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Based on the theoretical framework, this paper analyzes the sentiment-herding relationship in emerging stock markets (ESMs). First, it aims to examine the effect of investor sentiment on herding. Second, it seeks the direction of causality between sentiment and herding time series.

The present study applies the Exponential Generalized Auto_Regressive Conditional Heteroskedasticity (EGARCH) model to capture the volatility clustering of herding on the financial market and to investigate the role of the investor sentiment on herding behaviour. Then the vector autoregression (VAR) estimation uses the Granger causality test to determine the direction of causality between the investor sentiment and herding. This study uses a sample consisting of stocks listed on the Shanghai Composite index (SSE) (348 stocks), the Jakarta composite index (JKSE) (118 stocks), the Mexico IPC index (14 stocks), the Russian Trading System index (RTS) (12 stocks), the Warsaw stock exchange General index (WGI) (106 stocks) and the FTSE/JSE Africa all-share index (76 stocks). The sample includes 5,020 daily observations from February 1, 2002, to March 31, 2021.

The research findings show that the sentiment has a significant negative impact on the herding behaviour pointing out that the higher the investor sentiment, the lower the herding. However, the results of the present study indicate that a higher investor sentiment conducts a higher herding behaviour during market downturns. Then the outcomes suggest that during the crisis period, the direction is one-way, from the investor sentiment to the herding behaviour.

The findings may have implications for universal policies of financial regulators in EMs. We have found evidence that the Emerging investor sentiment contributes to the investor herding behaviour. Therefore, the irrational investor herding behaviour can increase the stock market volatility, and in extreme cases, it may lead to bubbles and crashes. Market regulators could implement mechanisms that can supervise the investor sentiment and predict the investor herding behaviour, so they make policies helping stabilise stock markets.

The originality of this paper lies in investigate the sentiment-herding relationship during the Surprime crisis and the Covid-19 epidemic in the EMs.

This study aims to the framework of the development of a new sand concrete, essentially manufactured with river/dune sand and recycled plastic aggregates (PAs; 0/3.15 mm). This new concrete may have a great interest, as it can enable us to achieve the best economical, technical and ecological solutions for local construction problems. Given the high abundance of dune sand (DS) and the large quantities of plastic waste, plastic–mineral sand concrete can be a good alternative to the ordinary building materials available on the local market.

A replacement of sand by PAs is made by volume substitution. The plastic percentages laid down are 0%, 25%, 50% and 100%. Indeed, after a general experimental characterization of the studied composites, the investigation mainly concentrated on the study of the effect of the addition of plastic particles on the accelerated carbonation of river sand (RS) concrete and DS concrete, separately.

The density of the composites and consequently their compressive strength are slightly reduced; but their thermal insulation is significantly improved. Their structure seems to be homogeneous, the plastic grains are well distributed in the matrix and the adhesion “plastic–matrix” is good. At small plastic contents, the RS concrete is slightly better. As regards the carbonation results, the PAs significantly contribute to the improvement of the resistance of the composite against carbonation effect. It can be observed that increasing the proportion of plastic particles in sand concrete considerably decreases the thickness of the carbonated concrete.

The studies led on the behavior of plastic concrete, particularly in arid zones, are very limited. Moreover, for sand concrete, there are no similar studies. Therefore, the characterization of such materials is necessary. In addition of thermo-mechanical characterization, this work aims at studying the durability of the material, especially its resistance to carbonation. On the other hand, this work has a significant positive impact on both environment and economy, since it focuses on the recycling of industrial waste, and the valorization of DS, which is available in great quantities in south of Algeria.

The purpose of this research is to assess the removal of oil spills from the seawater surface as well as the antibacterial activity of ZnFe₂O₄-cetyltrimethylammonium bromide (CTAB, cationic surfactant) magnetic nanoparticles (ZFO-CTAB MNPs).

A CTAB-assisted sol–gel method was used to synthesize ZFO-CTAB MNPs. X-ray powder diffraction and Fourier transform infrared spectroscopy were used for ZFO-CTAB MNPs characterization. Also, the magnetic force and apparent density of ZFO-CTAB MNPs were determined. The oil spill cleanup was investigated by using the gravimetric oil removal (GOR) technique, which used ZFO-CTAB MNPs as oil absorbent material and four oil samples (crude, diesel, gasoline and used oil) as oil spill models. The antibacterial activity of ZFO-CTAB MNPs against Gram-negative bacteria (Pseudomonas aeruginosa, Escherichia coli and Salmonella typhi) was investigated by using the optical density method.

The results revealed that, when the amount of ZFO-CTAB was 0.01 g, gasoline oil had the highest GOR (51.80 ± 0.88 g/g) and crude oil had the lowest (11.29 ± 0.82 g/g). Furthermore, for Escherichia coli, Salmonella typhi and Pseudomonas aeruginosa, ZFO-CTAB MNPs inhibited bacterial growth with a higher percentage (94.24%–95.63%).

The applications of ZFO-CTAB MNPs in the cleanup of oil spills from aqueous solutions, as well as their antibacterial activity. The results showed that ZFO-CTAB MNPs are a promising material for removing oil spills from bodies of water as well as an antibacterial agent against Gram-negative bacterial strains.

The great value of offline identification of machine parameters is when the machine manufacturer does not provide its parameters. Most machine control strategies require parameter values, and some circumstances in the industrial sector only require offline identification. This paper aims to present a new offline method for estimating induction motor parameters based on least squares and a salp swarm algorithm (SSA).

The central concept is to use the classic least squares (LS) method to acquire the majority of induction machine (IM) constant parameters, followed by the SSA method to obtain all parameters and minimize errors.

The obtained results showed that the LS method gives good results in simulation based on the assumption that the measurements are noise-free. However, unlike in simulations, the LS method is unable to accurately identify the machine’s parameters during the experimental test. On the contrary, the SSA method proves higher efficiency and more precision for IM parameter estimation in both simulations and experimental tests.

After performing a primary identification using the technique of least squares, the initial intention of this study was to apply the SSA for the purpose of identifying all of the machine’s parameters and minimizing errors. These two approaches use the same measurement from a simple running test of an IM, and they offer a quick processing time. Therefore, this combined offline strategy provides a reliable model based on the identified parameters.

This study aims to examine whether and how gender diversity on corporate boards is associated with voluntary nonfinancial disclosures, particularly water disclosures.

This study uses corporate water information disclosure data from Chinese listed firms between 2010 and 2018 to conduct regression analyses to examine the association between female directors and water information disclosure.

Empirical results show that female directors have a significantly positive association with corporate water information disclosure. Additionally, internal industry water sensitivity of firms moderates this significant relationship.

This study determined that female directors can promote not only water disclosure but also positive corporate water performance, reflecting the consistency of words and deeds of female directors in voluntary nonfinancial disclosures.

This study examines the importance of income income-shifting arrangements of US multinational corporations (MNCs) on future stock price crash risk.

This study employs a sample of 7,641 corporation-year observations over the 2005–2017 period and uses ordinary least squares regression analysis.

The authors find that the income-shifting arrangements of MNCs are positively and significantly associated with stock price crash risk after controlling for corporate tax avoidance and other known determinants of stock price crash risk in the regression model. This result is robust to alternative measures of stock price crash risk and income-shifting, and several endogeneity tests. The authors also observe that income-shifting arrangements increase stock price crash risk both directly and indirectly through the information opacity channel. Finally, in cross-sectional analyses, the authors find that the positive association between income-shifting and stock price crash risk is more pronounced for MNCs that use tax haven subsidiaries and have weak corporate governance mechanisms.

The authors provide new empirical evidence that MNCs will likely face significant capital market consequences regarding their income-shifting arrangements.

Reducing aircraft fuel consumption has become a paramount research area, focusing on optimizing operational parameters like speed and altitude during the cruise phase. However, the existing methods for fuel reduction often rely on complex experimental calculations and data extraction from embedded systems, making practical implementation challenging. To address this, this study aims to devise a simple and accessible approach using available information.

In this paper, a novel analytic method to estimate and optimize fuel consumption for aircraft equipped with jet engines is proposed, with a particular emphasis on speed and altitude parameters. The dynamic variations in weight caused by fuel consumption during flight are also accounted for. The derived fuel consumption equation was rigorously validated by applying it to the Boeing 737–700 and comparing the results against the fuel consumption reference tables provided in the Boeing manual. Remarkably, the equation yielded closely aligned outcomes across various altitudes studied. In the second part of this paper, a pioneering approach is introduced by leveraging the particle swarm optimization algorithm (PSO). This novel application of PSO allows us to explore the equation’s potential in finding the optimal altitude and speed for an actual flight from Algiers to Brussels.

The results demonstrate that using the main findings of this study, including the innovative equation and the application of PSO, significantly simplifies and expedites the process of determining the ideal parameters, showcasing the practical applicability of the approach.

The suggested methodology stands out for its simplicity and practicality, particularly when compared to alternative approaches, owing to the ready availability of data for utilization. Nevertheless, its applicability is limited in scenarios where zero wind effects are a prevailing factor.

The research opens up new possibilities for fuel-efficient aviation, with a particular focus on the development of a unique fuel consumption equation and the pioneering use of the PSO algorithm for optimizing flight parameters. This study’s accessible approach can pave the way for more environmentally conscious and economical flight operations.

Behavioral finance and market microstructure studies suggest that the investor sentiment and liquidity are related. This paper aims to examine the aggregate sentiment–liquidity relationship in emerging markets (EMs) for both the sample period and crisis period. Then, it verifies this relationship, using the asymmetric sentiment.

This study uses a sample consisting of stocks listed on the SSE Shanghai composite index (348 stocks), the JKSE (118 stocks), the IPC (14 stocks), the RTS (12 stocks), the WSE (106 stocks) and FTSE/JSE Africa (76 stocks). This is for the period ranging from February, 2002 until March, 2021 (230 monthly observations). We use the panel data and apply generalized method-of-moments (GMM) of dynamic panel estimators.

The empirical analysis shows the following results: first, it demonstrates a significant relationship between the aggregate investor sentiment and the stock market liquidity for the sample period and crisis one. Second, referring to the asymmetric sentiment, we have empirically given proof that the market is significantly more liquid in times of the optimistic sentiment than it is in times of the pessimistic sentiment. Third, using panel causality tests, we document a unidirectional causality between the investor sentiment and liquidity in a direct manner through the noise traders and the irrational market makers and also a bidirectional causality in an indirect channel.

The results reported in this paper have implications for regulators and investors in EMs. Firstly, the study informs the regulators that the increases and decreases in the stock market liquidity are related to the investor sentiment, not financial shocks. We empirically evince that the traded value is higher in the crisis. Secondly, we inform insider traders and rational market makers that the persistence of increases in the trading activity in both quiet and turbulent times is associated with investor participants such as noise traders and irrational market makers.

The originality of this work lies in employing the asymmetric sentiment (optimistic/pessimistic) in order to denote the sentiment–liquidity relationship in EMs for the sample period and the 2007–2008 subprime crisis.

This paper aims to examine the influence of the International Financial Reporting Standards (IFRS) adoption on corporate tax avoidance (CTA). In addition, this study aims to explore whether family ownership moderates the impact of IFRS adoption on CTA.

The authors used a sample of 1,856 firms from various countries around the world, covering the period between 2010 and 2022. To estimate the proposed econometric models, the authors applied both fixed and random effects regression methods.

The present findings show that IFRS adoption has a negative impact on CTA, as measured by the effective tax rate and book-tax differences. This negative impact is more pronounced in “common law” countries than in “civil law countries.” Additionally, the authors found that family ownership plays a moderating role by positively affecting the impact of IFRS adoption on CTA.

The findings have practical, regulatory and academic implications for fostering accountability and fairness in taxation. This study suggests that implementing IFRS reduces tax avoidance and emphasizes the need for firms to evaluate the implications of IFRS adoption on their tax-planning strategies. It highlights the importance of aligning financial reporting practices with international standards to enhance transparency and minimize tax avoidance opportunities. The differential impact of IFRS adoption between “common law” and “civil law” countries underscores the role of legal and regulatory frameworks. In addition, family ownership plays a significant role in shaping tax-planning strategies. From an academic perspective, this research provides a foundation for further exploration into the relationship between IFRS adoption and tax avoidance.

The existing literature has predominantly concentrated on examining the effect of IFRS adoption on CTA, and the empirical findings have been inconsistent. This study introduces a novel perspective by considering the moderating influence of family ownership in determining the impact of IFRS adoption on CTA.

This study aims to explore the possibility of using magnetic biochar composite (MBCC) derived from Heglig tree bark (HTB) powder (agricultural solid waste) and cobalt ferrite (CoFe₂O₄, CFO) for oil spill removal from seawater surface.

One-pot co-precipitation route was used to synthesize MBCC. The prepared materials were characterized by X-ray diffraction, scanning electron microscopy-energy dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy. The densities of the prepared materials were also estimated. Crude, diesel engine and gasoline engine oils were used as seawater pollutant models. The gravimetric oil removal (GOR) method was used for removing oil spills from seawater using MBCC as a sorbent material.

The obtained results revealed that the prepared materials (CFO and MBCC) were able to remove the crude oil and its derivatives from the seawater surface. Besides, when the absorbent amount was 0.01 g, the highest GOR values for crude oil (31.96 ± 1.02 g/g) and diesel engine oil (14.83 ± 0.83 g/g) were obtained using MBCC as an absorbent. For gasoline engine oil, the highest GOR (27.84 ± 0.46 g/g) was attained when CFO was used as an absorbent.

Oil spill removal using MBCC derived from cobalt ferrite and HTB. Using tree bark as biomass (eco-friendly, readily available and low-cost) for magnetic biochar preparation also is a promising method for minimizing agricultural solid wastes (e.g. HTB) and obtaining value-added-products.