Learning and Excess Stock Volatility

2014 ◽  
Author(s):  
Adem Atmaz
Keyword(s):  
Stock Volatility

Investor Herding Behavior and Stock Volatility

2020 ◽  
Vol 13 (2) ◽  
pp. 59-81
Author(s):  
Wonse Kim ◽  
J.B. Chay ◽  
Youngjoo Lee
Keyword(s):  
Herding Behavior ◽  
Stock Volatility

scholarly journals Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1794
Author(s):  
Eduardo Ramos-Pérez ◽  
Pablo J. Alonso-González ◽  
José Javier Núñez-Velázquez
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Short Term Memory ◽  
Risk Measures ◽  
Hybrid Models ◽  
Stock Volatility ◽  
Management Actions ◽  
Equity Risk ◽  
Hedging Strategies ◽  
Volatility Models

Events such as the Financial Crisis of 2007–2008 or the COVID-19 pandemic caused significant losses to banks and insurance entities. They also demonstrated the importance of using accurate equity risk models and having a risk management function able to implement effective hedging strategies. Stock volatility forecasts play a key role in the estimation of equity risk and, thus, in the management actions carried out by financial institutions. Therefore, this paper has the aim of proposing more accurate stock volatility models based on novel machine and deep learning techniques. This paper introduces a neural network-based architecture, called Multi-Transformer. Multi-Transformer is a variant of Transformer models, which have already been successfully applied in the field of natural language processing. Indeed, this paper also adapts traditional Transformer layers in order to be used in volatility forecasting models. The empirical results obtained in this paper suggest that the hybrid models based on Multi-Transformer and Transformer layers are more accurate and, hence, they lead to more appropriate risk measures than other autoregressive algorithms or hybrid models based on feed forward layers or long short term memory cells.

The Interplay Between Sentiment and MAX: Evidence from an Emerging Market

2021 ◽  
pp. 097265272096951
Author(s):  
Nilesh Gupta ◽  
Joshy Jacob
Keyword(s):  
Emerging Market ◽  
Jel Classification ◽  
Stock Volatility ◽  
The Poor

Investors with lottery preferences are known to concentrate on stocks with rare but extreme past returns. We investigate the extent to which lottery preference, measured by the MAX variable, varies with the market-wide irrational sentiment. We find that the high-MAX stocks have higher overpricing in a high-sentiment market and earn a lower alpha, compared to the low-sentiment market. Accordingly, the poor returns earned by a long-short portfolio of stocks with extreme MAX values are primarily due to the overvaluation of the high MAX-portfolio during the high sentiment phase. The higher stock volatility in India also magnifies the lottery preference of investors. JEL Classification: G4, G12, G41, G11

scholarly journals Dividend taxes and stock volatility

2017 ◽  
Vol 25 (2) ◽  
pp. 377-403
Author(s):  
Erin E. Syron Ferris
Keyword(s):  
Stock Volatility ◽  
Dividend Taxes

An empirical study on big data stock volatility forecasting algorithm based on multivariate hybrid criterion fuzzy model

2021 ◽  
pp. 002072092098399
Author(s):  
Zhang Xiao-Wen ◽  
Zeng Min
Keyword(s):  
Big Data ◽  
Stock Market ◽  
Fuzzy Model ◽  
Reference Value ◽  
Developed Countries ◽  
Theoretical Research ◽  
Prediction Algorithm ◽  
Real Economy ◽  
Stock Volatility ◽  
The Us

The fluctuation of the stock market has always been a matter of great concern to investors. People always hope to judge the trend of the stock market through the trend of the K line, so as to obtain the price difference through trading, Therefore, it is a theoretical research concerned by the academic circles to carry out empirical research through big data stock volatility prediction algorithm, so as to establish a model to predict the trend of the stock market. After decades of development, China's stock market has gradually matured in continuous exploration. However, compared with the stock market in developed countries, there are still imperfections. For example, the market value of China's stock market does not improve well with economic growth. Year-on-year growth and the development of the real economy. By studying the historical data from 2002 to 2017, we use the Multivariate Mixed Criterion Fuzzy Model (MMCFM) to predict the price changes in the stock market, and obtain the market in China through error statistical analysis. (SSE) is more unstable than the US stock market. Therefore, Multivariate Mixing Criterion (MMC) can be used as a reference indicator to visually measure market maturity. In this paper, we establish a multivariate mixed criteria fuzzy model, and use big data to predict the stock volatility. The algorithm verifies the reliability and accuracy of the model, which has a good reference value for investors.

Board characteristics and integrated reporting quality: evidence from ESG European companies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Salim Chouaibi ◽  
Yamina Chouaibi ◽  
Ghazi Zouari
Keyword(s):  
Risk Estimation ◽  
Reporting Quality ◽  
Annual Reports ◽  
Integrated Reporting ◽  
Stock Volatility ◽  
Content Type ◽  
Board Characteristics ◽  
Thomson Reuters ◽  
Related Quality ◽  
Practical Implications

PurposeThe aim of this study is to analyze the possible relationship between board characteristics and integrated reporting quality in an international setting.Design/methodology/approachTo test the study's hypotheses, the authors applied linear regressions with a panel data, and the authors collected data from the Thomson Reuters database (ASSET4) and from the annual reports from European companies to analyze data of 253 listed companies selected from the environmental, social and governance (ESG) index between 2010 and 2019.FindingsThe reached empirical results prove to indicate well that both of the board size, independence and diversity appear to have a significantly positive effect on the integrated reporting quality. Noteworthy, also, is the fact that the appointment of an independent nonexecutive chairman is positively associated with the integrated reporting related quality, and holds for firms with a nonindependent chairman.Practical implicationsBeyond the theoretical implications, our study also has several practical implications. These findings are particularly relevant for managers, shareholders, and policymakers. Thus, stakeholders should consider the accuracy of disclosure in determining the optimal reporting strategy (reducing risk estimation, returns' stock volatility, increasing long-term shareholder value and reputation of the firm).Originality/valueThis article is motivated by the low number of works in the context about the corporate social responsibility and sustainability issues. It makes an important contribution to the academic literature by adding to the limited body of research on integrated reporting and corporate governance in an ESG company setting. The study is also important for practitioners seeking to improve the quality of their integrated reports.

Momentum and the Cross-Section of Stock Volatility

2021 ◽  
Author(s):  
Minyou Fan ◽  
Fearghal Kearney ◽  
Youwei Li ◽  
Jiadong Liu
Keyword(s):  
Cross Section ◽  
Stock Volatility ◽  
The Cross
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