Search Results (112)

The purpose of this paper is to analyze how China’s outward foreign direct investment (OFDI), driven by different motivations, affects the bilateral global value chain (GVC) relationship between the home country (China) and host countries, evaluating both bilateral GVC trade value and relative GVC positions. Employing the OECD Trade in Value Added (TiVA) database combined with Chinese listed firm data, we found the following results: (1) Strategic asset-seeking OFDI strengthens the GVC relationship between China and host countries while enhancing China’s GVC position relative to host countries. (2) Efficiency-seeking OFDI increases the domestic value-added exported from host countries to China but does not improve China’s relative GVC position. (3) Natural resource-seeking OFDI enhances bilateral GVC trade volumes but has no significant impact on the relative GVC positions of China and host countries. (4) China’s OFDI, not driven by these motivations, generates a trade substitution effect between home and host countries. We also examined the heterogeneity of these effects. Our findings suggest that China’s OFDI fosters equitable and sustainable international cooperation, supports mutually beneficial GVC trade and host-country economic growth, and therefore, progresses toward Sustainable Development Goal (SDG) 8. Full article

This systematic literature review explores the application of artificial intelligence (AI) and machine learning (ML) in financial market forecasting, with a focus on four asset classes: equities, cryptocurrencies, commodities, and foreign exchange markets. Guided by the PRISMA methodology, the study identifies the most widely used predictive models, particularly LSTM, GRU, XGBoost, and hybrid deep learning architectures, as well as key evaluation metrics, such as RMSE and MAPE. The findings confirm that AI-based approaches, especially neural networks, outperform traditional statistical methods in capturing non-linear and high-dimensional dynamics. However, the analysis also reveals several critical research gaps. Most notably, current models are rarely embedded into real or simulated trading strategies, limiting their practical applicability. Furthermore, the sensitivity of widely used metrics like MAPE to volatility remains underexplored, particularly in highly unstable environments such as crypto markets. Temporal robustness is also a concern, as many studies fail to validate their models across different market regimes. While data covering one to ten years is most common, few studies assess performance stability over time. By highlighting these limitations, this review not only synthesizes the current state of the art but also outlines essential directions for future research. Specifically, it calls for greater emphasis on model interpretability, strategy-level evaluation, and volatility-aware validation frameworks, thereby contributing to the advancement of AI’s real-world utility in financial forecasting. Full article

There is a growing body of literature that focuses on the applicability of artificial intelligence (AI) in English as a Foreign Language (EFL) and English Language (EL) classrooms; however, educational application of AI in the EFL and EL classroom for gifted students presents a new paradigm. This paper explores the existing research to highlight current practices and future possibilities of AI for teaching EFL and EL to address gifted students’ special needs. In general, the uses of AI are being established for class instruction and intervention; nevertheless, there is still uncertainty about practitioner use of AI with gifted students in EFL and EL classrooms. This review identifies 42 examples of GenAI Models that can be used in gifted EFL and EL classrooms. In addition, the research conducted thus far has highlighted the positive contribution of the use of AI in EFL and EL environments, albeit some disadvantages and challenges have also been identified. The results also endorse the use of AI with gifted students as an asset and highlight the need for AI literacy for both teachers and gifted students in order to adapt to this new educational paradigm. In conclusion, more studies are needed, as many aspects regarding both teachers’ and gifted students’ use of AI remain to be elucidated to improve future applications of AI to teach EFL and EL to gifted students. Full article

The recent economic turmoil and the increasing volatility of bitcoins have necessitated the need for exploring safe-haven assets for bitcoins. In this quest, the present study aims to investigate the safe haven for bitcoins by examining the dynamic relationship between bitcoins, gold, foreign exchange, and stablecoins. This is achieved by calculating hedge ratios and portfolio weight ratios for various asset classes, by employing adaptive-based techniques such as generalized orthogonal generalized autoregressive conditional heteroscedasticity, corrected dynamic conditional correlation, corrected asymmetric dynamic conditional correlation, and asymmetric dynamic conditional correlation under various market and time-varying conditions. The empirical estimate reveals that all the selected asset classes are effective risk diversifiers for bitcoins. However, among all the asset classes, as per the hedge and portfolio weight ratio, Japanese yen, stablecoin for Japanese yen and Great Britain Pound, and Crypto Holding Frank Token (lowest-cost hedging strategies) are the most effective risk diversifiers when compared with bitcoins. Moreover, while considering external economic shocks, the empirical estimate posits that stablecoins are more stable risk diversifiers compared to the asset class they represent. Furthermore, in terms of the bivariate portfolio analysis formed with bitcoin, this study concludes that the weight of bitcoin is more stable when combined with gold, tether gold, Euro, Great Britain Pound, Swiss franc, and Japanese Yen. Thus, these assets are attractive for long-term investment strategies. This study provides investors and policymakers with significant insight into understanding safe-haven assets for bitcoin’s volatility and constructing a flexible portfolio that is dependent on the investment timeline and the prevailing market conditions. Full article

Exchange rate volatility poses significant challenges for emerging markets, influencing trade balances, inflation, and capital flows. South Africa’s Rand is particularly vulnerable to global risk sentiment, gold price fluctuations, and interest rate differentials, yet prior studies often analyse these factors in isolation. This study integrates them within an autoregressive distributed lag framework, using monthly data from 2005 to 2023 to capture both short-term fluctuations and long-term equilibrium effects. The findings confirm that higher global risk sentiment triggers immediate Rand depreciation, driven by capital outflows to safe-haven assets. Conversely, rising gold prices and favourable interest rate differentials stabilise the Rand, strengthening trade balances and attracting capital inflows. These results underscore the interconnected nature of global financial conditions and exchange rate movements. This study highlights the importance of economic diversification, foreign reserve accumulation, and proactive monetary policies in mitigating currency instability in emerging markets. Full article

We propose the financial generative adversarial network–bidirectional long short-term memory (FINGAN-BiLSTM) model to accurately reproduce the complex statistical properties and stylized facts, namely, heavy-tailed behavior, volatility clustering, and leverage effects observed in the log returns of the foreign exchange (FX) market. The proposed model integrates a bidirectional LSTM (BiLSTM) into the conventional FINGAN framework so that the generator, discriminator, and predictor networks simultaneously incorporate both past and future information, thereby overcoming the information loss inherent in unidirectional LSTM architectures. Experimental results, assessed using metrics such as the Kolmogorov–Smirnov statistic, demonstrate that FINGAN-BiLSTM effectively mimics the distributional and dynamic patterns of actual FX data. In particular, the model significantly reduces the maximum cumulative distribution discrepancy in assets with high standard deviations and extreme values, such as the Canadian dollar (CAD) and the Mexican Peso (MXN), while precisely replicating dynamic features like volatility clustering and leverage effects, thereby outperforming conventional models. The findings suggest that the proposed deep learning–based forecasting model holds significant promise for practical applications in financial risk assessment, derivative pricing, and portfolio optimization, and they highlight the need for further research to enhance its generalization capabilities through the integration of exogenous economic variables. Full article

Since the emergence of the Black–Scholes model (BSM) in the early 1970s, models for the pricing of financial options have been developed and evolved with mathematical tools that provide greater efficiency and accuracy in the valuation of these assets. In this research, we have used the generalized conformable derivatives associated with seven obtained conformable models with a closed-form solution that is similar to the traditional Black and Scholes. In addition, an empirical analysis was carried out to test the models with Mexican options contracts listed in 2023. Six foreign options were also tested, in particular three London options and three US options. With this sample, in addition to applying the seven generalized conformable models, we compared the results with the Heston model. We obtained much better results with the conformable models. Similarly, we decided to apply the seven conformable models to the data of the Morales et al. article, and we again determined that the conformable models greatly outperform the approximation of the Black, Scholes (BS), and Merton model with time-varying parameters and the basic Khalil conformable equation. In addition to the base sample, it was decided to test the strength of the seven generalized conformable models on 10 stock options that were out-sampled. In addition to the MSE results, for the sample of six options whose shares were traded in the London and New York stock markets, we tested the positivity and stability of the results. We plotted the values of the option contracts obtained by applying each of the seven generalized conformable models, the values of the contracts obtained by applying the traditional Heston model, and the market value of the contracts. Full article

Several studies have investigated the determinants of firms’ capital structure choices. Though an intercorporate loan is an essential source of corporate debt, there are no studies that examine the determinants of firms’ preference to use the intercorporate loan as a source of debt. This study examines the relevance of the conventional capital structure determinants in explaining firms’ tendency to use intercorporate loans. The study is based on a dataset of 53,112 firm-year observations comprising 3739 non-financial listed Indian firms for 21 years from 2002 to 2022. The random effect logistic regression model is used to investigate the objectives. The conventional capital structure determinants are relevant in explaining firms’ decisions to use intercorporate loans. Firm size, asset tangibility, and earnings volatility favorably influence the tendency to use intercorporate loans, whereas profitability, growth, uniqueness, dividend payment, ownership concentration, and foreign promoter holdings adversely affect the same. The results reveal that the influence of firm size, uniqueness, earnings volatility, and ownership concentration are not unidirectional for group-affiliated and standalone firms. The findings are mostly consistent with the arguments of conventional capital structure theories. The results of this study will be pragmatic for financial managers in their capital structure decisions. Full article

This study explores the following two aspects: (i) the impact of Environmental, Social, and Governance (ESG) scores and corporate ownership characteristics on the performance of Chinese listed companies, and (ii) whether different ownership characteristics (state-owned, private, foreign) moderate the relationship between ESG participation and corporate performance. By analyzing a comprehensive sample of 4649 listed companies in China, we provide robust evidence that ESG participation and its three pillars (i.e., Environmental, Social, and Governance) can significantly enhance corporate performance, as measured by the accounting-based proxy return on assets (ROA). Moreover, our research findings reveal an important and novel discovery: in the Chinese market, ownership types have significantly different moderating effects on the relationship between ESG and corporate performance. Specifically, compared to state-owned enterprises and private corporations, foreign ownership exhibits a stronger moderating effect in enhancing the positive impact of ESG on ROA, followed by private corporations, while the moderating effect of state-owned enterprises is the weakest. This result provides new perspectives and empirical support on how ESG and ownership structure jointly affect corporate performance, offering references for future related research and policy formulation. Full article

There is growing recognition that build-to-rent (BTR), a novel institutional asset class, could improve rental affordability and housing choice in Australia. Despite favourable market conditions and increasing demand, Australia’s BTR sector remains underdeveloped compared to the US and UK. Although the asset class has attracted significant foreign institutional capital, there is little interest from domestic institutional funds. This contrasting investment behaviour between foreign and domestic funds has brought a new dimension to the debates on BTR in Australia. The study uses qualitative research design to examine institutional investor behaviour towards BTR in Australia. Interviews were conducted with experienced BTR investors across three countries—Australia, the US, and the UK—to understand the barriers and investment behaviour towards BTR. The study finds that the key barriers hindering BTR growth in Australia include unfavourable tax treatment, complex planning processes, and insufficient affordable housing incentives. Institutional investors’ decisions are influenced by firm characteristics, operational capabilities, and risk attitudes. Due to risk considerations, Australian superfunds prefer stabilised assets over new developments. Also, sustainability and ESG factors are increasingly important considerations in BTR investment decisions. The research highlights the need for a supportive regulatory environment, efficient property management, and innovative financing solutions to boost BTR investments. To accelerate BTR growth in Australia, policymakers should address tax disparities, streamline planning processes, and enhance affordable housing incentives. Developing BTR-responsive financial instruments could reduce financing costs and attract more institutional capital to the sector. Full article

The dynamic impact of financial market capitalisation on South Africa’s unemployment and economic growth is empirically explored in this study using the finance-augmented Solow model framework. South Africa’s high rate of structural unemployment and its robust financial market, which is at the same standard as those in countries with advanced economies, served as the driving force for the study. Evidence for the dynamic link is presented by a time series analysis that employed the VECM model. South Africa continues to face persistent macroeconomic issues, including stagnant economic growth, declining investment, and rising unemployment. Market capitalisation, net acquisition of financial assets, and foreign direct investment all have a favourable and substantial effect on economic growth. According to VECM estimation results, unemployment has a detrimental effect on economic growth. Also, market capitalisation has significant positive effects on economic growth. Unemployment and economic growth are inversely related, thus unemployment has an adverse effect on economic growth. According to the findings, financial markets have distinct effects on economic growth because of their various functions within the economy. It was also shown that foreign direct investment has a crucial role in increasing economic growth. This implies the important role that the financial market and systems have in South Africa’s economic growth. The article advises authorities to keep enacting measures to boost capital market growth to increase employment, while also making sure that other structural issues affecting the labour market are effectively addressed to stimulate job creation. Full article

In this study, we investigate the pricing of two types of vulnerable foreign equity options using an intensity-based model. It is considered that the intensity process consists of both systematic and idiosyncratic components. In addition, we assume that the underlying asset processes follow a two-factor stochastic volatility model. Under the proposed model, we obtain the explicit pricing formulas of vulnerable foreign equity options. Finally, we provide some numerical examples to demonstrate how credit risk and stochastic volatility affect option prices. Full article

Earnings management is a widespread phenomenon in practice, with researchers therefore focusing on trying to understand what motives and factors lead to companies engaging in earnings management. In addition to internal financial and macroeconomic factors, the influence of institutional factors including corporate social responsibility (CSR) has been widely studied in recent years. In Lithuania, there have been no studies on the manipulation of accounting information in socially responsible companies. Therefore, this study aims to identify the impact of CSR on the application of earnings management in the context of internal financial and macroeconomic factors. The results of this study are significant as they not only enable assessing the impact of social responsibility on the application of earnings management in Lithuanian companies, but also the influence of macroeconomic factors such as the gross domestic product (GDP), inflation, foreign direct investment (FDI), average wages, and unemployment, as well as internal financial factors such as leverage, returns on assets (RoA), and the profitability of EBIT. The results show that CSR reduces the use of earnings management, regardless of whether it is accrual-based or real earnings management. Additionally, this analysis demonstrates that, among the internal financial factors, leverage carries the most substantial influence. The higher a company’s leverage, the more inclined that company is to use earnings management. Exploring the impact of macroeconomic indicators, it was found that the GDP, inflation, and unemployment rate have a statistically significant impact on the use of earnings management, albeit only if the firm uses accrual-based earnings management and adopts a profit-enhancing strategy. Full article

Encouraging cities to take the lead in achieving carbon peak and carbon neutrality holds significant global implications for addressing climate change. However, existing studies primarily focus on the urban scale, lacking more comprehensive county-level analyses, which hampers the effective implementation of differentiated carbon mitigation policies. Therefore, this study focused on the Pearl River Delta urban agglomeration in China, adopting nighttime light data and socio-economic spatial data to estimate carbon emissions at the county level. Furthermore, trend analysis, spatial autocorrelation analysis, and Geodetector were adopted to elucidate the spatiotemporal patterns and influencing factors of county-level carbon emissions. Carbon emissions were predominantly concentrated in the counties on the eastern bank of the Pearl River Estuary. Since 2010, there has been a deceleration in the growth rate of carbon emissions in the region around the Pearl River Estuary, with some counties exhibiting declining trends. Throughout the study period, construction land expansion consistently emerged as a predominant factor driving carbon emission growth. Additionally, foreign direct investment, urbanization, and fixed asset investment each significantly contributed to the increased carbon emissions during different development periods. Full article

The Rurality Index is an important reference for the formulation of rural development strategies and policies, but the evaluation of the rurality of megacities based on the township scale is relatively limited. Based on the perspective of spatial governance, this study constructed the evaluation index system of Shanghai’s rurality and carried out the evaluation of Shanghai’s rurality at the township scale from 2005 to 2020. The article adopts the MGWR model to analyze the driving effects of five key driving factors (the proportion of foreign population, per capita industrial output value, public finance revenue, social fixed asset investment, and rail transit coverage), and adopts the Geo-Detector model to analyze the interactive driving effects of two factors. The results indicate that the rurality index of megacities and townships as a whole shows a weakening trend, and the above factors have a predominantly negative impact on rurality, with differences in the intensity of the impact in different periods. There is an obvious interactive additive effect between the factors. When formulating policies for township development, government departments need to take into account the functional positioning of the region and comprehensively adopt targeted policies on population, industry, transportation, finance and investment to regulate and guide the transformation or sustainable development of the countryside. Full article