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on Forecasting |
| By: | Cheng Zhang |
| Abstract: | Forecasting random walks is notoriously challenging, with na\"ive prediction serving as a difficult-to-surpass baseline. To investigate the potential of using movement predictions to improve point forecasts in this context, this study focuses on symmetric random walks, in which the target variable's future value is reformulated as a combination of its future movement and current value. The proposed forecasting method, termed the fusion of movement and na\"ive predictions (FMNP), is grounded in this reformulation. The simulation results show that FMNP achieves statistically significant improvements over na\"ive prediction, even when the movement prediction accuracy is only slightly above 0.50. In practice, movement predictions can be derived from the comovement between an exogenous variable and the target variable and then linearly combined with the na\"ive prediction to generate the final forecast. FMNP effectiveness was evaluated on four U.S. financial time series -- the close prices of Boeing (BA), Brent crude oil (OIL), Halliburton (HAL), and Schlumberger (SLB) -- using the open price of the Financial Times Stock Exchange (FTSE) index as the exogenous variable. In all the cases, FMNP outperformed the na\"ive prediction, demonstrating its efficacy in forecasting symmetric random walks and its potential applicability to other forecasting tasks. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.14469 |
| By: | Robert-Paul Berben; Rajni Rasiawan; Jasper de Winter |
| Abstract: | This paper examines the performance of machine learning models in forecasting Dutch inflation over the period 2010 to 2023, leveraging a large dataset and a range of machine learning techniques. The findings indicate that certain machine learning models outperform simple benchmarks, particularly in forecasting core inflation and services inflation. However, these models face challenges in consistently outperforming the primary inflation forecast of De Nederlandsche Bank for headline inflation, though they show promise in improving the forecast for non-energy industrial goods inflation. Models employing path averages rather than direct forecasting achieve greater accuracy, while the inclusion of non-linearities, factors, or targeted predictors provides minimal or no improvement in forecasting performance. Overall, Ridge regression has the best forecasting performance in our study. |
| Keywords: | Inflation forecasting; Big data; Machine learning; Random Forest; Ridge regression |
| JEL: | C22 C53 C55 E17 E31 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:dnb:dnbwpp:828 |
| By: | Адилханова Зарина // Adilkhanova Zarina (National Bank of Kazakhstan); Ержан Ислам // Yerzhan Islam (National Bank of Kazakhstan) |
| Abstract: | В условиях нестабильной макроэкономической среды повышение точности прогнозирования инфляции является приоритетной задачей для центральных банков, особенно тех, которые придерживаются режима инфляционного таргетирования. Традиционные эконометрические модели сталкиваются с ограничениями при учёте волатильности, внешних шоков и нелинейных взаимосвязей. Данное исследование направлено на улучшение прогнозирования инфляции путём интеграции методов машинного обучения в существующую систему селективно-комбинированного прогнозирования инфляции. Включение таких алгоритмов, как Ridge Regression, Lasso Regression и Elastic Net, позволяет выявлять сложные паттерны в макроэкономических данных и повышать точность прогнозов. Сравнительный анализ прогнозов, полученных с использованием традиционных эконометрических моделей (OLS, LTAR, BVAR, RW) и алгоритмов машинного обучения, показывает, что гибридный подход значительно снижает ошибки прогнозирования и повышает надёжность прогнозов в краткосрочном периоде. Полученные результаты могут внести вклад в совершенствование инструментов макроэкономического прогнозирования и развитие более эффективной денежно-кредитной политики, поддерживая качество принятия решений центральными банками. // In an environment of macroeconomic instability, improving the accuracy of inflation forecasting is a priority for central banks, especially those operating under inflation targeting regimes. Traditional econometric models face limitations in accounting for volatility, external shocks, and nonlinear relationships. This study aims to enhance inflation forecasting by integrating machine learning methods into the existing Selective-Combined Inflation Forecasting System (SSCIF). The inclusion of algorithms such as Ridge Regression, Lasso Regression, and Elastic Net enables the identification of complex patterns in macroeconomic data, thereby improving forecast accuracy. A comparative analysis of forecasts generated using traditional econometric models (OLS, LTAR, BVAR, RW) and machine learning algorithms demonstrates that the hybrid approach significantly reduces forecasting errors and enhances the reliability of short-term forecasts. The results contribute to the advancement of macroeconomic forecasting tools and the development of more effective monetary policy, supporting better decision-making by central banks. |
| Keywords: | инфляция, прогнозирование, индекс потребительских цен, модель, машинное обучение, эконометрические модели, точность прогнозов, inflation, forecasting, consumer price index, model, machine learning, econometric models, forecast accuracy |
| JEL: | E31 E37 C52 C61 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:aob:wpaper:62 |
| By: | Konstantinos-Leonidas Bisdoulis |
| Abstract: | Fluctuations in the stock market rapidly shape the economic world and consumer markets, impacting millions of individuals. Hence, accurately forecasting it is essential for mitigating risks, including those associated with inactivity. Although research shows that hybrid models of Deep Learning (DL) and Machine Learning (ML) yield promising results, their computational requirements often exceed the capabilities of average personal computers, rendering them inaccessible to many. In order to address this challenge in this paper we optimize LightGBM (an efficient implementation of gradient-boosted decision trees (GBDT)) for maximum performance, while maintaining low computational requirements. We introduce novel feature engineering techniques including indicator-price slope ratios and differences of close and open prices divided by the corresponding 14-period Exponential Moving Average (EMA), designed to capture market dynamics and enhance predictive accuracy. Additionally, we test seven different feature and target variable transformation methods, including returns, logarithmic returns, EMA ratios and their standardized counterparts as well as EMA difference ratios, so as to identify the most effective ones weighing in both efficiency and accuracy. The results demonstrate Log Returns, Returns and EMA Difference Ratio constitute the best target variable transformation methods, with EMA ratios having a lower percentage of correct directional forecasts, and standardized versions of target variable transformations requiring significantly more training time. Moreover, the introduced features demonstrate high feature importance in predictive performance across all target variable transformation methods. This study highlights an accessible, computationally efficient approach to stock market forecasting using LightGBM, making advanced forecasting techniques more widely attainable. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.07580 |
| By: | Laura Coroneo |
| Abstract: | This paper discusses three key themes in forecasting for monetary policy highlighted in the Bernanke (2024) review: the challenges in economic forecasting, the conditional nature of central bank forecasts, and the importance of forecast evaluation. In addition, a formal evaluation of the Bank of England's inflation forecasts indicates that, despite the large forecast errors in recent years, they were still accurate relative to common benchmarks. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.07386 |
| By: | Andrea Bastianin (Department of Economics, Management, and Quantitative Methods, University of Milan and Fondazione Eni Enrico Mattei); Xiao Li (Department of Economics, Management, and Quantitative Methods, University of Milan; Fondazione Eni Enrico Mattei and University of Pavia); Luqman Shamsudin (Fondazione Eni Enrico Mattei and Department of Environmental Science and Policy, University of Milan) |
| Abstract: | The transition to a cleaner energy mix, essential for achieving net-zero greenhouse gas emissions by 2050, will significantly increase demand for metals critical to renewable energy technologies. Energy Transition Metals (ETMs), including copper, lithium, nickel, cobalt, and rare earth elements, are indispensable for renewable energy generation and the electrification of global economies. However, their markets are characterized by high price volatility due to supply concentration, low substitutability, and limited price elasticity. This paper provides a comprehensive analysis of the price volatility of ETMs, a subset of Critical Raw Materials (CRMs). Using a combination of exploratory data analysis, data reduction, and visualization methods, we identify key features for accurate point and density forecasts. We evaluate various volatility models, including Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Stochastic Volatility (SV) models, to determine their forecasting performance. Our findings reveal significant heterogeneity in ETM volatility patterns, which challenge standard groupings by data providers and geological classifications. The results contribute to the literature on CRM economics and commodity volatility, offering novel insights into the complex dynamics of ETM markets and the modeling of their returns and volatilities. |
| Keywords: | Critical Raw Materials, Energy Transition, Features, Volatility, Forecasting, Density forecasts |
| JEL: | C22 C53 C58 Q02 Q30 Q42 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:fem:femwpa:2025.04 |