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The evolution of three sector indices for the US and the Chinese stock market. (a) The evolutions of the price of three sector indices selected from the US stock market; (b) the evolutions of the price of three sector indices selected from the Chinese stock market.
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As a most important component of capital market, stock market has always been regarded as the “barometer” of macroeconomy. However, many researchers have found that the stock market is not always in the lead, especially for the emerging markets, and the leading role of different sector indices is also different for the corresponding sectors. From t...
Citations
... According to the International Monetary Fund (IMF), the combined contributions of these two countries are anticipated to constitute approximately half of the global growth in 2023 (Thomas Helbling & Srinivasan, 2023). The world's largest developing markets have risen swiftly in recent decades, yet real-world examples have shown that market volatility has remained high over the years (Jin & Guo, 2021). India has initiated a production-linked incentive program valued at $26 billion. ...
This study empirically investigates the volatility spillover among the sectors of emerging markets, that is, India and China and developed markets, that is, the United Kingdom (UK) and the United States (US). Focusing on financial services, auto, oil and gas, Information Technology (IT), healthcare and real estate sectors, the research employs the BEKK GARCH and GO-GARCH models to analyze the daily data. Results reveal that the own market’s conditional volatility is primarily responsible for the volatility spillover in every sector. Further, the study also found evidence of major cross-market volatility spillover in the oil and gas, IT, healthcare and real estate sectors of emerging and developed markets. Specifically, the US IT sector dominated other markets’ IT sectors. The hedge ratio indicates that hedging between sectors of the emerging and developed markets is the cheapest, contrasting with the higher cost for hedging solely with the emerging or developed markets sectors. Investors are advised to monitor and rebalance their portfolios based on the volatility and dynamics of developed market sectors for optimum return. Additionally, the study found that the BEKK model is better for risk-return optimization.
... Choi and Kim conducted an empirical analysis on politically-themed stocks in South Korea, creating networks based on these stocks influenced by political figures [11]. Jin and Guo (2021) showed that since 2013, specific sector indices like consumption, industry, and real estate have been leading corresponding macroeconomic variables in the U.S. stock market [12]. Finally, Mensi (2022) found that sectors like oil, gold, financials, utilities, communication services, consumer staples, and healthcare are net recipients of spillovers, while other sectors are net contributors, determined through methods like the time-frequency spillover method, wavelet method, and the DCC-GARCH model [13]. ...
... Choi and Kim conducted an empirical analysis on politically-themed stocks in South Korea, creating networks based on these stocks influenced by political figures [11]. Jin and Guo (2021) showed that since 2013, specific sector indices like consumption, industry, and real estate have been leading corresponding macroeconomic variables in the U.S. stock market [12]. Finally, Mensi (2022) found that sectors like oil, gold, financials, utilities, communication services, consumer staples, and healthcare are net recipients of spillovers, while other sectors are net contributors, determined through methods like the time-frequency spillover method, wavelet method, and the DCC-GARCH model [13]. ...
This study presents a novel approach to predicting price fluctuations for U.S. sector index ETFs. By leveraging information-theoretic measures like mutual information and transfer entropy, we constructed threshold networks highlighting nonlinear dependencies between log returns and trading volume rate changes. We derived centrality measures and node embeddings from these networks, offering unique insights into the ETFs’ dynamics. By integrating these features into gradient-boosting algorithm-based models, we significantly enhanced the predictive accuracy. Our approach offers improved forecast performance for U.S. sector index futures and adds a layer of explainability to the existing literature.
... While there is a significant body of research in this area, more comprehensive studies are needed to evaluate this relationship in various sectors of the U.S. stock market (Jin & Guo, 2021). The main idea of the research is to address this gap, thereby providing novel insights into the sectorspecific effects of macroeconomic variables on stock prices. ...
... Despite substantial research into the macroeconomic determinants of stock price movement, numerous gaps exist. Existing studies tend to focus on an aggregated analysis of stock market performance, often overlooking potential sector-specific responses to macroeconomic factors (Jin & Guo, 2021). This broad approach may overlook the distinct sensitivities and characteristics of various sectors, such as technology, healthcare, or energy, which may respond differently to changes in macroeconomic variables. ...
... However, in Exchange rate, Jin and Guo (2021) found a negative relationship between immediate exchange rate movements and the industrial sector, with a coefficient of -1.5 and a pvalue less than 0.05. Our study aligns with Jin and Guo (2021) regarding the direction of the relationship. ...
This research delves into the nuanced interplay between selected macroeconomic variables and their influence on the performance of stock market indices across diversified sectors in the US stock markets. By employing the Autoregressive Distributed Lag (ARDL) model, the study intricately dissects both immediate and prolonged impacts within a chronological series. The investigation unveils that different sectors manifest varied levels of susceptibility to macroeconomic alterations. This heterogeneity, in response, accentuates the multifaceted nature of stock market reactions to economic stimuli. The research not only characterizes the intensity and orientation of these associations but also furnishes pivotal insights into the stock market's vulnerability vis-à-vis overarching economic transformations. The distilled findings from this research cater to a broad audience, encompassing policymakers, investors, and academic enthusiasts, aiding them in preempting market comportments in response to economic vicissitudes.
Keywords: ARDL methodology, Macroeconomic Variables, stock market dynamics, sectoral variations, economic reactivity, Time Series.
... In another thread, there are abundant studies dedicated to the comparative studies on the two major stock markets, namely the Chinese and US markets [29,[64][65][66][67]. Although RMT has been applied to stock markets, there is still a lack of comprehensive studies using RMT to analyze the Chinese and US markets. ...
... Particularly, some works investigated the markets of the US and China [64,66]. According to the strong connection between financial assets and institutions and the diversity as well as the localization of the stock market, one study previously analyzed the topological structure of financial networks of two major markets of China and the US with complex network theory [29]. ...
This research systematically analyzes the behaviors of correlations among stock prices and the eigenvalues for correlation matrices by utilizing random matrix theory (RMT) for Chinese and US stock markets. Results suggest that most eigenvalues of both markets fall within the predicted distribution intervals by RMT, whereas some larger eigenvalues fall beyond the noises and carry market information. The largest eigenvalue represents the market and is a good indicator for averaged correlations. Further, the average largest eigenvalue shows similar movement with the index for both markets. The analysis demonstrates the fraction of eigenvalues falling beyond the predicted interval, pinpointing major market switching points. It has identified that the average of eigenvector components corresponds to the largest eigenvalue switch with the market itself. The investigation on the second largest eigenvalue and its eigenvector suggests that the Chinese market is dominated by four industries whereas the US market contains three leading industries. The study later investigates how it changes before and after a market crash, revealing that the two markets behave differently, and a major market structure change is observed in the Chinese market but not in the US market. The results shed new light on mining hidden information from stock market data.
... Stock markets have been emphasized repeatedly as the representative proxy of the financial market. However, the stock exchanges also have been contemplated as the "yardstick" of the macroeconomy since they are the most significant part of the capital market (Jin and Guo, 2021). Financial markets and real economic output are linked. ...
... Numerous academics have discovered that the stock market does not always take the lead, particularly for developing countries, and that various sector indexes play varying degrees of leadership for the relevant industries (Jin and Guo, 2021). Their study used the thermal optimum approach to investigate the evolving lead-lag correlations between stock market benchmark index and macroeconomic factors for the United States and China from sectoral comparability between developed economies and developing markets. ...
This research aims at finding the association between the
real economy and its financial market counterpart in the real estate sector in India. Firstly, it would perform a sectoral analysis to establish a short/long-run relationship between the real economy and financial markets. Secondly, it would accomplish a focused pairwise comparison in this context to empirically demonstrate the relationship. Thirdly, this study would contemplate the development of a comprehensive index reflecting the health of the real estate sector by incorporating critical variables from both the real economy and financial market. Lastly, this research points out plausible sectoral variability in the stock movement alongside their causal relationship with the major macroeconomic indicators.
... Its structure and trading activities are complex and influential. It is the thermometer of national economic development (Jin and Guo, 2021). The factors that affect the volatility of the stock market are very complex, showing highly nonlinear and dynamic characteristics (Barra et al., 2020), which increases the difficulty and risk of investment. ...
Stock market analysis is helpful for investors to make reasonable decisions and maintain market stability, and it usually involves not only quantitative data but also qualitative information, so the analysis method needs to have the ability to deal with both types of information comprehensively. In addition, due to the inherent risk of stock investment, it is necessary to ensure that the analysis results can be traced and interpreted. To solve the above problems, a stock market analysis method based on evidential reasoning (ER) and hierarchical belief rule base (HBRB) is proposed in this paper. First, an evaluation model is constructed based on expert knowledge and ER to evaluate stock market sentiment. Then, a stock market decision model based on HBRB is constructed to support investment decision making, such as buying and selling stocks and holding positions. Finally, the Shanghai Stock Index from 2010 to 2019 is used as an example to verify the applicability and effectiveness of the proposed stock market analysis method for investment decision support. Experimental research demonstrates that the proposed method can help analyze the stock market comprehensively and support investors to make investment decisions effectively.
... The stock market is a leading factor in other economic variables, capable of determining the direction of economic changes and serving as an economic "barometer." China's stock market has evolved for more than two decades since the turn of the century, but its maturity remains low, often resulting in increasing market volatility (Jin & Guo, 2021). Since the establishment of the stock exchange and the start of trading, the stock market's operation has demonstrated a high degree of instability. ...
... Since the establishment of the stock exchange and the start of trading, the stock market's operation has demonstrated a high degree of instability. This has disrupted internal connections with economic trends to some level, causing the Chinese stock market's "barometer" function to be steadily questioned and challenged, which is not beneficial to the stock market's long-term growth [4]. ...
... The functions provide more direct and important impact factors, enabling new thinking modes for China's Securities investment sector, refining the Chinese stock market's construction, and fully using the stock market's "barometer" function. Applying the Western stock market economic "barometer" function to the Chinese market is still uncertain (Jin & Guo, 2021). It remains to be seen if China can completely embody the "barometer" role of the Chinese stock market. ...
The rise of China and the five-fold increase in the expanse of its stock market over the previous decade has spurred a rising body of research in financial economics on the marketplace. The link between the development of the Chinese stock market and China’s economy has always been an eternal topic. The existing literature has rarely systematically analyzed whether the stock market development is a barometric measure of China's economy. To this end, this paper examines the role of using the Chinese stock market’s development as a barometer of China's economy by studying the relationship between stock price changes in the stock market and China's Industrial Development Index.
... As a result, expected stock prices will rise. These outcomes are somewhat similar to those of studies such as Akbar et al. (2018), Ditimi and Sunday (2018), Abbas et al. (2019c), Celebi and Hönig (2019), and Jin and Guo (2021). Further, there is a strong positive correlation between inflation and stock market prices in China, implying that a money shock generates inflation and that increased liquidity may lower the interest rate, causing investors to shift their cash holdings to stocks in search of potential capital gains. ...
This study investigates the dynamic links between the stock market and macroeconomic fundamentals in China by using monthly data ranging from 2002:M2 to 2019:M12. Based on wavelet analysis, the results reveal that interrelatedness between stock and macro-economic returns is statistically significant at low, medium, and high frequencies in this country. We find that stock returns have a positive influence on the macroeconomic variables in the long run, indicating that the stock market leads macroeconomic factors. However, macroeconomic variables impact the stock market in the short term. In addition, we build the wavelet-based Granger causality test at various time scales to provide additional support to our causal association outcomes. The empirical findings of this study offer straightforward insights into investors and policymakers in connection with relationships between the stock market and macroeconomic variables in China.
... For this reason, investor sentiments are considered a crucial aspect of the capital market since they play a vital role in the constant fluctuations of the stock prices, thus creating uncertainty regarding future returns on investments (Concetto, & Ravazzolo, 2019). Jin, Z., & Guo, K. (2021). Carried out a study comparing mature markets represented by the US stock market and emerging markets represented by the Chinese stock market at the sectoral level. ...
Purpose: From a broader perspective, it is generally accepted that every investor aims to maximize return on their investment. To achieve this, the security market has significantly attracted so much interest from numerous stakeholders around the globe. However, it is difficult to forecast the stock index volatility exhaustively since it is triggered by different factors, which erode the investors' confidence. The impact of volatility in the stock market is not the same (Liu et al., 1998), and it is transmitted from one sector to the other. Therefore, this study seeks to establish the relationship between the selected macroeconomic variables sectoral index volatility after introducing the moderating effect of investor sentiment. Methodology: This review employed Systematic review research design to trace, gather and appraise relevant studies that address the relationship between the dependent and independent variables. Findings: The outcomes of the study review the existence of a conceptual framework gap as empirical literature does not offer conclusive results on the sectoral index volatility and how it is influence by macroeconomic variables and investor sentiment. Previous studies were majorly conducted at a different time period in other markets presenting a geographical gap, and without factoring sectoral perspective. Unique contribution to theory, practice and policy: The study will be beneficial to investors in portfolio formation for diversification purposes. The models developed from this study will aid the capital market authority and government to regulate listed firms in Kenya to develop policies that minimizes return volatility. The study will add new knowledge on sectoral index volatility to maximize market returns for listed firms in Kenya.
... An increase in output leads to a rising in income and consumption so that company generates high profit, which in turn will increase stock prices and reduce volatility (Abduh & Surur, 2013). Our findings confirm the previous studies such as Yusof and Majid (2007) in the context of Malaysian Islamic and conventional stock markets and in the context of China and the US stock market (Jin & Guo, 2021). ...
Islamic stock market is apparently different from the conventional stock market due to the prohibition of unlawful goods and excessive risk-taking behavior. This study explores the extent to which the Indonesian Islamic and conventional stock returns' volatility responds to the macroeconomic indicators. This study employs Jakarta Islamic Index (JII) and Indonesian Stock Exchange (IDX) and uses monthly time-series data covering 2001: M1 - 2019: M12. The volatility of stock returns is measured using Generalized Autoregressive Conditional Heteroskedasticity (GARCH). By employing the Autoregressive Distributed Lag Model (ARDL), the results validate the evidence of the long-run relationship between the stock market's volatility and macroeconomic variables. A rising in money supply and an economic upturn reduce the volatility of conventional stock returns but only an expansionary money supply diminishes the volatility of Islamic stock returns. Conversely, high inflation and sharp depreciation of the Rupiah boost the stock returns' volatility. The results further show an interesting finding that the Islamic stock market's volatility is more responsive to changes in macroeconomic indicators than the volatility of their counterpart conventional stock market. Policymakers should take strict rules during the worst economic conditions to minimize the negative impact of the instability of macroeconomic variables.
