The role of categorical EPU indices in predicting stock-market returns

Chen, Juan; Ma, Feng; Qiu, Xuemei; Li, Tao

doi:10.1016/j.iref.2023.05.003

Cited by 5 publications

(2 citation statements)

References 68 publications

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“…Numerous researchers have shown a strong interest in improving predictive models by incorporating various input indicators. These include factors like oil prices (Sim and Zhou, 2015), economic policy uncertainty (Chen et al ., 2023) and data from Google Trends (Saetia and Yokrattanasak, 2022). However, it's important to note that the use of bond-related predictors is relatively limited in the existing research.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Chinese stock prices using convertible bond: an evidence-based neural network approach

Maneejuk,

Zou,

Yamaka

2023

AJEB

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PurposeThe primary objective of this study is to investigate whether the inclusion of convertible bond prices as important inputs into artificial neural networks can lead to improved accuracy in predicting Chinese stock prices. This novel approach aims to uncover the latent potential inherent in convertible bond dynamics, ultimately resulting in enhanced precision when forecasting stock prices.Design/methodology/approachThe authors employed two machine learning models, namely the backpropagation neural network (BPNN) model and the extreme learning machine neural networks (ELMNN) model, on empirical Chinese financial time series data.FindingsThe results showed that the convertible bond price had a strong predictive power for low-market-value stocks but not for high-market-value stocks. The BPNN algorithm performed better than the ELMNN algorithm in predicting stock prices using the convertible bond price as an input indicator for low-market-value stocks. In contrast, ELMNN showed a significant decrease in prediction accuracy when the convertible bond price was added.Originality/valueThis study represents the initial endeavor to integrate convertible bond data into both the BPNN model and the ELMNN model for the purpose of predicting Chinese stock prices.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting Chinese stock prices using convertible bond: an evidence-based neural network approach

Maneejuk,

Zou,

Yamaka

2023

AJEB

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“…[17] found the negative impact of an increase in EPU of US markets on other economies such as China. [18] studied the impact of EPU on the stock market returns during the US-China Trade war and Covid-19 period. [19] found that the trade policy uncertainty (categorical component of EPU) could be used to predict the US stock returns during the recession period.…”

Section: Trade-related Proxiesmentioning

confidence: 99%

Mean and volatility spillover in Asian economies: Evidence from trade war

Shafique,

Bhutta

2023

PLoS ONE

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This study aims to assess the mean and volatility spillover due to trade war between US and China on the Asian markets using GARCH, evidencing that portfolio opportunity exists for the investors in these markets. These markets may offer diversification benefits to investors who fear the negative ramifications of stock markets of the economies in US and China. The study creates a composite variable to test the impact of trade war. The composition of the variable is based on Bilateral Tariffs, Trade policy and Economic policy uncertainty of US only. It means the study covers the US side only for creating a trade war variable. The findings of the study reveal no mean or volatility spillover exists. The study has implications for investors and policymakers.

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Combination forecasts of China's oil futures returns based on multiple uncertainties and their connectedness with oil

Shi,

Wei,

et al. 2023

Energy Economics

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The role of categorical EPU indices in predicting stock-market returns

Cited by 5 publications

References 68 publications

Predicting Chinese stock prices using convertible bond: an evidence-based neural network approach

Predicting Chinese stock prices using convertible bond: an evidence-based neural network approach

Mean and volatility spillover in Asian economies: Evidence from trade war

Combination forecasts of China's oil futures returns based on multiple uncertainties and their connectedness with oil

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