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This study aims to explore the impact of systematic liquidity risk on the averaged cross-sectional equity return of the Indian equity market. It also examines the effects of illiquidity and decomposed illiquidity on the conditional volatility of the equity market.

The present study employs the Liquidity Adjusted Capital Asset Pricing Model (LCAPM) for pricing systematic liquidity risk using the Fama & MacBeth cross-sectional regression model in the Indian stock market from January 1, 2012, to March 31, 2021. Further, the study employed an exponential generalized autoregressive conditional heteroscedastic (1,1) model to observe the impact of decomposed illiquidity on the equity market’s conditional volatility. The study also uses the Ordinary Least Square (OLS) model to illuminate the return-volatility-liquidity relationship.

The study’s findings indicate that the commonality between individual security liquidity and aggregate liquidity is positive, and the covariance of individual security liquidity and the market return negatively affects the expected return. The study’s outcome specifies that illiquidity time series analysis exhibits the asymmetric effect of directional change in return on illiquidity. Further, the study indicates a significant impact of illiquidity and decomposed illiquidity on conditional volatility. This suggests an asymmetric effect of illiquidity shocks on conditional volatility in the Indian stock market.

This study is one of the few studies that used the World Uncertainty Index (WUI) to measure liquidity and market risks as specified in the LCAPM. Further, the findings of the reverse impact of illiquidity and decomposed higher and lower illiquidity on conditional volatility confirm the presence of price informativeness and its immediate effects on illiquidity in the Indian stock market. The study strengthens earlier studies and offers new insights into stock market liquidity to clarify the association between liquidity and stock return for effective policy and strategy formulation that can benefit investors.

This study examines the impact of China’s mixed-ownership reform on the innovation of non-state-owned acquirers, with a particular focus on the impact on firms with high financing constraints, low-quality accounting information or less tangible assets.

We use a proprietary dataset of firms listed on the Shanghai and Shenzhen Stock Exchanges to investigate the impact of mixed ownership reform on non-state-owned enterprise (non-SOE) innovation. We employ regression analysis to examine the association between mixed ownership reform and firm innovation.

The study finds that non-state-owned firms can improve innovation by acquiring equity in state-owned enterprises (SOEs) under the reform. Eased financing constraints, lowered financing costs, better access to tax incentives or government subsidies, lowered agency costs, better accounting information quality and more credit loans are underlying the impact. Additionally, cross-ownership connections amongst non-SOE executives and government intervention strengthen the impact, whilst regional marketisation weakens it.

This study adds to the literature on the association between mixed ownership reform and firm innovation by focussing on the conditions under which this impact is stronger. It also sheds light on the policy implications for SOE reforms in emerging economies.

This paper aims to elucidate the relationship between entrepreneurial orientation, reverse innovation and international performance of emerging economy multinational enterprises (EMNEs).

The authors analyze archival data of Chinese limited companies between 2010 and 2016, including 11,230 firm-year observations about 1708 firms. In order to test the study’s mediation hypotheses, the authors apply an ordinary least square (OLS) regression.

The authors find evidence that the entrepreneurial orientation of EMNEs has a positive effect on reverse innovations. Furthermore, the authors find positive effects of reverse innovation on the international performance of EMNEs. This pattern of results suggests that the relationship between entrepreneurial orientation and international performance is partially mediated by reverse innovation.

The study’s findings help managers in EMNEs to promote reverse innovation by building and using their entrepreneurial orientation. It also helps them to set out and gauge the chances of success of their internationalization strategies. The findings also hold relevance for firms in developed economies as well, as they may understand which emerging economy competitors stand to threaten their positions.

The strategic role of reverse innovations – i.e. clean slate, super value and technologically advanced products originating from emerging markets – has generated considerable research attention. It is clear that reverse innovations impact the international performance of EMNEs. Yet how entrepreneurial orientation influences international performance is still underexplored. Thus, the current study clarifies the mechanism by examining and testing the mediating role of reverse innovation among the entrepreneurial orientation–international performance link.

This study examines, in relation to agency theory, the influence of corporate mechanism on the environmental reporting of banking businesses registered on the Dhaka Stock Exchange (DSE).

This study was carried out consuming an example of 150 annual reports from 30 banks for the period 2015–2019. Ordinary least squares (OLS) regression was used to investigate the inspiration of corporate governance on the range of inclusive environmental reporting.

The outcomes reveal that insider equity, board leadership structure, and presence of female directors are statistically significant, while board size and outside directors are insignificant. Furthermore, the results also indicate that the adoption of environmental disclosure among banking businesses in Bangladesh is extra motivated by an increase in the inside skills and moderately the outside acceptability weights. Additionally, there appears to be a supposed lack of stakeholder pressure for environmental disclosure.

The results show that the range of environmental reporting of banking businesses in Bangladesh is good, at an average of 53.90%. It concludes that corporate governance has a substantial inspiration on the range of environmental reporting of banking businesses in Bangladesh.

This study aims to investigate the impact of supply chain concentration (SCC) on corporate research and development (R&D) investment and determine the moderating roles of industry concentration and financing constraints on the relationship between SCC and R&D investment.

The study collected data from Chinese listed companies, used the fixed effects model to test the research hypotheses and further used the two-stage Heckman test and propensity score matching (PSM) to address potential endogeneity issues.

The result reveals a negative impact of SCC on corporate R&D investment. In addition, industry concentration mitigates the negative impact of SCC on corporate R&D investment, but financing constraints strengthen the negative impact.

This study introduces the concept of SCC and empirically tests its effect on R&D investment, further explaining the lack of corporate innovation. This study inspires companies to strengthen SC management and weigh the level of SCC with environmental factors.

The purpose of this study is to examine the relationship between the corporate social responsibility (CSR) performance of job-hopping executives at their former and subsequent firms.

We conduct regression analyses using a sample of firms listed on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2020 to examine whether CSR performance is similar from one firm to the next as executives switch jobs.

We find a positive relationship between the CSR performance of former and subsequent firms under job-hopping executives. This relationship is the strongest in the year of the job switch; it weakens in the second year and eventually disappears in the third year. In addition, we show that this relationship benefits different CSR stakeholder groups and is contingent on executive and subsequent firm attributes and job-hopping characteristics. Furthermore, we demonstrate that firms that hire a new chief executive officer from a firm with a strong track record in CSR, the new firm experiences a significant surge in CSR performance compared with firms that do not experience such a shock.

This study has implications for executive hiring decisions.

This study extends the understanding of CSR determinants through the lens of inter-organisational ties associated with job-hopping executives.

The current study aims to examine the impact of two black swan events on the performance of six stock markets in Gulf Cooperation Council (GCC) economies (Abu Dhabi, Bahrain, Dubai, Oman, Qatar and Saudi Arabia). The two selected black swan events are the US Mortgage and credit crisis (Global Financial Crisis of 2008) and the COVID-19 pandemic.

The performance of all the six stock markets are represented by their return and price volatility behavior, which has been measured by applying ARCH/GARCH model. The comparative analysis is done by employing mean difference models. The data is collected from Bloomberg on a daily frequency.

The response of two black swan events on the GCC stock markets has been heterogenous in nature. During the financial crisis, the impact was heavily felt on most of the stock markets in the GCC countries. It is revealed that the financial crisis had a negative significant impact on four of the six countries. Whereas during the COVID-19 crisis, it is revealed that there is no significant impact on four of the six selected stock markets. The positive significant impact is felt on two stock markets, namely, the Abu Dhabi stock market and the Saudi stock market.

The present investigation attempts to fill the gap in the literature on the intended topic because it is evident from the literature on the chosen subject that no study has been undertaken to evaluate and contrast the impact of the GFC crisis and COVID-19 on the GCC stock markets.

This research paper aims to explore asymmetric market efficiency of the 13 Euro countries, i.e. Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Netherland, Portugal, Slovakia, Slovenia and Spain, concerning the period before global financial crisis (GFC), after GFC and period of COVID-19 pandemic.

Multifractal detrended fluctuation analysis (MF-DFA) is applied to examine the persistence and anti-persistency. It also discusses the random walk behavior hypothesis of these 13 countries non-stationary time series. Additionally, generalized Hurst exponents are applied to estimate the relative efficiency between short- and long-run horizons and small and large fluctuations.

The current study results suggest that most countries' markets are multifractal and exhibit long-term persistence in the short and long run. Moreover, the results with respect to full sample confirm that Portugal is the most efficient country in short run and Austria is the least efficient country. However, in long run, Austria appeared to be highly efficient, and Slovakia is the least efficient. In the pre-GFC period, Greece is said to be the relatively most efficient market in the short run, whereas Austria is the most efficient market in the long run. In the case of Post-GFC, Netherland and Ireland are the most efficient markets in short and long run, respectively. Lastly, COVID-19 results indicate that Finland's stock market is the most efficient in short run. Whereas, in the long run, the high efficiency is illustrated by Germany. In contrast, the most affected stock market due to COVID-19 is Belgium.

This study will add value to the present knowledge on efficient market hypothesis (EMH) with the MF-DFA approach. Also, with the MF-DFA approach, potential investors will be capable of ranking the stock markets of Eurozone countries based on their efficiency in the period before and after GFC and then specifically in the period of COVID-19.

本研究旨在探討13個歐元區國家在環球金融危機前後, 以及2019新型冠狀病毒病肆虐時期之不對稱市場效率; 這13個國家包括: 奧地利、比利時、芬蘭、法國、德國、希臘、愛爾蘭、義大利、荷蘭、葡萄牙、斯洛伐克、斯洛維尼亞和西班牙。

研究人員使用多重分形去趨勢波動分析法、來探討持續性與反持續性。這分析法也用來討論正在研究中的13個國家的非平穩時間序列的隨機漫步假說; 而且, 廣義赫斯特指數被用來估算長期/短期投資與大/小波動之間的相對效率。

研究結果間接表明了大部份國家的市場都是多重分形的; 而且, 它們無論以短期抑或以長期來審視觀察, 均能展示持久性。再者, 就整體樣本而言, 研究結果確認了在短期來看, 葡萄牙是效率最高的國家, 而奧地利則效率最低。唯以長期來審視觀察, 奧地利則似乎效率很高, 而效率最低的則是斯洛伐克。在環球金融危機爆發前, 就短期而言, 希臘被認為是相對效率最高的市場, 而長期而言, 效率最高的則是奧地利。至於在環球金融危機爆發後, 就短期而言, 荷蘭是效率最高的市場, 而就長期而言, 效率最高的則是愛爾蘭。最後, 2019新型冠狀病毒病的結果顯示, 就短期而言, 荷蘭的股票市場是效率最高的, 而長期而言, 德國則展示了其高效率性。而受疫情影響最大的股票市場則是比利時。

研究採用了多重分形去趨勢波動分析法、來探討股票市場的效率, 並以此分析法來討論有關國家的非平穩時間序列的隨機漫步假說, 這使我們對效率市場假說有進一步的認識; 就此而言, 本研究為有關的探討增添價值; 而且, 有意投資者在使用多重分形去趨勢波動分析法下, 能夠基於歐元區國家的股票市場在環球金融危機前後, 以及更明確地在2019新型冠狀病毒病肆虐時期的效率, 來把這些股票市場分等級。

環球金融危機、2019新型冠狀病毒病、效率市場假說、多重分形去趨勢波動分析.

This study’s goal was to identify how several markets have developed over time and what determinants have influenced this process, based on adaptive markets hypothesis (AMH). In this regard, the authors consider that agents are driven by the seeking for abnormal returns to stay “alive” and their environment could somehow modify their decision-making processes, as well as influence the degree of efficiency of the market.

The authors collected the daily closing-of-the-market index from 50 countries, between 1990 and 2022. The sample includes emerging countries, developed countries and frontier markets. Then, the authors ran multilevel modeling using Hurst exponent as an informational efficiency metric estimated by two different moving windows: 500 and 1,250 observations (approximately 2 and 5 years).

The results indicate that the efficiency of the markets is not constant over time. The authors also have identified that markets follow a cyclical pattern of efficiency/inefficiency, and they are currently in a period of convergence to efficiency, possibly explained by the increase in computational capacity and speed of the available information to agents. In addition, this study identified that country characteristics are associated with market efficiency, considering institutional factors.

Studies of this nature contribute to the literature, considering the importance of better comprehension of market efficiency dynamics and their determinants, specially observing other theories on the relationship between information and markets (like AMH), which work with other investor assumptions than those used by efficient market hypothesis.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.