Market Returns Research Articles

Oil Price Dynamics and Sectoral Indices in India – Pre, Post and during COVID Pandemic: A Comparative Evidence from Wavelet-based Causality and NARDL

Due to the COVID pandemic, the stock market has been affected adversely around the globe and investment decisions are now more challenging and riskier. Hence, in this paper, we aim to investigate the impact of oil prices on the Indian stock market and eight sectoral indices for the period of pre, post, and during the COVID pandemic. The maximal overlap discrete wavelet transform (MODWT) is used to decompose and to denoise the original time series data as oil price and market return are found to be noisy. We employ the wavelet-based Granger causality (WGC) and non-linear, autoregressive distributed lag model (NARDL) to investigate the causality in the frequency domain as well as the short-run and long-run asymmetry of oil price impact. Our analysis shows a feedback relation between low frequency (higher investment horizon) and the long-run asymmetric impact of oil prices on all sectors during all three periods. We discuss the dynamic time-varying relationship between the oil price and sectoral return along with the investment implications in detail.

The Impact of COVID-19 on the Cypriot Stock Market Dynamics

This paper examines the effects of coronavirus disease 2019 (COVID-19) pandemic outbreak on the Cypriot Stock Exchange (CSE), which has encountered substantial turmoil. For this purpose, daily stock market returns were used over the period of September 3, 2019 - July 10, 2020, for the Cypriot economy. The study applied Granger causality models to explore whether the CSE is impacted by the crisis generated by novel coronavirus. Hsiao’s approach to Granger causality was employed to investigate the causalities among COVID-19 and stock market returns, as well as between pandemic measures and several commodities. The analyses uncover intricate dynamics and contributing factors, shedding light on the observed volatility. The findings demonstrate significant volatility throughout the pandemic, with notable shifts in market conditions. Nevertheless, the stock market showcased resilience and recovery during the initial shutdown period. These insights contribute to understanding the pandemic’s impact on the CSE, offering crucial guidance for investors, policymakers, and market participants.

Performance Analysis and Prospects of ESG Exchange-Traded Funds in the Mainland Chinese Market

This article delves into In this article, we conduct a comprehensive analysis of Environmental, Social, and Governance (ESG) Exchange-Traded Funds (ETFs) in the dynamic landscape of the Mainland Chinese market. The research categorizes existing academic perspectives into three distinct groups, evaluating whether ESG ETFs outperform, perform similarly, or underperform conventional ETFs. Employing a meticulous methodology, encompassing annualized volatility, daily average returns, Sharpe ratio, Information ratio, and tracking error, we delve into the nuanced performance metrics of these investment vehicles. The findings reveal a nuanced picture: within an annualized volatility range of 0.6% to 0.8%, ESG ETFs exhibit competitive performance, particularly demonstrating resilience in risk mitigation when market returns are negative. However, our analysis underscores crucial challenges in the current ESG landscape in Mainland China. The lack of clear ESG definitions and standardized evaluation criteria hampers a comprehensive assessment. Additionally, there is a notable disparity in ESG ratings for listed companies, adding complexity to accurate evaluations. Looking ahead, the study emphasizes the imperative of defining ESG standards clearly and establishing uniform evaluation systems and information disclosure mechanisms. These steps are essential to enhance the accuracy and reliability of ESG ETF evaluations, thereby fostering a more robust and stable investment environment. This research provides valuable insights for investors, policymakers, and market participants, illuminating the path toward optimizing the potential of ESG ETFs in the Mainland Chinese market.

The Relationship between Oil Prices and Stock Market: Evidence from Jordan

Objectives: This study aims to investigate the dynamic short- and long-run relationship between oil returns and stock market returns. Methods: Daily data from the Amman Stock Exchange (ASE) spanning the period from 2013 to 2022 is analyzed using the vector autoregression (VAR) model. The VAR model is employed to assess the short-run dynamic impact of oil returns on stock market returns. Granger causality tests are conducted to examine the causal relationship between oil returns and stock market returns. Additionally, the Johansen-Juselius integration test is utilized to investigate long-run cointegration between the two variables. Results: The results from the vector autoregression model (VAR) reveal a statistically significant positive effect of oil returns on the returns of the general index, industry index, and financial index of the ASE. The analysis does not conclusively establish causality between oil prices and stock market returns. However, it identifies highly significant long-run cointegration between oil returns and the returns of all stock market indexes in the ASE. The robustness of these findings is confirmed across different data frequencies and macroeconomic conditions. Conclusions: The study finds that oil prices exert a positive influence on market returns in the Amman Stock Exchange both in the short and long run. These findings hold important implications for academics and investors in Jordan, suggesting potential avenues for further research and informing investment strategies in relation to oil price fluctuations.

Dual-market quantitative trading: The dynamics of liquidity and turnover in financial markets

Financial market liquidity is a popular research topic. Investor-driven research uses the turnover rate to measure liquidity and generally finds that the higher the stock turnover rate, the lower the returns. However, the traditional financial liquidity theory has been impacted by new machine-driven quantitative trading models. To explore high machine-driven liquidity and the impact of high turnover rates on returns, this study establishes a dual-market quantitative trading system, introduces a variational modal decomposition (VMD)-bidirectional gated recurrent unit (BiGRU) model for data prediction, and uses the back-end Hong Kong foreign exchange market to develop a quantitative trading strategy using the same rotating funds in the U.S. and Chinese stock markets. The experimental results show that given a principal amount of 210,000.00 CNY, the final predicted net return is 226,538.30 CNY, a net return of 107.86%, which is 40.6% higher than the net return of a single Chinese market. We conclude that, under machine-driven trading, increasing liquidity and turnover increase returns. This study provides a new perspective on liquidity theory that is useful for future financial market research and quantitative trading practices.

Predicting Analysts’ S and P 500 Earnings Forecast Errors and Stock Market Returns using Macroeconomic Data and Nowcasts

This study scrutinizes the quality of “bottom-up” forecasts of near-term S and P 500 Composite earnings, derived by aggregating analysts’ forecasts for individual firm-level earnings. We examine whether forecasts are broadly consistent with current macroeconomic conditions reflected in economists’ near-term outlook and other available data. To the contrary, we find that a simple macroeconomic model of aggregate S and P 500 earnings, coupled with GDP forecasts from the Blue Chip Survey and recent dollar exchange rate movements, can predict large and statistically significant errors in equity analysts’ bottom-up forecasts for S and P 500 earnings in the current quarter and the quarter ahead. This finding is robust to the requirement that our econometric model is calibrated using only data available at the time of forecast. Moreover, the discrepancy between the macro-model-based earnings forecasts and analysts’ forecasts has predictive power for 3-month-ahead stock returns

Economic Sentiment and Market Return of Indian Stock Market: An ARDL Approach

This study investigates the relationship between economic sentiment and the return of the BSE S&P 500 Index over a decade, employing an autoregressive distributed lag (ARDL) model. Using 12 macroeconomic variables as proxies for economic sentiment, the analysis reveals a high degree of correlation between these proxies and market return. The findings demonstrate that market return is significantly influenced by its own lags, economic corporate premium (ECORPREM), foreign direct investment (FDI), gross domestic product (GDP), inflation rate (INFLAT), prime lending rate (PLR), and short-term interest rate (SHORTINT). Specifically, market return shows both positive and negative correlations with its various lags, while ECORPREM, FDI, GDP, and SHORTINT exhibit significant relationships with market return at different lag intervals. Additionally, INFLAT positively influences market return at specific lags, and PLR is positively correlated with market return in the current period. The study identifies that ECORPREM, FDI, GDP, INFLAT, PLR, and SHORTINT, with -values below 0.20, are statistically relevant in explaining market return. Conversely, variables such as exchange rate (EXRATE), foreign exchange reserves (FEXRES), index of industrial production (IIP), liquidity in the economy (LIQECO), oil prices (OILPRICE), and terms of trade premium (TERMSPRE) do not significantly impact market return, as indicated by their higher -values. These results provide valuable insights for retail investors, policymakers, and other stakeholders in refining their decision-making processes in the Indian stock market. The study also challenges the classical finance theory of investor rationality, suggesting avenues for further research in international contexts.

Monetary Policy and Stock Market Return: The Case of Saudi Arabia Interest Rates hikes and Stocks Returns

The purpose of this research is to examine whether changes in macroeconomic variables have an effect on Saudi Arabia's main index, TASI and whether Monetary policy actions produce positive, negative or no effect at all for all listed companies. Accordingly, we examine the stock price adjustment around announcements of changes in interest rates, we examine the impact of a series of interest rate hikes on the stock returns under the main stock index in the Saudi Stock Market, TASI. The data covers four events between 16 March 2021 to 5 October 2022. We use an event study approach, with an estimation window length of [-250,-11] and an event window length of [-10,7], and we apply the OLS market model for daily data. The significance of the cumulative abnormal return CAAR is tested using the t statistics. The empirical results show that TASI responds negatively to three out of four announcements of interest rate hikes. The positive reaction occurs during the July 2022 monetary action. The positive reaction is attributed to the rising oil prices during that period. Finally, the results of this research open a window for future research to examine the impact of interest rates on the Saudi stock market over the long run.

The Effect of Budget Deficits on Stock Market Returns in Emerging Markets: A Panel VAR Analysis

This study examines the impact of fiscal deficits on stock returns. The sample covers emerging markets for the period 2016Q1-2021Q4. The analysis results using panel vector autoregression (PVAR) and Granger causality tests indicate that fiscal deficits cause stock returns. These results are confirmed for countries with low and medium levels of financial development and for European countries. However, in countries with high levels of financial development such as the BRICS and ASEAN countries, no effect is observed. It is also found that this effect disappears during the COVID-19 pandemic. The results of the study question the strong form of the efficient market hypothesis (EMH). According to the EMH, stock prices should fully reflect all available information. However, the impact of fiscal deficits on stock returns in low- and middle-income countries and European countries suggests that market participants may not fully reflect this information, addressing the existence possibility of markets that are not efficient in the strong form.

Stock market return and merger and acquisition activity in Poland

This article explores mergers and acquisitions trends in the Polish market in a period between 2019 and 2022, focusing on transactions exceeding one million dollars and their correlation with sector-specific stock market returns. The research reveals that the real estate sector, encompassing both development and property management, dominated M&A activity, comprising 25% of transactions in 2022. Significant positive correlations were observed between transaction volumes and average stock market returns, notably in sectors like real estate (R-Pearson = 0.75) and TMT (R-Pearson = 0.87). These findings indicate that sectors with higher returns attracted more M&A activity, reflecting investor confidence and strategic growth opportunities. Despite fluctuations in company valuations post-pandemic, the overall volume of M&A transactions continued to rise, driven by firms with strong cash reserves leveraging lower valuations for strategic expansions. The study anticipates sustained momentum in technology and real estate sectors, alongside emerging opportunities in energy and biotechnology, influenced by global trends and technological advancements. This research contributes valuable insights into the evolving landscape of M&A in Poland, highlighting sectoral attractiveness and strategic implications for investors and businesses.

The Asymmetric Effects of Oil Price Volatility on Stock Returns: Evidence from Ho Chi Minh Stock Exchange

This study is the first to investigate the asymmetric effects of oil price volatility on stock returns for the Ho Chi Minh Stock Exchange (HOSE). We utilized weekly series of VN30-Index, WTI crude oil prices, geopolitical risks (GPR) index, and gold prices spanning from 6 February 2012 to 31 December 2023 as data sources. Using a nonlinear autoregressive distributed lag (NARDL) bounds testing approach, we found that, in the shortterm, oil price volatility has negative asymmetric effects on market returns. Specifically, in the shortterm, a 1 percent increase in oil price volatility immediately leads to a 2.6868 percent decrease in the market returns, while a similar magnitude decrease in oil price volatility is associated with a 6.3180 percent increase in the market returns. In addition, the results obtained from the NARDL model indicated that, in the longterm, the negative and positive changes of oil price volatility have significantly negative effects on the market returns. Finally, the findings derived from the error correction model (ECM) show that a 98.21 percent deviation from the equilibrium level in the previous week is converged and corrected back to the long-term equilibrium in the current week.

Co-movement between COVID-19, oil price and American stock market during 2020: Fresh investigation from partial and multiple wavelet methods

The pandemic has brought economic and social challenges to society, policymakers, financial market investors and firms. The current study aims to shed light on the financial aspects of COVID-19 during the first wave in 2020. In doing so, the study aims to explore the association between COVID-19 cases, oil prices, and the stock market returns (Standard & Poor's). In this paper, the authors use the daily new cases of COVID-19, oil price and stock market return from 21 January 2020 to 10 April 2020. To check the nexus between studied factors, we apply Wavelet Coherence, Partial Wavelet Coherence and Multiple Wavelet Coherence methods. The graphical empirics of Wavelet Coherence Methods mentioned interesting outcomes; (i) COVID-19 and oil price tend to move in the same direction, with COVID-19 leading, (ii) COVID-19 affect stock market returns, and (iii) oil price and stock market returns have strong co-movement.

The market risk premium in Australia: Forward‐looking evidence from the options market

AbstractThis paper analyses forward‐looking estimates of the expected market return in Australian. By utilising option prices, we compute a lower bound for the capital gain and dividend components of the expected return. Over a 17‐year period, the average 1‐month expected return lower bound is found to be 8.6% per annum, compared with an average realised return of 10.9% per annum. Our option‐based estimates demonstrate significant predictive power beyond historical averages and enable direct measurement of the expected return term structure. This approach complements traditional measures of expected returns and offers valuable insights for practitioners, academics, and policymakers in Australia.

The Role Of The Green Economy, Technological Innovation, Stock Market Advantages In Sustainable Development

A green economy to achieve sustainable development provides technological innovation and stock market profits in a sustainable social and environmental economy. To accelerate the transition to an environmentally friendly economy, companies need to understand the impact of implementing environmentally friendly policies and activities on the economy. Results show that green economic growth policies produce future aggregate stock market returns are lower, consistent with the perceived reduction in risk argument by investors. As this research increases our understanding of how the transition to a more sustainable green economy can impact the aggregate profitability of financial markets, implementing a green economy is important to implement appropriate measures to support it sensitively without incurring large costs that adversely impact the health of the economy. Increasing the driving force of technological innovation is the need to comply with environmentally friendly transformation in the economy and realize high-quality economic growth in technological innovation that does not provide support for green economic growth, but over time the impact of technological innovation on economic growth will change to positive. This shows that increasing the ability of technological innovation to encourage environmentally friendly economic efficiency.

Analyzing downside risk of BRICS stock indices: insights from value at risk and time series econometrics

Purpose This paper aims to analyze the downside risk for the stock indices of BRICS countries. The study also aimed to study the interrelationship, directional influence and interdependence among the stock exchanges of BRICS economies to provide insights for policymakers, fund managers, investors and other stakeholders. Design/methodology/approach The authors used Value at Risk (VaR) as an indicator of downside risk and time series econometrics for measuring the long run relationship, directional influence and interdependence. Findings The calculated VaR estimates, long-run linkages and strong interdependence among these indices especially with the returns of Brazil exerting a notable impact on the returns of other BRICS nations. These results emphasize the significance of taking into account cross-country spillover effects and domestic market dynamics in the context of portfolio management and risk assessment strategies. Further, from the extended results of variance decomposition analysis, the authors find that Brazil’s, China’s and South African stock market returns have a significantly lagged impact on their own stock market, while Russia’s and India stock market returns do not have a significantly lagged impact on their own stock markets. Originality/value To the best of the authors’ knowledge, this is the first study comprehensively analyzing the BRICS indices downside risk through the historical simulation method of VaR estimation, which is an unexplored area of risk management.

The Korean Spotlight: The Effect of Culture on Stock Returns Through Investor Attention*

AbstractThis study empirically examines the relationship between “investor attention” and aggregate stock returns, seeking to determine whether interest in South Korea can predict the returns of the Korean stock market. The results confirm a positive relationship between interest in Korea and future stock market returns. Furthermore, this relationship is observed to have emerged with the widespread dissemination of Korean culture starting in 2017. This impact is found to be more significant on the KOSDAQ market compared to the KOSPI market. Within the KOSPI market, the influence of interest on stock returns is particularly pronounced in the case of small‐cap stocks. It is also observed that since 2017, as interest in South Korea has increased, information related to the improvement of Korea's corruption index has begun to significantly affect stock returns.

Partisan conflict, trade policy uncertainty, and the energy market

This paper investigates the time-varying effects of partisan conflict (PC) and trade policy uncertainty (TPU) on the energy market from January 2000 to July 2021 by employing a time-varying parameter vector autoregressive model with stochastic volatility. The results show that the pass-through effects of the PC and TPU shocks on energy return and volatility varied over time and are different across energy sub-sectors. PC and TPU had a greater impact on energy market return in the short-term, whereas the impact on energy market volatility was spread across relatively long periods. Generally, the impact of TPU on energy return and volatility was greater than that of PC at different lag periods and different points. Further, under different energy frameworks, the trend of the time-varying relationship between PC and TPU was roughly the same, indicating the robustness of our results.

Safe haven properties of industrial stocks against ESG in the United States: Portfolio implication for sustainable investments

This study explores the safe haven role of industry sectors classified by the Global Industry Classification Standards (GICS) for global Environmental, Social, and Governance (ESG) stocks in the United States. Our study applies the novel cross-quantilogram and quantile time-frequency connectedness approach for daily time series data from November 2019 to October 2023. Our findings confirm that industry sectors, mainly financials, industrial, communication, information technology, energy, consumer staples, and consumer discretionary, are the safe haven avenues at down, normal, and up market conditions; however, these safe haven avenues vanish for the long-term investment horizon. We also explore the risk spillover patterns for the total time domain and the decomposed spillover level for the short- and long-term spillovers. Our connectedness finding shows that the net transmitters and net receiver patterns are similar for the extreme markets but dramatically change for the normal markets. Finally, based on the portfolio rebalancing strategy, the returns of our portfolio, comprised of industry sectors and the ESG index, outperform the overall market. Moreover, our portfolio yields lower negative returns and a higher Sharpe ratio when compared with the overall market returns. These findings confirm the benefits of diversification by reducing downside portfolio risk. This study is primarily relevant for socially responsible portfolio and risk management investors.

Investor sentiment and the holiday effect in the cryptocurrency market: evidence from China

This study employs a fixed-effects model to investigate the holiday effect in the cryptocurrency market, using trading data for the top 100 cryptocurrencies by market capitalization on Coinmarketcap.com from January 1, 2017 to July 1, 2022. The results indicate that returns on cryptocurrencies increase significantly during Chinese holiday periods. Additionally, we use textual analysis to construct an investor sentiment indicator and find that positive investor sentiment boosts cryptocurrency market returns. However, when positive investor sentiment prevails in the cryptocurrency market, the holiday effect weakens, implying that positive investor sentiment attenuates the holiday effect. Robustness tests based on the Bitcoin market generate consistent results. Moreover, this study explores the mechanisms underlying the cryptocurrency holiday effect and examines the impact of epidemic transmission risk and heterogeneity characteristics on this phenomenon. These findings offer novel insights into the impact of Chinese statutory holidays on the cryptocurrency market and illuminate the role of investor sentiment in this market.

Weryfikacja modelu wyceny kapitału CAPM – na przykładzie indeksów giełdowych krajów Europy Środkowo-Wschodniej

The stock exchanges of CEE countries constitute a minor part of the capital of the entire Europe, but they are dynamically developing and cooperating capital markets. After 30 years of development, it seems reasonable whether the processes of asset pricing in them are aimed at the efficiency level of developed markets and are subject to the postulates of pricing models, such as CAPM. The work aims to test the standard CAPM using the CEE country indices. Apart from the classical approach, conditional regressions in relation to the global market condition are an important contribution to the research. The obtained results indicate that the significance of the risk premium, largely depends on the sign of the market returns, showing the advantage of conditional relations over unconditional ones. The research analyzed the sensitivity of the results to the existing socio-political crises and assessed the risk of selected stock exchanges.