News Text Embeddings and Clean Energy ETF Return Direction Prediction: An Empirical Study Based on BERT-PCA and LightGBM

Weiliang Liu

doi:10.47297/wspdecWSP2515-797314.20261001

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News Text Embeddings and Clean Energy ETF Return Direction Prediction: An Empirical Study Based on BERT-PCA and LightGBM

10卷第1期

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News Text Embeddings and Clean Energy ETF Return Direction Prediction: An Empirical Study Based on BERT-PCA and LightGBM
News Text Embeddings and Clean Energy ETF Return Direction Prediction: An Empirical Study Based on BERT-PCA and LightGBM
Universiti Putra Malaysia, Selangor, 47300, Malaysia
2026年10卷第1期页码：67-71
纸质出版：2026-01-30
DOI：10.47297/wspdecWSP2515-797314.20261001
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Weiliang Liu. (2026). News Text Embeddings and Clean Energy ETF Return Direction Prediction: An Empirical Study Based on BERT-PCA and LightGBM. 发展经济学, 10(1), 67-71.

Liu Weiliang.News Text Embeddings and Clean Energy ETF Return Direction Prediction: An Empirical Study Based on BERT-PCA and LightGBM[J].Development Economics of China,2026,10(01):67-71.
Weiliang Liu. (2026). News Text Embeddings and Clean Energy ETF Return Direction Prediction: An Empirical Study Based on BERT-PCA and LightGBM. 发展经济学, 10(1), 67-71. DOI： 10.47297/wspdecWSP2515-797314.20261001.

Liu Weiliang.News Text Embeddings and Clean Energy ETF Return Direction Prediction: An Empirical Study Based on BERT-PCA and LightGBM[J].Development Economics of China,2026,10(01):67-71. DOI： 10.47297/wspdecWSP2515-797314.20261001.

摘要

Abstract

This study investigates whether deep semantic information from financial news headlines provides incremental predictive value for the weekly price direction of the iShares Global Clean Energy ETF (ICLN). Using 189 weeks of data from June 2020 to March 2024

we construct a predictive framework combining BERT embeddings

PCA dimensionality reduction

and a LightGBM classifier

evaluated under a rigorous rolling window validation protocol. Empirical results reveal that although textual factors dominate feature importance—contributing 92.2% of total gain—the full model achieves an out-of-sample AUC of only 0.5359 and a recall of just 11% for upward weeks

failing to generate effective trading signals. These findings indicate that

under the current sample and model specifications

news headline semantics do not provide stable predictive power for ICLN's weekly direction. Methodologically

this paper contributes a transparent pipeline for transforming unstructured news into structured predictors and establishes a rigorous evaluation framework suitable for small-sample financial time series

offering a valuable reference for empirical research in financial natural language processing.

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references

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