The steadily growing access to high-quality spatio-temporal crime count data with a high level of spatial detail allows to uncover interesting relationships between crime types within and between small regional units. Data coherent forecasting of such counts has to take the integer and...

We propose a regression-based algorithm that allows to construct arbitrarily many comparable, multi-annual, consistent time series on monthly, weekly, daily, hourly and minute-by-minute search volume indices based on the scattered data obtained from Google Trends. The accuracy of the algorithm...

This paper studies the dynamics of stock market volatility and retail investor attention measured by internet search queries. We find a strong co-movement of stock market indices’ realized volatility and the search queries for their names. Furthermore, Granger causality is bi-directional: high...

This paper studies the dynamics of stock market volatility and retail investor attention measured by internet search queries. We find a strong co-movement of stock market indices' realized volatility and the search queries for their names. Furthermore, Granger causality is bi-directional: high...

This paper studies the dynamics of stock market volatility and retail investors' attention to the stock market, where attention to the stock market is measured by internet search queries related to the leading stock market index. We find a strong co-movement of the Dow Jones' realized volatility...

We evaluate the usefulness of Google search volume to predict returns and volatility of multiple cryptocurrencies. The analysis is based on a new algorithm which allows to construct mulit-annual, consistent time series of Google search volume indices (SVIs) on various frequencies. As...