# Quantifying the Risk-Return Tradeoff in Forecasting

arXiv: https://arxiv.org/abs/2605.09712 · HTML: https://mlsanity.com/paper/2605.09712/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 61 references that are in the map.
- First posted on arXiv 10 May 2026.

## Builds on (most cited first, 11 of 11)

- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)
- [Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations, and Forecast Rankings](https://mlsanity.com/paper/1503.08195/) (2015)
- [Forecaster's Dilemma: Extreme Events and Forecast Evaluation](https://mlsanity.com/paper/1512.09244/) (2015)
- [How is Machine Learning Useful for Macroeconomic Forecasting?](https://mlsanity.com/paper/2008.12477/) (2020)
- [Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models](https://mlsanity.com/paper/1706.05280/) (2017)
- [Macroeconomic Forecasting with Large Language Models](https://mlsanity.com/paper/2407.00890/) (2024)
- [To Bag is to Prune](https://mlsanity.com/paper/2008.07063/) (2020)
- [A Neural Phillips Curve and a Deep Output Gap](https://mlsanity.com/paper/2202.04146/) (2022)
- [Dual Interpretation of Machine Learning Forecasts](https://mlsanity.com/paper/2412.13076/) (2024)
- [From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks](https://mlsanity.com/paper/2311.16333/) (2023)

## Abstract (arXiv)

Average forecast accuracy is not the same as forecast reliability. I treat forecast loss differentials relative to a benchmark as a return series. I then evaluate these returns using risk-adjusted performance measures from finance, including the Sharpe ratio, Sortino ratio, Omega ratio, and drawdown-based metrics. I also introduce the Edge Ratio capturing a model's propensity to deliver uniquely informative predictions relative to the forecasting frontier. I apply this framework to U.S. macroeconomic forecasting, comparing econometric benchmarks, machine learning models, a foundation model (TabPFN), and the Survey of Professional Forecasters. While it is often feasible to beat professional forecasters in terms of average accuracy, it is much harder to beat them on a risk-adjusted basis. They rarely exhibit catastrophic failures and often achieve high Edge Ratios, plausibly reflecting the value of contextual judgment. Nonetheless, selected machine learning methods deliver attractive risk profiles for specific targets. The framework naturally extends to meta-analyses across targets, horizons, and samples, illustrated with a density forecast evaluation and the M4 competition.