Similar Search Results

Stock market return predictability : Google pessimistic sentiments versus fear gauge

Habibah, Ume; Rajput, Suresh Kumar Oad; Sadhwani, Ranjeeta - In: Cogent economics & finance 5 (2017) 1, pp. 1-15

This study aims at comparing Google Search Volume Indices (GSVIs—including market crash and bear market) and VIX (Investor Fear Gauge Index) in terms of explaining the S&P 500 returns. The VIX is found a more robust predictor of stock market returns than Google indices, and it does granger...

Persistent link: https://www.econbiz.de/10011886968

Does Investor Sentiment Predict the Near-term Returns of the Chinese Stock Market?

Cheema, Muhammad A. - 2018

Recent evidence on the relationship between investor sentiment and subsequent monthly market returns in China shows that investor sentiment is a reliable momentum predictor since an increase (decrease) in investor sentiment leads to higher (lower) future returns. However, we suggest that...

Persistent link: https://www.econbiz.de/10012931914

Distillation of news flow into analysis of stock reactions

Zhang, Junni L.; Härdle, Wolfgang; Chen, Cathy Y.; … - 2015

News carry information of market moves. The gargantuan plethora of opinions, facts and tweets on financial business offers the opportunity to test and analyze the influence of such text sources on future directions of stocks. It also creates though the necessity to distill via statistical...

Persistent link: https://www.econbiz.de/10010471736

The stock-bond correlation

Czasonis, Megan; Kritzman, Mark; Turkington, David - 2020 - This version: April 27, 2020

Investors rely on the stock-bond correlation for a variety of tasks, such as forming optimal portfolios, designing … hedging strategies, and assessing risk. Most investors estimate the stock-bond correlation simply by extrapolating the … historical correlation of monthly returns and assume that this correlation best characterizes the correlation of future, annual …

Persistent link: https://www.econbiz.de/10012225162

Measuring China's stock market sentiment

Li, Jia; Chen, Yun; Shen, Yan; Wang, Jingyi; Huang, Zhuo - 2019

This paper develops textual sentiment measures for China's stock market by extracting the textual tone of 60 million messages posted on a major online investor forum in China from 2008 to 2018. We conduct sentiment extraction by using both conventional dictionary methods based on customized word...

Persistent link: https://www.econbiz.de/10012125620

Investor Sentiment : Predicting the Overvalued Stock Market

Cheema, Muhammad A. - 2020

The research literature shows that investor sentiment is a contrarian predictor of aggregate stock market returns. However, we contend that investor sentiment only predicts aggregate stock market returns during high-sentiment states where overpricing is more prevalent than underpricing. Using a...

Persistent link: https://www.econbiz.de/10012852587

A data-driven deep learning approach incorporating investor sentiment and government interventions to predict post-crash stock return in China's A-share market

Lin, Weiran; Yu, Haijing; Wang, Liugen - In: Journal of innovation & knowledge : JIK 10 (2025) 3, pp. 1-12

Global financial markets frequently experience extreme volatility, which poses significant challenges in forecasting stock returns, particularly following market crashes. Traditional models often falter under these conditions due to heightened investor sentiment and strong regulatory...

Persistent link: https://www.econbiz.de/10015413148

Tan, Selin Duz - In: Borsa Istanbul Review 22 (2022) 4, pp. 812-827

This study shows how correlated information consumption (CIC) of retail investors relates to comovement in stock market outcomes. We construct clusters of stocks with CIC by employing network analysis on Google co-search data. We predict significant comovement in returns and liquidity of stocks...

Persistent link: https://www.econbiz.de/10013334839

A latent dynamic factor approach to forecasting multivariate stock market volatility : conference paper

Gribisch, Bastian - 2013

This paper proposes a latent dynamic factor model for low- as well as high-dimensional realized covariance matrices of stock returns. The approach is based on the matrix logarithm and allows for flexible dynamic dependence patterns by combining common latent factors driven by HAR dynamics and...

Persistent link: https://www.econbiz.de/10010341025

On the economic determinants of optimal stock-bond portfolios : international evidence

Conrad, Christian; Stürmer, Karin - 2017 - This version: May 31, 2017

Using a modified DCC-MIDAS specification that allows the long-term correlation component to be a function of multiple … explanatory variables, we show that the stock-bond correlation in the US, the UK, Germany, France, and Italy is mainly driven by …

Persistent link: https://www.econbiz.de/10011745369