Movement Prediction-Adjusted Naive Forecast: Is the Naive Baseline Unbeatable in Financial Time Series Forecasting?

TL;DR

This paper proposes MPANF, a novel forecast combination method that refines naive forecasts using directional movement predictions, demonstrating improved accuracy in financial time series forecasting when movement predictions are reliable.

Contribution

The study introduces MPANF, a new method that systematically adjusts naive forecasts with movement predictions, outperforming traditional benchmarks in financial data.

Findings

MPANF outperforms benchmarks with movement prediction accuracy of ~0.55.

MPANF is effective when reliable movement predictions are available.

The method improves forecast accuracy across multiple error metrics.

Abstract

In financial time series forecasting, the naive forecast is a notoriously difficult benchmark to surpass because of the stochastic nature of the data. Motivated by this challenge, this study introduces the movement prediction-adjusted naive forecast (MPANF), a forecast combination method that systematically refines the naive forecast by incorporating directional information. In particular, MPANF adjusts the naive forecast with an increment formed by three components: the in-sample mean absolute increment as the base magnitude, the movement prediction as the sign, and a coefficient derived from the in-sample movement prediction accuracy as the scaling factor. The experimental results on eight financial time series, using the RMSE, MAE, MAPE, and sMAPE, show that with a movement prediction accuracy of approximately 0.55, MPANF generally outperforms common benchmarks, including the naive forecast, naive forecast with drift, IMA(1,1), and linear regression. These findings indicate that MPANF has the potential to outperform the naive baseline when reliable movement predictions are available.