Search Results (665)

This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. The PVAR-CEE reveals numerous unexpected findings related to carbon markets: ETS returns exhibit persistence with an autoregressive coefficient of −0.137 after a four-month lag, while increasing inflation results in rising ETS after the same period. Furthermore, ETSs generate spillover effects in the real economy, as elevated ETSs today forecast a 0.125-point reduction in unemployment one month later and a 0.0173 increase in inflation after two months. Impulse response analysis indicates that exogenous shocks, including Brent oil prices, policy uncertainty, and financial volatility, are swiftly assimilated by ETS pricing, with effects dissipating completely within three to eight months. XGBoost models ascertain that policy uncertainty and Brent oil prices are the most significant predictors of one-month-ahead ETSs, whereas ESG factors are relevant only beyond certain thresholds and in conditions of low policy uncertainty. These findings establish ETS markets as dynamic transmitters of macroeconomic signals, influencing energy management, labor changes, and sustainable finance under carbon pricing frameworks. Full article

This study evaluates the risk-adjusted performance of Hospitality REITs using multi-factor asset pricing models and downside risk measures with the aim of assessing their diversification potential and crisis sensitivity. Unlike prior studies that examine REITs in aggregate, this study isolates Hospitality REITs to explore their unique cyclical and macroeconomic sensitivities. This study looks at the risk-adjusted performance of Hospitality Real Estate Investment Trusts (REITs) in relation to more general REIT indexes and the S&P 500 Index. The study reveals that monthly returns of Hospitality REITs increasingly move in tandem with the stock markets during financial crises, which reduces their historical function as portfolio diversifiers. Investing in Hospitality REITs exposes one to the hospitality sector; however, these investments carry notable risks and provide little protection, particularly during economic upheavals. Furthermore, the study reveals that Hospitality REITs underperform on a risk-adjusted basis relative to benchmark indexes. The monthly returns of REITs show significant volatility during the post-COVID-19 era, which causes return-to-risk ratios to be below those of benchmark indexes. Estimates from multi-factor models indicate negative alpha values across conditional models, indicating that macroeconomic variables cause unremunerated risks. This industry shows great sensitivity to market beta and size and value determinants. Hospitality REITs’ susceptibility comes from their showing the most possibility for exceptional losses across asset classes under Value at Risk (VaR) and Conditional Value at Risk (CvaR) downside risk assessments. The findings have implications for investors and portfolio managers, suggesting that Hospitality REITs may not offer consistent diversification benefits during downturns but can serve a tactical role in procyclical investment strategies. Full article

This study examines the impact of geopolitical risk (GPR) on the connectedness dynamics among the sovereign bonds of the emerging seven (E7) and the Group of Seven (G7) countries. Initially, a quantile-based vector-autoregressive (Q-VAR) connectedness approach is used to calculate the total connectedness index (TCI) among sovereign bonds under different market states. Then, the impact of GPR on the TCI at the median and tails is estimated to examine if GPR affects the TCI among sovereign bonds. Using daily yields from 30 January 2012, to 17 June 2024, the findings show that the GPR is one of the significant determinants of the TCI among sovereign bonds during normal and extreme market conditions. Other determinants of the TCI include yields on Treasury bills (T-bills), the exchange rate, and the financial market volatility index. The impact of GPR on the TCI varies significantly during different GPR episodes and bond market conditions. The effect of GPR on the TCI among sovereign bonds yields is higher during war times and when bond yields are average. These findings can be utilized by investors seeking to achieve international diversification and policymakers aiming to mitigate the effects of heightened geopolitical risk on financial stability. Furthermore, GPR can be used as an early signal tool for systematic tail risk spillovers among sovereign bonds. Full article

As investment portfolios become increasingly diversified and financial asset risks grow more complex, accurately forecasting the risk of multiple asset classes through mathematical modeling and identifying their heterogeneity has emerged as a critical topic in financial research. This study examines the volatility and tail risk of gold, crude oil, Bitcoin, and selected stock markets. Methodologically, we propose two improved Value at Risk (VaR) forecasting models that combine the autoregressive (AR) model, Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, Extreme Value Theory (EVT), skewed heavy-tailed distributions, and a rolling window estimation approach. The model’s performance is evaluated using the Kupiec test and the Christoffersen test, both of which indicate that traditional VaR models have become inadequate under current complex risk conditions. The proposed models demonstrate superior accuracy in predicting VaR and are applicable to a wide range of financial assets. Empirical results reveal that Bitcoin and the Chinese stock market exhibit no leverage effect, indicating distinct risk profiles. Among the assets analyzed, Bitcoin and crude oil are associated with the highest levels of risk, gold with the lowest, and stock markets occupy an intermediate position. The findings offer practical implications for asset allocation and policy design. Full article

Global financial markets have experienced significant volatility during crises, particularly COVID-19 and the Russia–Ukraine conflict, prompting questions about how regional markets respond to such shocks. Previous research highlights the influence of crises on stock market volatility, focusing on individual events or global markets, but less is known about the comparative dynamics within the Gulf Cooperation Council (GCC) markets. Our study investigated volatility and asymmetric behavior within GCC stock markets during both crises. Furthermore, the econometric model E-GARCH(1,1) was applied to the daily frequency data of financial stock market returns from 11 March 2020 to 31 July 2023. This study examined volatility fluctuation patterns and provides a comparative assessment of GCC stock markets’ behavior during crises. Our findings reveal varying degrees of market volatility across the region during the COVID-19 crisis, with Qatar and the UAE exhibiting the highest levels of volatility persistence. In contrast, the Russia–Ukraine conflict has had a distinct effect on GCC markets, with Oman exhibiting the highest volatility persistence and Kuwait having the lowest volatility persistence. This study provides significant insights for policymakers and investors in managing risk and enhancing market resilience during economic and geopolitical uncertainty. 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

Over the past decade, multifactor models have shown enhanced capability compared to single-factor models in explaining asset return variability. Given the common assertion that higher risk tends to yield higher returns, this study empirically examines the augmented human capital six-factor model’s performance on thirty-two portfolios of non-financial firms sorted by size, value, profitability, investment, and labor income growth in the Indian market over the period July 2010 to June 2023. Moreover, the current study extends the Fama and French five-factor model by incorporating a human capital proxy by labor income growth as an additional factor thereby proposing an augmented six-factor asset pricing model (HC6FM). The Fama and MacBeth two-step estimation methodology is employed for the empirical analysis. The results reveal that small-cap portfolios yield significantly higher returns than large-cap portfolios. Moreover, all six factors significantly explain the time-series variation in excess portfolio returns. Our findings reveal that the Indian stock market experienced heightened volatility during the COVID-19 pandemic, leading to a decline in the six-factor model’s efficiency in explaining returns. Furthermore, Gibbons, Ross, and Shanken (GRS) test results reveal mispricing of portfolio returns during COVID-19, with a stronger rejection of portfolio efficiency across models. However, the HC6FM consistently shows lower pricing errors and better performance, specifically during and after the pandemic era. Overall, the results offer important insights for policymakers, investors, and portfolio managers in optimizing portfolio selection, particularly during periods of heightened market uncertainty. Full article

In the context of macroeconomic recovery and accelerating digital transformation in the post-pandemic era, the hotel industry in China is undergoing profound structural changes. This research investigates the core competencies required for hotel managers to navigate these challenges. Data was collected via a quantitative survey involving a structured questionnaire, was conducted among hotel managers in Shanghai, China, resulting in 404 valid responses. Employing exploratory factor analysis using SPSS, this study identifies seven key competency dimensions encompassing 36 ranked items, including interpersonal communication, leadership, operational knowledge, human resource management, financial analysis, technology, and administrative management. The results show that economic recovery has brought new opportunities but also challenges to the hotel industry, and that managers must possess a diverse set of core competencies to adapt to the demanding new market changes. The novelty of this research lies in its empirical grounding and its focus on the intersection of digitalization and economic recovery within China’s hotel industry. It pioneers a dynamic strategic competency framework tailored to the evolving demands of the hotel industry during a period of economic volatility, providing empirical evidence and advice for optimizing the industry’s talent training systems. Simultaneously, it brings a new perspective for dealing with the recovery path for the hotel enterprises in other urban and travel destinations, aiming to promote industry sustainability and competitive advantages. Future research could extend the proposed framework by exploring its applicability across different cultural and economic contexts. Full article

This study examined the relationship between margin trading and cryptocurrency investment using data from the 2018 and 2021 waves of the National Financial Capability Study (NFCS) Investor Survey. Guided by behavioral finance theory, which suggests that cognitive biases may influence risk-taking, the study explored whether margin loan use and margin calls are associated with higher cryptocurrency participation. Margin loans are inherently risky, as they must be repaid regardless of investment outcomes, and margin calls are triggered when an investor’s equity falls below a required threshold. The results showed a positive and statistically significant association between margin activity and cryptocurrency investment. Specifically, individuals with a margin loan were 17 percentage points more likely to invest in cryptocurrency, while those who have experienced a margin call were 23 percentage points more likely. Given the extreme volatility of cryptocurrencies, these results highlight the increased risks investors face when using leverage in speculative markets. The analysis is based on cross-sectional data from U.S. investors; therefore, the findings should be interpreted as correlational rather than causal. Full article

Globalization and the spread of technological innovations have made world markets and economies increasingly unified and conditioned by international trade, not only for sales markets but above all for the supply of raw materials necessary for the functioning of the production complex of each country. Alongside oil and gold, the main commodities traded include industrial metals, such as aluminum and copper, mineral products such as gas, electrical and electronic components, agricultural products, and precious metals. The conflict between Russia and Ukraine tested the unification of markets, given that these are countries with notable raw materials and are strongly dedicated to exports. This suggests that commodity prices were able to influence the stock markets, especially in the countries most closely linked to the two belligerents in terms of import-export. Given the importance of industrial metals in this period of energy transition, the aim of our study is to analyze whether Industrial Metals volatility affects G7 stock markets. To this end, the BEKK-GARCH model is used. The sample period spans from 3 January 2018 to 17 September 2024. The results show that lagged shocks and volatility significantly and positively influence the current conditional volatility of commodity and stock returns during all periods. In fact, past shocks inversely influence the current volatility of stock indices in periods when external events disrupt financial markets. The results show a non-linear and positive impact of commodity volatility on the implied volatility of the stock markets. The findings suggest that the war significantly affected stock prices and exacerbated volatility, so investors should diversify their portfolios to maximize returns and reduce risk differently in times of crisis, and a lack of diversification of raw materials is a risky factor for investors. Full article

Predicting and trading assets in the global financial market represents a complex challenge driven by the dynamic and volatile nature of the sector. This study proposes a day trading strategy that optimizes asset purchase and sale parameters using differential evolution. To this end, an innovative financial indicator was developed, and machine learning models were employed to improve returns. The work highlights the importance of optimizing training sets for machine learning algorithms based on probable asset behaviors (scenarios), which allows the development of a robust model for day trading. The empirical results demonstrate that the LSTM algorithm excelled, achieving approximately 98% higher returns and an 82% reduction in DrawDown compared to asset variation. The proposed indicator tracks asset fluctuation with comparable gains and exhibits lower variability in returns, offering a significant advantage in risk management. The strategy proves to be adaptable to periods of turbulence and economic changes, which is crucial in emerging and volatile markets. Full article

The integration of artificial intelligence (AI) and advanced deep learning techniques is reshaping intelligent financial forecasting and decision-support systems. This study presents a comprehensive comparative analysis of advanced deep learning models, including state-of-the-art transformer architectures and established non-transformer approaches, for long-term stock market index prediction. Utilizing historical data from major global indices (S&P 500, NASDAQ, and Hang Seng), we evaluate ten models across multiple forecasting horizons. A dual-metric evaluation framework is employed, combining traditional predictive accuracy metrics with critical financial performance indicators such as returns, volatility, maximum drawdown, and the Sharpe ratio. Statistical validation through the Mann–Whitney U test ensures robust differentiation in model performance. The results highlight that model effectiveness varies significantly with forecasting horizons and market conditions—where transformer-based models like PatchTST excel in short-term forecasts, while simpler architectures demonstrate greater stability over extended periods. This research offers actionable insights for the development of AI-driven intelligent financial forecasting systems, enhancing risk-aware investment strategies and supporting practical applications in FinTech and smart financial analytics. Full article

This study investigates the relationship between overconfidence and meme stock valuation, drawing on panel data from 28 meme stocks listed from 2019 to 2024. The analysis incorporates key financial indicators, including Tobin’s Q ratio, market capitalization, return on assets, leverage, and volatility. A range of overconfidence proxies is employed, including changes in trading volume, turnover rate, changes in outstanding shares, and alternative measures of excessive trading. We observe a significant positive relationship between overconfidence (as measured by changes in trading volume) and firm valuation, suggesting that investor biases contribute to notable pricing distortions. Leverage has a significant negative relationship with firm valuation. In contrast, market capitalization has a significant positive relationship with firm valuation, implying that meme stock investors respond to both speculative sentiment and traditional firm fundamentals. Robustness checks using alternative proxies reveal that turnover rate and changes in the number of shares are negatively related to valuation. This shows the complex dynamics of meme stocks, where psychological factors intersect with firm-specific indicators. However, results from a dynamic panel model estimated using the Dynamic System Generalized Method of Moments (GMM) show that the turnover rate has a significantly positive relationship with firm valuation. These results offer valuable insights into the pricing behavior of meme stocks, revealing how investor sentiment impacts periodic valuation adjustments in speculative markets. 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

The stock price of PT Indika Energy Tbk (INDY) reflects the dynamics of Indonesia’s energy sector, which is heavily influenced by global coal price fluctuations, national energy policies, and geopolitical conditions. This study aimed to develop an accurate forecasting model to predict the movement of INDY stock prices using a hybrid machine learning approach called CNN-BiGRU-AM. The objective was to generate future forecasts of INDY stock prices based on historical data from 28 August 2019 to 24 February 2025. The method applied a hybrid model combining a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and an Attention Mechanism (AM) to address the nonlinear, volatile, and noisy characteristics of stock data. The results showed that the CNN-BiGRU-AM model achieved high accuracy with a Mean Absolute Percentage Error (MAPE) below 3%, indicating its effectiveness in capturing long-term patterns. The CNN helped extract local features and reduce noise, the BiGRU captured bidirectional temporal dependencies, and the Attention Mechanism allocated weights to the most relevant historical information. The model remained robust even when stock prices were sensitive to external factors such as global commodity trends and geopolitical events. This study contributes to providing more accurate forecasting solutions for companies, investors, and stakeholders in making strategic decisions. It also enriches the academic literature on the application of deep learning techniques in financial data analysis and stock market forecasting within a complex and dynamic environment. Full article