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Article
Publication date: 18 July 2011

Jamel E. Henchiri

Company disclosures on the web are a useful tool to promote the efficiency of financial markets. Moreover, they can be a source of strategic financial communication. The objective…

563

Abstract

Purpose

Company disclosures on the web are a useful tool to promote the efficiency of financial markets. Moreover, they can be a source of strategic financial communication. The objective of the study reported in this paper is to make an inventory of the information published on the web sites of companies listed in the Moroccan and Tunisian stock exchanges, and to compare the practices of those companies with those of their European counterparts. The study also seeks to identify the determinants of these disclosures.

Design/methodology/approach

The study develops a composite scale to measure the quality of web site disclosures. This scale is used to score the web sites of the top 91 companies listed on the Casablanca and Tunis stock exchanges in 2007. The quality of those web sites is compared with the quality of some web sites of European companies. A number of hypotheses relating to the determinants of web site quality are then tested using linear modeling techniques.

Findings

Two thirds of the firms listed in the Casablanca and Tunis stock exchanges have a web site (www.casablanca‐bourse.com). An average of 39.7 percent of Moroccan web sites and 19.4 percent of those from Tunisia meet the benchmark quality criteria used by this study, compared with between 48 percent and 61 percent for European firms. The average extended score is 32.80 percent; Moroccan firms score 38.34 percent on average, while Tunisian firms score 28.12 percent. The determinants of this information level are found to be accounting performance and the proportion of shares held by foreigners. Web site quality is also linked to firm size. Apart from those characteristics, no effect of the economic sector, the country or market performance could be detected.

Originality/value

The study presents an international comparison (north/south) and builds a novel scale in order to explain web disclosures. This is an area that has not previously been explored, and includes some financial markets that are under‐researched.

Details

EuroMed Journal of Business, vol. 6 no. 2
Type: Research Article
ISSN: 1450-2194

Keywords

Available. Content available
Article
Publication date: 26 April 2013

81

Abstract

Details

EuroMed Journal of Business, vol. 8 no. 1
Type: Research Article
ISSN: 1450-2194

Available. Content available
Article
Publication date: 18 July 2011

Evangelos Tsoukatos and Yiannis Dimotikalis

343

Abstract

Details

EuroMed Journal of Business, vol. 6 no. 2
Type: Research Article
ISSN: 1450-2194

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Article
Publication date: 16 July 2024

Sirine Ben Yaala and Jamel Eddine Henchiri

This study aims to predict stock market crises in the Middle East North Africa (MENA) regions by leveraging the nonlinear autoregressive neural network with exogenous inputs…

64

Abstract

Purpose

This study aims to predict stock market crises in the Middle East North Africa (MENA) regions by leveraging the nonlinear autoregressive neural network with exogenous inputs (NARX) model with two measures of investor sentiment: the ARMS indicator and Google Trends' search volume of positive and negative words.

Design/methodology/approach

Employing a novel approach, this study utilizes the NARX model with ten neurons in the hidden layer and the Levenberg–Marquardt training algorithm. It evaluates model performance through learning, validation and test errors, as well as correlation analysis between predicted and actual crises.

Findings

The NARX model, incorporating investor sentiment, has proven to be a reliable tool for forecasting crises, helping market participants understand data complexity and avoid crisis consequences. The divergence in how investors interpret market news, with some focusing solely on negative developments and others valuing positive outcomes, highlights the predictive nature of the optimistic and pessimistic sentiments captured by the model.

Research limitations/implications

This study advocates for integrating behavioral approaches into stock market crisis prediction, highlighting the significance of investor sentiment and deep learning. It advances crisis mechanism understanding and opens avenues in behavioral finance. Integration of these findings into finance and economics education could enhance students' risk understanding and mitigation strategies.

Practical implications

The adoption of NARX models, incorporating investor sentiment, empowers market participants to proactively manage crises, adjust strategies, enhance asset protection and make informed decisions. These models enable them to minimize losses, maximize returns and diversify portfolios effectively in response to market fluctuations. These insights also guide policymakers such as governments, regulatory institutions and financial organizations in formulating crisis prevention and mitigation policies, bolstering economic and financial stability.

Social implications

This research reduces economic uncertainty, safeguards individuals' savings and investments and promotes a stable financial climate.

Originality/value

This study is one of the first attempts to demonstrate the detection and prediction of stock market crises, specifically in the MENA stock market, using the NARX model. It offers a robust forecasting model using machine learning and investor sentiment, providing decision-making support for investment strategies and policy development aimed at enhancing financial and economic stability.

Details

Journal of Financial Regulation and Compliance, vol. 32 no. 5
Type: Research Article
ISSN: 1358-1988

Keywords

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Article
Publication date: 21 May 2024

Manel Mahjoubi and Jamel Eddine Henchiri

This paper aims to investigate the effect of the economic policy uncertainty (EPU), geopolitical risk (GPR) and climate policy uncertainty (CPU) of USA on Bitcoin volatility from…

162

Abstract

Purpose

This paper aims to investigate the effect of the economic policy uncertainty (EPU), geopolitical risk (GPR) and climate policy uncertainty (CPU) of USA on Bitcoin volatility from August 2010 to August 2022.

Design/methodology/approach

In this paper, the authors have adopted the empirical strategy of Yen and Cheng (2021), who modified volatility model of Wang and Yen (2019), and the authors use an OLS regression with Newey-West error term.

Findings

The results using OLS regression with Newey–West error term suggest that the cryptocurrency market could have hedge or safe-haven properties against EPU and geopolitical uncertainty. While the authors find that the CPU has a negative impact on the volatility of the bitcoin market. Hence, the authors expect climate and environmental changes, as well as indiscriminate energy consumption, to play a more important role in increasing Bitcoin price volatility, in the future.

Originality/value

This study has two implications. First, to the best of the authors’ knowledge, the study is the first to extend the discussion on the effect of dimensions of uncertainty on the volatility of Bitcoin. Second, in contrast to previous studies, this study can be considered as the first to examine the role of climate change in predicting the volatility of bitcoin. This paper contributes to the literature on volatility forecasting of cryptocurrency in two ways. First, the authors discuss volatility forecasting of Bitcoin using the effects of three dimensions of uncertainty of USA (EPU, GPR and CPU). Second, based on the empirical results, the authors show that cryptocurrency can be a good hedging tool against EPU and GPR risk. But the cryptocurrency cannot be a hedging tool against CPU risk, especially with the high risks and climatic changes that threaten the environment.

Details

Journal of Financial Economic Policy, vol. 16 no. 4
Type: Research Article
ISSN: 1757-6385

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Article
Publication date: 23 November 2023

Sirine Ben Yaala and Jamel Eddine Henchiri

This study aims to predict stock market crashes identified by the CMAX approach (current index level relative to historical maximum) during periods of global and local events…

72

Abstract

Purpose

This study aims to predict stock market crashes identified by the CMAX approach (current index level relative to historical maximum) during periods of global and local events, namely the subprime crisis of 2008, the political and social instability of 2011 and the COVID-19 pandemic.

Design/methodology/approach

Over the period 2004–2020, a log-periodic power law model (LPPL) has been employed which describes the price dynamics preceding the beginning dates of the crisis. In order to adjust the LPPL model, the Global Search algorithm was developed using the “fmincon” function.

Findings

By minimizing the sum of square errors between the observed logarithmic indices and the LPPL predicted values, the authors find that the estimated parameters satisfy all the constraints imposed in the literature. Moreover, the adjustment line of the LPPL models to the logarithms of the indices closely corresponds to the observed trend of the logarithms of the indices, which was overall bullish before the crashes. The most predicted dates correspond to the start dates of the stock market crashes identified by the CMAX approach. Therefore, the forecasted stock market crashes are the results of the bursting of speculative bubbles and, consequently, of the price deviation from their fundamental values.

Practical implications

The adoption of the LPPL model might be very beneficial for financial market participants in reducing their financial crash risk exposure and managing their equity portfolio risk.

Originality/value

This study differs from previous research in several ways. First of all, to the best of the authors' knowledge, the authors' paper is among the first to show stock market crises detection and prediction, specifically in African countries, since they generate recessionary economic and social dynamics on a large extent and on multiple regional and global scales. Second, in this manuscript, the authors employ the LPPL model, which can expect the most probable day of the beginning of the crash by analyzing excessive stock price volatility.

Details

African Journal of Economic and Management Studies, vol. 15 no. 3
Type: Research Article
ISSN: 2040-0705

Keywords

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