Journal of Forecasting最新文献

Sparse ensemble forecasting has become an increasingly competitive technique for forecasting research and practice in recent years. This paper examines the role of sparse ensemble in unemployment rates forecasting using expert forecasters. First, we show how the effectiveness of sparse ensembles is influenced by the complexity and accuracy of the base models. Second, we extend sparse regularization techniques to settings with unknown bias and variance employing Monte Carlo simulations. Third, we highlight the critical role of the regularization coefficient $�\lambda$, which serves as a key shrinkage factor and necessitates a balance between model sparsity and forecasting accuracy. Experimental results on unemployment rate data demonstrate the superiority of sparse ensemble learning over equal-weight strategies. This framework provides novel insights into predictive modeling within the fields of economics and labor markets.

A deep learning binary classifier is proposed to test if asset returns follow martingale difference sequences. The Neyman–Pearson classification paradigm is applied to control the type I error of the test. In Monte Carlo simulations, I find that this approach has better power properties than variance ratio and portmanteau tests against several alternative processes. I apply this procedure to a large set of exchange rate returns and find that it detects several potential deviations from the martingale difference hypothesis that the conventional statistical tests fail to capture.

In China's financial and economic system, the agricultural futures market plays an important role in guiding the market to self regulate and providing efficient information transmission for regulators. The effective prediction of futures prices can assist in guiding agricultural production, monitoring operational risks arising from significant price fluctuations, and enhancing the predictability and pertinence of the country's macroeconomic regulation policies. This study investigates the main variety of grain futures—soybean futures, taking into account complex market and non-market influencing factors. Using historical market data and related news headlines of soybean futures as source data and integrating topic identification and sentiment analysis techniques, a novel framework for predicting agricultural futures prices that integrates topic sentiment is constructed. This model uses BERTopic to extract topic information from agricultural news texts, then integrates FinBERT to construct topic-based sentiment features, fuses them with structured market features, and constructs LSTM price prediction model with multi-feature inputs. In order to better model the short-term features and state transfer patterns of the time series, hidden Markov model (HMM) is further used to extract the hidden states, which are deeply fused with the LSTM model. The empirical results show that the model fusing topic and sentiment features significantly improves the forecasting accuracy in all lags, LSTM works best in short-term forecasting, and the combination of HMM and LSTM exhibits significant performance advantages in medium- and long-term forecasting. Compared with the baseline model that relies only on market features, topic sentiment features provide important incremental information for price forecasting, and the contribution of each topic sentiment feature calculated based on the PI metric is close to 50%. In addition, deep learning–based prediction model performs better than baseline machine learning models in dealing with extreme external shocks such as climate disasters, the COVID-19 pandemic, and the Russia–Ukraine conflict.

We examine the role of the technology in predicting carbon prices using a large set of machine learning models. The predictors are selected from technological, environmental, financial, energy, and geopolitical aspects. Our sample covers the daily period from August 1, 2014, to March 4, 2024. We find that technology factors (Information Technology Index, AEX Technology Index, and Tech All Share Index) significantly improve the prediction accuracy of carbon prices, both when included in the prediction model individually and simultaneously. Furthermore, the Diebold–Mariano and Clark–West tests highly reject the null of equal predictive accuracy between the technology model and the baseline model (without technology variables). Moreover, results show that XGBoost outperforms the alternative machine learning models for all forecasting horizons (1, 5, 22, and 250 days). We present significant policy implications useful for investors, companies, and policymakers.

This article proposed a novel hybrid framework, the WTC-DCA-Informer, for forecasting volatility in the Australian stock market. The findings indicated that (1) through a comprehensive comparison with various machine learning and deep learning models, the proposed WTC-DCA-Informer framework significantly outperformed traditional methods in terms of predictive performance. (2) Across different training set proportions, the WTC-DCA-Informer model demonstrated exceptional forecasting capabilities, achieving a coefficient of determination (R²) as high as 0.9216 and a mean absolute percentage error (MAPE) as low as 13.6947%. (3) The model exhibited strong adaptability and robustness in responding to significant market fluctuations and structural changes before and after the outbreak of COVID-19. This study offers a new perspective and tool for forecasting financial market volatility, with substantial theoretical and practical implications for enhancing the efficiency and stability of financial markets.

This paper proposes a novel hybrid nonlinear interval decomposition ensemble (NIDE) framework to improve forecasting accuracy of interval-valued gas prices. The framework first decomposes the price series using bivariate empirical mode decomposition and interval multiscale permutation entropy to capture dynamics driven by long-term trends, events, and short-term fluctuations. Tailored models are then employed for each component, including a threshold autoregressive interval model, interval event study methodology, and interval random forest. Finally, an ensemble prediction integrates the component forecasts. Empirical results show that the NIDE approach significantly outperforms benchmarks in out-of-sample forecasting of interval-valued natural gas prices. For instance, the RMSE improvements range from 10.3% to 38.8% compared to benchmark models. Additionally, the NIDE approach not only enhances accuracy but also provides economic interpretation by identifying drivers like speculative trading and public interest proxied by online trends.

Interval-valued data are a special symbolic data, which contains rich information. The prediction of interval-valued data is a challenging task. In terms of predicting interval-valued data, machine learning algorithms typically consider mean regression, which is sensitive to outliers and may lead to unreliable results. As an important complement to mean regression, in this paper, a quantile regression artificial neural network based on a center and radius method (QRANN-CR) is proposed to address this problem. Numerical studies have been conducted to evaluate the proposed method, comparing with several traditional models, including the interval-valued quantile regression, the center method, the MinMax method, and the bivariate center and radius method. The simulation results demonstrate that the proposed QRANN-CR model is an effective tool for predicting interval-valued data with higher accuracy and is more robust than the other methods. A real data analysis is provided to illustrate the application of QRANN-CR.