arXiv 20 Jun 2024 · cs.CE
arXiv:2406.14469 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Moosa (2013) Why is it so difficult to outperform the random walk in exchange rate forecasting? | 0.511 | 2 | 1 | 100% |
| 2 | Rogers, A (1995) Population forecasting: Do simple models outperform complex models? | 0.405 | 1 | 1 | 100% |
| 3 | Baker, M., Wurgler, J (2007) Investor sentiment in the stock market | 0.405 | 1 | 1 | 100% |
| 4 | Barberis, N., Shleifer, A., Vishny, R (1998) A model of investor sentiment | 0.405 | 1 | 1 | 100% |
| 5 | Beck, N., Dovern, J., Vogl, S (2025) Mind the naive forecast! a rigorous evaluation of forecasting models for time series with low predictability | 0.405 | 1 | 1 | 100% |
| 6 | Beraha, M., Metelli, A.M., Papini, M., Tirinzoni, A., Restelli, M (2019) Feature selection via mutual information: New theoretical insights | 0.405 | 1 | 1 | 100% |
| 7 | Bustos, O., Pomares-Quimbaya, A (2020) Stock market movement forecast: A systematic review | 0.405 | 1 | 1 | 100% |
| 8 | Cover, T.M., Thomas, J.A (2012) Elements of Information Theory | 0.405 | 1 | 1 | 100% |
| 9 | Crutchfield, J.P., Feldman, D.P (2003) Regularities unseen, randomness observed: Levels of entropy convergence | 0.405 | 1 | 1 | 100% |
| 10 | De Bondt, W.F., Thaler, R (1985) Does the stock market overreact? | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 40 scored citations.