---
title: ML Earnings Forecasts and Investor Expectations
url: https://www.ml-quant.com/papers/ssrn/4673392/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4673392
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4673392
featured: 2024-01-03
citations: unknown
topic: Econometrics & Forecasting
---


# ML Earnings Forecasts and Investor Expectations

The research indicates that machine learning can enhance earnings forecasts, especially for small firms and longer horizons, and that investors' expectations align with the best machine forecast.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4673392
- Identifier: SSRN 4673392
- Released: 2023-07-10
- First featured: Quant Letter No. 31 (2024-01-03): https://www.ml-quant.com/issues/2024-01-03/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

## Related

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- [Earnings Forecast Accuracy](https://www.ml-quant.com/papers/ssrn/4827682/): The study examines the link between model-based earnings forecast accuracy and portfolios sorted on implied cost of capital, highlighting that machine learning models provide the highest return spreads and the importance of considering transaction costs in financial analysis.
- [TKAN: Temporal Kolmogorov-Arnold Networks](https://www.ml-quant.com/papers/ssrn/4825654/): The article presents Temporal Kolomogorov-Arnold Networks (TKANs), a new neural network design that merges the benefits of Recurrent Neural Networks and Long Short-Term Memory for improved multistep time series forecasting.
