Quantifying the Risk-Return Tradeoff in Forecasting

TL;DR

This paper introduces a finance-inspired framework to evaluate forecast models based on risk-adjusted performance metrics, revealing that beating professional forecasters is challenging on a risk basis but possible with certain machine learning models.

Contribution

It proposes a novel risk-return evaluation framework for forecasting models, incorporating new metrics like the Edge Ratio, and applies it to macroeconomic and forecasting competitions.

Findings

Professional forecasters rarely have catastrophic failures.

Some machine learning models show attractive risk profiles.

Risk-adjusted performance often differs from average accuracy.

Abstract

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.