A sentiment-enhanced hybrid model for crude oil price forecasting

Fang, Yan; Wang, Wenyan; Wu, Peng; Zhao, Yunfan

doi:10.1016/j.eswa.2022.119329

Cited by 28 publications

(6 citation statements)

References 77 publications

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“…Extracting text sentiment for auxiliary prediction has been applied in the field of price prediction, e.g., see Refs. [ 32 , 35 , 36 ]. In addition, quantifying text readability is another effective means of processing text information.…”

Section: Literature Reviewmentioning

confidence: 99%

Do multisource data matter for NGP prediction? Evidence from the G-LSTM model

Hao,

Shang,

Yuan

et al. 2024

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

Do multisource data matter for NGP prediction? Evidence from the G-LSTM model

Hao,

Shang,

Yuan

et al. 2024

Heliyon

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“…Recent years have witnessed strong growth in adopting machine learning for time series forecasting, driven by its remarkable ability to uncover intri-cate and non-linear patterns within the data. Researchers have employed various Ml networks such as long-short-term memory (LSTM) [3,12,16], gated recurrent unite (GRU) [17,18,19], convolutional neural network (CNN) [20], and transformer models [21] to predict crude oil prices based on historical price data and some relevant features. Machine learning approaches often require extensive feature engineering and can be sensitive to the quality and availability of data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many researchers incorporated additional data sources, such as macroeconomic and technical indicators, social media sentiment, and news articles, to enhance prediction accuracy. Additionally, advancements in natural language processing and sentiment analysis techniques have allowed for a more comprehensive understanding of the impact of geopolitical events and news on oil prices [3,19,22,23]. A popular approach combines different neural networks with statistical and economic methods to improve crude oil forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Multi-Step Brent Oil Price Forecasting with Ensemble Multi-Scenario Bi-GRU Networks

Alruqimi,

Persio

2024

Preprint

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Despite numerous research efforts in applying deep learning to time series forecasting, achieving high accuracy in multi-step predictions for volatile time series like crude oil prices remains a significant challenge. Moreover, most existing approaches primarily focus on one-step forecasting, and the performance often varies depending on the dataset and specific case study. In this paper, we introduce an ensemble model to capture Brent oil price volatility and enhance the multi-step prediction. Our methodology employs a two-pronged approach. First, we assess popular deep-learning models and the impact of various external factors on forecasting accuracy. Then, we introduce an ensemble multi-step forecasting model for Brent oil prices. Our approach generates accurate forecasts by employing ensemble techniques across multiple forecasting scenarios using three BI-GRU networks. Extensive experiments were conducted on a dataset encompassing the COVID-19 pandemic period, which had a significant impact on energy markets. The proposed model's performance was evaluated using the standard evaluation metrics of MAE, MSE, and RMSE. The results demonstrate that the proposed model outperforms benchmark and established models.

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“…Closer to the domain of our article, Fang et al [50] showcase a hybrid model for crude oil price forecasting that integrates FinBERT, variational mode decomposition (VMD), attention mechanisms, and a BiGRU DL model. VMD is a modern signal decomposition technique that was recently used by Huang and Deng [51] for modeling crude oil prices.…”

Section: Foundation Models For Financial Sentiment Analysismentioning

confidence: 99%

Integrating Economic Theory, Domain Knowledge, and Social Knowledge into Hybrid Sentiment Models for Predicting Crude Oil Markets

2023

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For several decades, sentiment analysis has been considered a key indicator for assessing market mood and predicting future price changes. Accurately predicting commodity markets requires an understanding of fundamental market dynamics such as the interplay between supply and demand, which are not considered in standard affective models. This paper introduces two domain-specific affective models, CrudeBERT and CrudeBERT+, that adapt sentiment analysis to the crude oil market by incorporating economic theory with common knowledge of the mentioned entities and social knowledge extracted from Google Trends. To evaluate the predictive capabilities of these models, comprehensive experiments were conducted using dynamic time warping to identify the model that best approximates WTI crude oil futures price movements. The evaluation included news headlines and crude oil prices between January 2012 and April 2021. The results show that CrudeBERT+ outperformed RavenPack, BERT, FinBERT, and early CrudeBERT models during the 9-year evaluation period and within most of the individual years that were analyzed. The success of the introduced domain-specific affective models demonstrates the potential of integrating economic theory with sentiment analysis and external knowledge sources to improve the predictive power of financial sentiment analysis models. The experiments also confirm that CrudeBERT+ has the potential to provide valuable insights for decision-making in the crude oil market.

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A sentiment-enhanced hybrid model for crude oil price forecasting

Cited by 28 publications

References 77 publications

Do multisource data matter for NGP prediction? Evidence from the G-LSTM model

Do multisource data matter for NGP prediction? Evidence from the G-LSTM model

Enhancing Multi-Step Brent Oil Price Forecasting with Ensemble Multi-Scenario Bi-GRU Networks

Integrating Economic Theory, Domain Knowledge, and Social Knowledge into Hybrid Sentiment Models for Predicting Crude Oil Markets

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