Yield curve trading strategies exploiting sentiment data

IF 3.8 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance Pub Date : 2024-07-26 DOI:10.1016/j.najef.2024.102226

引用次数: 0

Abstract

This paper builds upon previous research findings that show macro sentiment data-augmented models are better at predicting the yield curve. We extend the dynamic Nelson–Siegel model with macro sentiment data from either Twitter or RavenPack. Vector autogressive (VAR) models and Markov-switching VAR models are used to predict changes in the shape of the yield curve. We build bond butterfly trading strategies that exploit our yield curve shape change predictions. We find that the economic returns from our trading strategies based upon models exploiting macro sentiment data do not statistically significantly differ from those which do not rely on it.

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利用情绪数据的收益率曲线交易策略

本文以之前的研究成果为基础，这些研究成果表明宏观情绪数据增强模型能更好地预测收益率曲线。我们利用 Twitter 或 RavenPack 中的宏观情绪数据对动态 Nelson-Siegel 模型进行了扩展。向量自回归 (VAR) 模型和马尔可夫切换 VAR 模型用于预测收益率曲线形状的变化。我们利用收益率曲线形状变化预测建立了债券蝶式交易策略。我们发现，基于宏观情绪数据模型的交易策略与不依赖宏观情绪数据的交易策略的经济回报在统计上没有显著差异。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

North American Journal of Economics and Finance Multiple-

CiteScore

7.30

自引率

8.30%

发文量

168

期刊介绍： The focus of the North-American Journal of Economics and Finance is on the economics of integration of goods, services, financial markets, at both regional and global levels with the role of economic policy in that process playing an important role. Both theoretical and empirical papers are welcome. Empirical and policy-related papers that rely on data and the experiences of countries outside North America are also welcome. Papers should offer concrete lessons about the ongoing process of globalization, or policy implications about how governments, domestic or international institutions, can improve the coordination of their activities. Empirical analysis should be capable of replication. Authors of accepted papers will be encouraged to supply data and computer programs.