Does Big Data Improve Financial Forecasting? The Horizon Effect

Olivier Dessaint, Thierry Foucault and Laurent Frésard

Working Papers from HAL

Abstract: We study how data abundance affects the informativeness of financial analysts' forecasts at various horizons. Analysts produce forecasts of short-term and long-term earnings and choose how much information to collect about each horizon to minimize their expected forecasting error, net of information acquisition costs. When the cost of obtaining short-term information drops (i.e., more data becomes available), analysts change their information collection strategy in a way that renders their short-term forecasts more informative but that possibly reduces the informativeness of their long-term forecasts. Using a large sample of analysts' forecasts at various horizons and novel measures of their exposure to abundant data (e.g., social media data), we provide empirical support for this prediction, which implies that data abundance can impair the quality of long-term forecasts.

Keywords: Big data; Financial analysts' forecasts; Forecasting horizon; Forecasts' informativeness; Social media (search for similar items in EconPapers)
Date: 2020-11-30
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Working Paper: Does Big Data Improve Financial Forecasting? The Horizon Effect (2020)
Working Paper: Does Big Data Improve Financial Forecasting? The Horizon Effect (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-03031876

DOI: 10.2139/ssrn.3702411

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