Disagreement between Human and Machine Predictions

Daisuke Miyakawa and Kohei Shintani
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Daisuke Miyakawa: Associate Professor, Hitotsubashi University Business School (E-mail: dmiyakawa@hub.hit-u.ac.jp)

No 20-E-11, IMES Discussion Paper Series from Institute for Monetary and Economic Studies, Bank of Japan

Abstract: We document how professional analysts' predictions of firm exits disagree with machine-based predictions. First, on average, human predictions underperform machine predictions. Second, however, the relative performance of human to machine predictions improves for firms with specific characteristics, such as less observable information, possibly due to the unstructured information used only in human predictions. Third, for firms with less information, reallocating prediction tasks from machine to analysts reduces type I error while simultaneously increasing type II error. Under certain conditions, human predictions can outperform machine predictions.

Keywords: Machine Learning; Human Prediction; Disagreement (search for similar items in EconPapers)
JEL-codes: C10 C55 G33 (search for similar items in EconPapers)
Date: 2020-08
New Economics Papers: this item is included in nep-bec, nep-big and nep-cmp
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Citations: View citations in EconPapers (4)

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