XXXIX CICLO | El Bayar Miriam

I am Mariam El Bayar, a PhD student within the framework of a cotutelle agreement between the university of Tuscia and the university of Sfax in Tunsia. I hold a research master degree in finance.

Research interests: Investigate the impact of investor sentiment measured by the Fear and Greed index on the portfolio management and the transmission of shocks between the Chinese, American and Euroopean financial markets, the commodities market and fintech during crisis .

– Measuring investor’s sentiment. -Investigate the impact of investor’s sentiment on risk transmission and portfolio management in the Chinese, American and European financial market?

– Investigate the impact of investor’s sentiment on risk transmission in the commodities market during crisis.

– Investigate the impact of investor’s sentiment on risk transmission on the fintech market during crisis. – Investigate the role of ESG factors on Fear mitigation and decision making during crisis Contribution and implications Our contribution to such an understanding consists in carrying out a detailed study of the connectedness between the investor’s sentiment and the returns of financial markets, fintech, green bonds and comodities, which includes all the phases of the great depression in China, USA and EU. Following this logic, we use the Bayesian TVC VAR model, which is a popular method used to measure the total interdependence or connectedness in a dynamic system of random variables. This study contributes to the existant literature in many ways. In fact, motivated by studies on behavioral finance, we frame the role of shocks to investor’s sentiment on financial markets, commodities and fintech markets. We first introduce a recent proxy for the measurement of the Chinese, American and European investor’s sentiment using the Fear and Greed Index. This study addresses a real psychological event such as the panic induced by the COVID-19 pandemic and the russian-ukrainian war, with an investor’s sentiment index that captures the anxiety embedded in the financial markets. Second, we investigate the directionality and dynamic connectedness among the different asset classes and the investor’s sentiment, which provides an alternative way to check the contagion effect. Then, we examine the impact of the pandemic and the russian-ukrainian war on the spillover connectedness among the investor’s sentiment, the stock and bond markets, the commodities market, the fintech market and the green bonds. Finally, we study the role of the ESG factors in fear mitigation. In fact, identifying the channel that plays a leading role in the risk transmission by detecting the mechanisms of risk propagation through each market during the coronavirus pandemic is essential for an effective risk management and portfolio diversification. Also, the network connectivity can help investors predict trends and promote their reasonable asset allocation using the investor’s sentiment and the market response to shocks. Hence, our methods help us to characterize the timing and evolution of the key aspects of crisis. To the best of our knowledge, this is the first study to explore this.

Behavioural study of connectedness and portfolio optimization among fintech, green bonds and commodities during crises. 

Introduction:

 Objectives Investigate the impact of investor sentiment measured by the Fear and Greed index on the portfolio management and the transmission of shocks between the Chinese, American and Euroopean financial markets, the commodities market and fintech during crisis . – Measuring investor’s sentiment. -Investigate the impact of investor’s sentiment on risk transmission and portfolio management in the Chinese, American and European financial market? – Investigate the impact of investor’s sentiment on risk transmission in the commodities market during crisis. – Investigate the impact of investor’s sentiment on risk transmission on the fintech market during crisis. – Investigate the role of ESG factors on Fear mitigation and decision making during crisis Contribution and implications Our contribution to such an understanding consists in carrying out a detailed study of the connectedness between the investor’s sentiment and the returns of financial markets, fintech, green bonds and comodities, which includes all the phases of the great depression in China, USA and EU. Following this logic, we use the Bayesian TVC VAR model, which is a popular method used to measure the total interdependence or connectedness in a dynamic system of random variables. This study contributes to the existant literature in many ways. In fact, motivated by studies on behavioral finance, we frame the role of shocks to investor’s sentiment on financial markets, commodities and fintech markets. We first introduce a recent proxy for the measurement of the Chinese, American and European investor’s sentiment using the Fear and Greed Index. This study addresses a real psychological event such as the panic induced by the COVID-19 pandemic and the russian-ukrainian war, with an investor’s sentiment index that captures the anxiety embedded in the financial markets. Second, we investigate the directionality and dynamic connectedness among the different asset classes and the investor’s sentiment, which provides an alternative way to check the contagion effect. Then, we examine the impact of the pandemic and the russian-ukrainin war on the spillover connectedness among the investor’s sentiment, the stock and bond markets, the commodities market, the fintech market and the green bonds. Finally, we study the role of the ESG factors in fear mitigation. In fact, identifying the channel that plays a leading role in the risk transmission by detecting the mechanisms of risk propagation through each market during the coronavirus pandemic is essential for an effective risk management and portfolio diversification. Also, the network connectivity can help investors predict trends and promote their reasonable asset allocation using the investor’s sentiment and the market response to shocks. Hence, our methods help us to characterize the timing and evolution of the key aspects of crisis. To the best of our knowledge, this is the first study to explore this. 

Literature

The research on investor attention attracts great attention, mainly because of the assumption that traditional asset pricing models don’t quite match the reality (Da et al, 2011; Afkhami et al, 2017). Fortunately, Da et al. (2011) use the Google search volume index (GSVI) to propose a direct measurement of investor attention on stock, as they point out that investors are paying attention to it when investors search for a stock in Google. At present, information has become a new factor in influencing oil prices, and the Internet is an essential tie between oil prices and information (Guo and Ji, 2013). In reality, investors also tend to use Google search engine to collect information on the oil prices because it is the most popular search engine, which reflects investors’ concern about oil prices (Yao et al., 2017). Ding and Hou (2015) provide evidence that the stock liquidity can be improved by the retail investor attention. Dimpfl and Jank (2016) use GSVI to reveal that investor attention has high contemporaneous linkage with the stock volatility and can provide prediction information for the stock volatility. Kim et al. (2019) select the Norway stock market as the research object and find that GSVI can forecast trading volume and volatility rather than returns. Xu et al. (2019a, 2019b) provide evidence that the source of stock volatility also includes the event impact measured by the GSVI in addition to macroeconomic fundamentals. J. Xiao and Y. Wang Energy Economics 97 (2021) 105180 2 Pham and Huynh (2020) first investigate the effect of investor attention measured by the GSVI on the green bond returns and volatility, and confirm their relationship is time-varying.

Why fear?

Fear represents a distinctive human emotion, uniquely recognizable within ourselves (LeDoux 2014). While we can articulate the specifics of our individual fear experiences, understanding the true feelings of another person proves challenging. This emotion is acknowledged as a conscious state comprising both associative and nonassociative components, arising from exposure to real or imagined threats (Costanzi, Marco, et al. 2011). Its prevalence is 12 significant, with a substantial portion of the global population enduring constant or recurring fear, often accompanied by physical and psychological distress a reality underscored by daily news and stark experiences. Anxiety and stress share connections with fear. While fear often induces anxiety and contributes to stress, it is distinct from both. In the formation of fear memory, the central elements are a perceived threat and the subsequent response to it. This association is not always applicable or necessary in the contexts of anxiety and stress. The survival value of fear is profound, rendering it the most extensively studied form of learning and memory. Malmendier and Nagel (2010) investigated the repercussions of the Great Depression on the investment behavior of individuals who lived through it, revealing a diminished willingness to take financial risks compared to those who did not experience that period. Choi et al. (2009) and Vissing-Jorgensen (2003) corroborate Malmendier and Nagel’s findings, emphasizing the substantial impact of memory on the inclination to undertake financial risks. Thaler and Johnson (1990) further support Malmendier and Nagel’s perspective, discussing the concept of being “snakebit,” wherein individuals who have suffered financial setbacks display reduced willingness to embrace risk in subsequent ventures. Even studies with a highly quantitative focus, such as Mandelbrot (1963), have identified long-term memory effects in market participants. In the last three years, fear was the sentiment the most generated in the financial markets by the crisis that happened such as the covid-19 pandemic and the Russian-Ukrainian war. The emergence of the coronavirus disease (COVID-19) in China in December 2019 and in Europe in February 2020 has led to a notable surge in fear and concerns about the virus, as indicated by national polls (Asmundson & Taylor, 2020a; McCarthy, 2020). A survey conducted in Belgium in early April 2020 with 44,000 participants revealed a substantial increase in the prevalence of anxiety (20%) and depressive disorders (16%) compared to 2018 (11% and 10%, respectively) (Sciensano, 2020). 

Methodological approach

The construction of the American Fear and greed index: The Fear and Greed Index is a tool that some investors use to evaluate the market. This assumes that excessive fear can cause transactions far below the intrinsic value of the stock, while unrestrained greed can cause bids well above the intrinsic value of the stock. The CNN fear and greed index examines seven different factors to establish how much fear and greed there is in the market. They are: Stock Price Strength – Stock Price Breadth -Junk Bond Demand – Market Volatility – Safe Haven Demand – Put and Call Options. 

The Bayesian TVC VAR model: we follow a more general Bayesian estimation procedure with time-varying volatility or asymmetric (non-conjugate) priorities. We follow the Cogley, T., & Sargent, T. J. (2002) method which is based on a simple VAR triangularization that allows to sample VAR coefficients by plotting them equation by equation. In their work, Cogley and Sargent proposed a Bayesian approach to estimating the TVC VAR model and performed empirical applications using macroeconomic data. They used Markov Chain Monte Carlo (MCMC) methods to estimate the model and obtain posterior coefficient distributions. Using Bayesian techniques, they were able to account for the uncertainty of the parameters and provide measures of the uncertainty in the estimation results. Time-varying VAR coefficients (TVC-VAR) are a generalization of VAR models where the coefficients can change over time.

Conclusion 

In this study, we focused on the investor sentiments by developing and employing a new approach in the form of a comprehensive Fear and Greed Sentiment Index and a feverish sentiment index which include a wide range of factors. Using Bayesian TVC-VAR to examine the sentiment spillover across the China, USA and EU, we explore the network 27 structure of emissions and the reception of fear shocks in underlying countries. Furthermore, we investigate the effectiveness ESG factors in fear mitigation during the COVID-19 pandemic. Finally, to validate the Fear and Greed index, we construct the Feverish index for each stock market.

Mezghani, T., Boujelbène, M., & Elbayar, M. (2021). Impact of COVID‐19 pandemic on risk transmission between googling investor’s sentiment, the Chinese stock and bond markets. China Finance Review International, 11(3), 322-348.
Publication link:Impact of COVID‐19 pandemic on risk transmission between googling investor’s sentiment, the Chinese stock and bond markets | Emerald Insight 

ISSN: 2044-1398