traditional time-series models in terms of forecasting performance. TCN has the outstanding prediction performance under different input configurations. This study can provide policymakers with a powerful tool for macro-financial regulation and risk early warning, and help improve macro-financial management in emerging markets.