Macroeconomic Indicators

Generative AI, Managerial Expectations, and Economic Activity ArXiv ID: 2410.03897 “View on arXiv” Authors: Unknown Abstract We use generative AI to extract managerial expectations about their economic outlook from 120,000+ corporate conference call transcripts. The resulting AI Economy Score predicts GDP growth, production, and employment up to 10 quarters ahead, beyond existing measures like survey forecasts. Moreover, industry and firm-level measures provide valuable information about sector-specific and individual firm activities. A composite measure that integrates managerial expectations about firm, industry, and macroeconomic conditions further significantly improves the forecasting power and predictive horizon of national and sectoral growth. Our findings show managerial expectations offer unique insights into economic activity, with implications for both macroeconomic and microeconomic decision-making. ...

Stock Volatility Prediction Based on Transformer Model Using Mixed-Frequency Data ArXiv ID: 2309.16196 “View on arXiv” Authors: Unknown Abstract With the increasing volume of high-frequency data in the information age, both challenges and opportunities arise in the prediction of stock volatility. On one hand, the outcome of prediction using tradition method combining stock technical and macroeconomic indicators still leaves room for improvement; on the other hand, macroeconomic indicators and peoples’ search record on those search engines affecting their interested topics will intuitively have an impact on the stock volatility. For the convenience of assessment of the influence of these indicators, macroeconomic indicators and stock technical indicators are then grouped into objective factors, while Baidu search indices implying people’s interested topics are defined as subjective factors. To align different frequency data, we introduce GARCH-MIDAS model. After mixing all the above data, we then feed them into Transformer model as part of the training data. Our experiments show that this model outperforms the baselines in terms of mean square error. The adaption of both types of data under Transformer model significantly reduces the mean square error from 1.00 to 0.86. ...