Search Results (8)

This study investigates the cross-country spillover effects of investor sentiment, specifically Fear and Greed, between the United States and South Africa, within the broader context of increasing global financial integration and behavioral finance. Using monthly data from June 2007 to June 2024, this research constructs and tests the validity of a South African Fear and Greed Index, adapted from CNN’s U.S.-centric index, to better capture the unique dynamics and contribute to an alternate sentiment index for an emerging market. Employing the Diebold and Yilmaz (DY) connectedness framework, this study quantifies both static and dynamic spillover effects via a vector autoregression-based variance decomposition model. The results reveal significant bidirectional sentiment transmission, with the U.S. acting as a dominant net transmitter and South Africa as a net receiver, along with notable cross-country effects closely linked to the global economic trend. Spillover intensity escalates during periods of global economic stress, such as the 2008 financial crisis and the COVID-19 pandemic. The findings highlight that the USA significantly influences South Africa and that the adapted SA Fear and Greed Index better accounts for South African market conditions. Full article

Amidst global economic fluctuations and escalating geopolitical risks, gold futures, as a pivotal safe-haven asset, demonstrate price dynamics that directly impact investor decision-making and risk mitigation effectiveness. Traditional forecasting models face significant limitations in capturing long-term trends, addressing abrupt volatility, and mitigating multi-source noise within complex market environments characterized by nonlinear interactions and extreme events. Current research predominantly focuses on single-model approaches (e.g., ARIMA or standalone neural networks), inadequately addressing the synergistic effects of multimodal market signals (e.g., cross-market index linkages, exchange rate fluctuations, and policy shifts) and lacking the systematic validation of model robustness under extreme events. Furthermore, feature selection often relies on empirical assumptions, failing to uncover non-explicit correlations between market factors and gold futures prices. A review of the global literature reveals three critical gaps: (1) the insufficient integration of temporal dependency and global attention mechanisms, leading to imbalanced predictions of long-term trends and short-term volatility; (2) the neglect of dynamic coupling effects among cross-market risk factors, such as energy ETF-metal market spillovers; and (3) the absence of hybrid architectures tailored for high-frequency noise environments, limiting predictive utility for decision support. This study proposes a three-stage LSTM–Transformer–XGBoost fusion framework. Firstly, XGBoost-based feature importance ranking identifies six key drivers from thirty-six candidate indicators: the NASDAQ Index, S&P 500 closing price, silver futures, USD/CNY exchange rate, China’s 1-year Treasury yield, and Guotai Zhongzheng Coal ETF. Second, a dual-channel deep learning architecture integrates LSTM for long-term temporal memory and Transformer with multi-head self-attention to decode implicit relationships in unstructured signals (e.g., market sentiment and climate policies). Third, rolling-window forecasting is conducted using daily gold futures prices from the Shanghai Futures Exchange (2015–2025). Key innovations include the following: (1) a bidirectional LSTM–Transformer interaction architecture employing cross-attention mechanisms to dynamically couple global market context with local temporal features, surpassing traditional linear combinations; (2) a Dynamic Hierarchical Partition Framework (DHPF) that stratifies data into four dimensions (price trends, volatility, external correlations, and event shocks) to address multi-driver complexity; (3) a dual-loop adaptive mechanism enabling endogenous parameter updates and exogenous environmental perception to minimize prediction error volatility. This research proposes innovative cross-modal fusion frameworks for gold futures forecasting, providing financial institutions with robust quantitative tools to enhance asset allocation optimization and strengthen risk hedging strategies. It also provides an interpretable hybrid framework for derivative pricing intelligence. Future applications could leverage high-frequency data sharing and cross-market risk contagion models to enhance China’s influence in global gold pricing governance. Full article

Using a sample of S&P 500 stocks, this paper examines the investor sentiment spillover network between firms and assesses how the sentiment connectedness in the network impacts stock price crash risk. We demonstrate that firms with higher sentiment connectedness are more likely to crash as they spread more irrational sentiment signals and are more sensitive to investor behaviors. Notably, we find that the effect of investor sentiment on crash risk mainly stems from sentiment connectedness among firms rather than firms’ individual sentiment, especially when market sentiment is surging or declining. These findings remain robust after controlling for other determinants of crash risk, including stock price synchronicity, accounting conservatism, and internal corporate governance strength. Our results underscore the importance of sentiment connectedness among firms and provide valuable insights for risk management among investors and regulatory authorities involved in monitoring risk. Full article

This paper examines the quantile connectedness between Real Estate Investment Trusts (REITs), housing market sentiment, and stock market sentiment in the U.S. over the period between January 2014 and June 2022 using the quantile vector autoregression (QVAR) model. We find modest spillover effects at the median quantile (8.51%), which become more pronounced at the extreme tails (between 50.51% and 59.73%). The COVID-19 pandemic amplifies these interconnections. REITs are net receivers at the median but net transmitters at extreme quantiles, while stock market sentiment mainly transmits during normal conditions and receives in highly bullish markets. Home purchase sentiment shifts from fluctuating roles before the pandemic to being a net transmitter post-2021. Overall, negative shocks have a greater impact than positive ones, and REITs exhibit stock-like behavior. These findings underscore the importance for fund managers and investors to consider sentiment volatility in both stock and real estate markets, especially during extreme market conditions. Full article

This paper seeks to investigate the connectivity of the US economy through the dynamics of the transmission of volatility in sectoral indices. For this, we use daily asset data and two methodologies. The first creates a spillover index that measures market connectivity and the second partitions this index into different frequency bands that denote periods. We found results that show significant transmissions of volatility among the 64 analyzed assets. Notably, the DJIA, Wilshire 5000, and S&P 500 showed significant volatility and were the main drivers of volatility for the other sectors and indices. Results also indicated that sectors that transferred volatility were influenced by three key factors: periods of economic uncertainty, socioeconomic circumstances resulting from post-crisis events, and the impact of economic and financial news on market sentiment. Additionally, we found that global returns and price changes in market indices sent considerable volatility into commodity assets. Our results are potentially useful for investors, portfolio managers, financial economists, financial advisors, financial market regulators, and policymakers. Full article

This study investigates interactions between energy funds and the oil market and examines the influence of environmental, social, and governance (ESG) criteria in dynamic responses by fund managers and investors. We test for price and volatility transmission (also referred to as “spillover”) between energy funds and the oil market using recently developed econometric techniques. After identifying specific information flows, we investigate whether certain fund characteristics, including several ESG dimensions, are associated with the existence of information transmissions. Then, in logit regressions, we seek to identify if energy fund managers and their investors make decisions using information regarding ESG metrics, including fossil fuel involvement. The results confirm bidirectional price and volatility transmission between energy funds and the oil market, consistent with evidence of the financialization of energy markets that has been identified in recent studies. Several ESG dimensions are shown to influence investor sentiment and affect price and volatility interactions. Dynamic investor decisions in funds in reaction to oil prices do not appear to be strongly influenced by the fossil fuel involvement of the funds. Fund flows do appear to influence the oil market, with fund fossil fuel involvement being an important factor. This paper evaluates the impact of granular ESG characteristics on energy mutual fund flows, price, and volatility interactions with the oil market. While our results support the findings from previous studies, they also provide several new insights into the impacts of ESG criteria and investor behavior, particularly the dynamic response by fund managers and energy market investors related to the fossil fuel involvement of the funds. Full article

Global crises have created unprecedented challenges for communities and economies across the world, triggering turmoil in global finance and economy. This study adopts the dynamic conditional correlation multiple generalized autoregressive conditional heteroskedasticity (DCC–MGARCH) model to explore contagion effects across financial markets in crisis. The main findings are as follows: (1) the financial crisis and COVID-19 pandemic intensified the connection between the Chinese and US stock markets in the short term; (2) the dynamic conditional correlations (DCCs) during the COVID-19 pandemic are higher than those during the 2008 financial crisis owing to the further opening of the Chinese capital market, and financial institutions’ investments in the European market are higher than those in the American markets; (3) a stepwise increase is observed in the dynamic conditional correlation between the returns on the S&P 500 Index and SSEC during and after the onset of a destructive crisis; and (4) a unidirectional contagion effect exists between the Chinese market and US market, and the Hong Kong stock market contributes to the risk spillover. Effective transmission channels of external negative shocks may be investors’ sentiments, financial institutions, and the RMB exchange rate in the stock markets. This study provides useful suggestions to authorities formulating financial regulations and investors diversifying risk investments. Full article

This paper investigates how investor sentiment affects stock market returns and evaluates the predictability power of sentiment indices on U.S. and EU stock market returns. As regards the American example, evidence shows that investor sentiment indices have an economic and statistical predictability power on stock market returns. Concerning the European market instead, investigation provides weak results. Moreover, comparing the two markets, where investor sentiment of U.S. market tries to predict the European stock market returns, and vice versa, the analyses indicate a spillover effect from the U.S. to Europe. Full article