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This paper aims to analyze the connectedness between Gulf Cooperation Council (GCC) stock market index and cryptocurrencies. It investigates the relevant impact of RavenPack COVID sentiment on the dynamic of stock market indices and conventional cryptocurrencies as well as their Islamic counterparts during the onset of the COVID-19 crisis.

The authors rely on the methodology of Diebold and Yilmaz (2012, 2014) to construct network-associated measures. Then, the wavelet coherence model was applied to explore co-movements between GCC stock markets, cryptocurrencies and RavenPack COVID sentiment. As a robustness check, the authors used the time-frequency connectedness developed by Barunik and Krehlik (2018) to verify the direction and scale connectedness among these markets.

The results illustrate the effect of COVID-19 on all cryptocurrency markets. The time variations of stock returns display stylized fact tails and volatility clustering for all return series. This stressful period increased investor pessimism and fears and generated negative emotions. The findings also highlight a high spillover of shocks between RavenPack COVID sentiment, Islamic and conventional stock return indices and cryptocurrencies. In addition, we find that RavenPack COVID sentiment is the main net transmitter of shocks for all conventional market indices and that most Islamic indices and cryptocurrencies are net receivers.

This study provides two main types of implications: On the one hand, it helps fund managers adjust the risk exposure of their portfolio by including stocks that significantly respond to COVID-19 sentiment and those that do not. On the other hand, the volatility mechanism and investor sentiment can be interesting for investors as it allows them to consider the dynamics of each market and thus optimize the asset portfolio allocation.

This finding suggests that the RavenPack COVID sentiment is a net transmitter of shocks. It is considered a prominent channel of shock spillovers during the health crisis, which confirms the behavioral contagion. This study also identifies the contribution of particular interest to fund managers and investors. In fact, it helps them design their portfolio strategy accordingly.

This paper examines the impact of political instability on the investors' behavior, measured by Google search queries, and on the dynamics of stock market returns.

First, by using the DCC-GARCH model, the authors examine the effect of investor sentiment on the Tunisian stock market return. Second, the authors employ the fully modified dynamic ordinary least square method (FMOL) to estimate the long-term relationship between investor sentiment and Tunisian stock market return. Finally, the authors use the wavelet coherence model to test the co-movement between investor sentiment measured by Google Trends and Tunisian stock market return.

Using the dynamic conditional correlation (DCC), the authors find that Google search queries index has the ability to reflect political events especially the Tunisian revolution. In addition, empirical results of fully modified ordinary least square (FMOLS) method reveal that Google search queries index has a slightly higher effect on Tunindex return after the Tunisian revolution than before this revolution. Furthermore, by employing wavelet coherence model, the authors find strong comovement between Google search queries index and return index during the period of the Tunisian revolution political instability. Moreover, in the frequency domain, strong coherence can be found in less than four months and in 16–32 months during the Tunisian revolution which show that the Google search queries measure was leading over Tunindex return. In fact, wavelet coherence analysis confirms the result of DCC that Google search queries index has the ability to detect the behavior of Tunisian investors especially during the period of political instability.

This study provides empirical evidence to portfolio managers that may use Google search queries index as a robust measure of investor's sentiment to select a suitable investment and to make an optimal investments decisions.

The important research question of how political instability affects stock market dynamics has been neglected by scholars. This paper attempts principally to fill this void by investigating the time-varying interactions between market returns, volatility and Google search based index, especially during Tunisian revolution.

The purpose of this paper is to evaluate the performance of Microfinance Institutions (MFIs) offering FinTech services. This study contributes to the existing literature on microfinance digitalization, financial inclusion and sustainable development. The study also takes into consideration a behavioral perspective through the efficiency evaluation process of MFIs offering FinTech services.

The following study employs the Stochastic Frontier Analysis approach to estimate the operational and social efficiency scores of the 387 MFIs over the period 2005–2019. Then, it tries to consider factors influencing MFIs' efficiency and assess their effects. Hence, two separate models for operation and social efficiency introducing a set of factors, including FinTech proxies and overconfidence proxies, are tested. The first model for operational efficiency uses a random-effects estimator while the second one for social efficiency uses a fixed-effects estimator.

The results show that innovative MFIs have weaker averages of operational efficiency than non-innovative ones but higher averages of social efficiency. This was justified by the fact that innovative MFIs are more socially oriented. Further, findings of this study depict that the proxies of FinTech affect negatively the level of operational efficiency of MFIs. They also depict a negative relationship between FinTech proxies and the level of social efficiency. These results hold through robustness tests.

The highlight of this study is that it takes heed of the indirect effect of technological innovation on the efficiency of MFIs. It has been proved that it moderates the impact of managerial overconfidence (manifested by excessive risk-taking, viz., high levels of PAR30, LGR and NIM) on the level of both operational and social efficiencies.

本文旨在對提供金融科技服務的微型金融機構的表現作出評價。我們的研究, 就現有之學術文獻而言, 在以下課題之探討上作出了貢獻: 微型金融的數字化、普惠金融、以及可持續發展。本研究亦以行為主義觀點, 對微型金融機構提供之金融科技服務的效率作出評價。

本研究使用隨機邊界分析法的理念, 去估計有關的387間微型金融機構於2005年至2019年期間、經營方面和社會方面的效率分數; 繼而嘗試找出影響微型金融機構效率的因素, 並評估這些因素的影響。為此目的, 研究人員分別測試兩個模型, 一個是探究運作方面的效率, 另一個則探究社會方面的效率。兩個模型內均放入一系列的因素, 其中包括金融科技代理和過度自信代理。探究運作方面的效率的模型使用了隨機效果估算器, 而探究社會方面的效率的模型則使用了固定效果估算器。

研究結果顯示、具創新精神的微型金融機構, 在運作方面的效率的平均值上,較沒具創新精神的為弱, 而社會方面的效率的平均值卻較高。這個結果是合理的, 因為具創新精神的微型金融機構會更著眼於社會。另外, 研究結果描繪了一個現象, 就是: 金融科技代理會對微型金融機構的運作效率水平產生負面影響; 我們也看到、金融科技代理與社會方面的效率水平之間的關聯是負面的; 這些研究結果、均通過穩健性檢驗。

本研究最突出之處為研究人員關注科技之創新會間接影響微型金融機構的效率。研究人員證明了於微型金融機構整合金融科技服務是會緩和管理上的過度自信給運作和社會兩方面的效率水平帶來的影響 (管理上的過度自信、顯露於過度的風險承擔, 即是, PAR30(貸款組合風險-30日)、LGR(貸款增長率) 和NIM(淨息差) 處於高水平)。