Financial indicators are the most used variables in measuring the business performance of companies, signaling about the financial position, comprehensive income, and other significant reporting aspects. In a competitive environment, the performance measurement model allows performing...

This paper predicts the likelihood that a restaurant will close within the next one to two years using a Yelp restaurant dataset and a high dimensional gradient boosting machine called LightGBM (hereafter GBM). This model, trained on more than 20,000 individual restaurants, has an accuracy just...

This paper considers the issue of predicting cyclical turning points using real-time diffusion indexes constructed using a large data set from March 2005 to September 2014. We construct diffusion indexes at the monthly frequency, compare several smoothing and signal extraction methods, and...

In this paper we investigate whether differences exist among forecasts using real-time or latest-available data to predict gross domestic product (GDP). We employ mixed-frequency models and real-time data to reassess the role of survey data relative to industrial production and orders in...

The prompt availability of information on the current state of the economy in real-time is required for prediction purposes and crucial for timely policy adjustment and economic decision-making. While important macroeconomic indicators are reported only quarterly and also published with...

Survey data can offer timely information on the current state of the economy and its short-term outlook. In this paper, we propose a "Swiss Economic Confidence Index" (SEC). This is a monthly indicator based on aggregating a selection of individual survey indicators, which we show to have...

Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models that exhibit model dependence and have high data demands. We explore deep neural networks as an opportunity to improve upon...

This paper presents a computational approach for predicting the S&P CNX Nifty 50 Index. A neural network based model has been used in predicting the direction of the movement of the closing value for the next day of trading. The model presented in the paper also confirms that it can be used to...

Uncertainty may affect economic behavior of individuals and firms in a wide variety of ways, with typically negative consequences for economic growth. It is due to this fact, combined with rising political uncertainty observed lately in many countries, that uncertainty has gained increasing...