Stock Market Returns Research Articles

Spillover Effects between Oil, Gold, Stock, and Exchange Rate Returns in Thailand: An Extended Joint Connected TVP-VAR Approach

The objective of this study is to analyze the spillover effects among the returns of oil, gold, the stock market, and exchange rates in Thailand. Using the time-varying parameter vector autoregression model (TVP-VAR) with extended joint connectedness and data from April 2002 to March 2024, our analysis reveals a moderate level of the dynamic linkage among these returns. We observe that the dynamic connectedness among all returns varies over time, influenced by global economic events and country situations. Notably, in Thailand's landscape, stock market and gold returns act as net shock transmitters, with the stock market exhibiting the highest volatility among all variables, while oil and exchange rate markets function as net recipients. These insights significantly contribute to understanding asset and commodity markets and offer valuable policy implications for effectively managing these markets.

The Impact of COVID-19 Pandemic on the Jordanian Stock Market Returns Volatility: Evidence from ASE20

This research examines the impact of the COVID-19 pandemic on the volatility behavior of Amman Stock Exchange (ASE) returns using ARMA–GARCH-type models for three sub-periods: pre-COVID-19, during COVID-19, and post-COVID-19. The research finds that volatility persistence is significant across all periods, with the pandemic period showing the highest impact of shocks. Bad news has no statistically significant impact on volatility in the pre-COVID-19 period or during the pandemic, while in the post-pandemic period, good news significantly influences volatility. Additionally, there exist notable changes in the autocorrelation and the shock structure of the AR and MA components. Considering these alterations in the asymmetric effects, the AR and MA components suggest significant shifts in market dynamics, investor sentiments, and economic policies in response to pandemic experiences.

Stock market responses to unconventional monetary policy shocks

We assess the impact of forward guidance and large-scale asset purchases (LSAPs) on stock market returns, finding that LSAP shocks during unconventional periods affect stock returns in a manner similar to federal funds rate (FFR) shocks during conventional periods.

Do Domestic Climate Rulings Make Climate Commitments More Credible? Evidence from Stock Market Returns

Do Domestic Climate Rulings Make Climate Commitments More Credible? Evidence from Stock Market Returns

Does the U.S. extreme indicator matter in stock markets? International evidence

We propose a new predictor—the innovation in the daily return minimum in the U.S. stock market (ΔMINUS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta {MIN}^{US}$$\\end{document})—for predicting international stock market returns. Using monthly data for a wide range of 17 MSCI international stock markets during the period spanning over half a century from January 1972 to July 2022, we find that ΔMINUS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta {MIN}^{US}$$\\end{document} have strong predictive power for returns in most international stock markets:ΔMINUS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta {MIN}^{US}$$\\end{document} negatively predicts the next-month stock market returns. The results remain robust after controlling for a number of macroeconomic predictors and conducting subsample and panel data analyses, indicating that ΔMINUS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta {MIN}^{US}$$\\end{document} has significant predictive power and it outperforms other variables in international markets. Notably, ΔMINUS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta {MIN}^{US}$$\\end{document} demonstrates excellent predictive power even during the periods driven by financial upheavals (e.g., Global Financial Crisis and European Sovereign Debt Crisis). Both panel regressions and out-of-sample tests also support the robust predictive performance of ΔMINUS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta {MIN}^{US}$$\\end{document}. The predictive power, however, disappears during the non-financial crisis caused by COVID-19 pandemic, which is originated from the health sector rather than the financial sector. The results provide a new perspective on U.S. extreme indicator in stock market return predictability.

Research on Impact of Investor Sentiment on Chinese Stock Market: Review and Prospect

Over time, investor behavior has become more complicated due to the unpredictability of the Chinese stock market. Major research has found that investor sentiment swings have a huge impact on the market. Therefore, investors need to observe and understand how investor sentiment affects the market in the right way. Much of the past literature has used textual data mining and event analysis to measure the impact of investor sentiment and its impact on stock market returns, pricing, and volatility. Several articles have concluded that investor sentiment is highly responsive to the Chinese stock market. Another phenomenon that has emerged on various forums and social media is that people's fear of the stock market is spreading. The results of event analysis can prove that the impact of events such as the lifting of the ban on restricted shares and the listing of companies will be very obvious and have an impact on investor sentiment. An important implication of this study is that in addition to the dominant role of Granger causality in the causality that produces negative returns, negative investor sentiment is the main factor determining the overall behavior of the stock market. When bad news breaks, investors release their pent-up emotions for a short period, resulting in a violent reaction. In addition, market sentiment can also affect trading volumes and stock prices. This has put pressure on the market. In the end, this paper expands the future research direction from the two aspects of communication mechanism and quantitative analysis.

Trump’s fake news and stock market returns

We use a novel database that identifies allegedly Donald Trump’s fake news during his presidency. We find that the number of daily fake news is positively related to contemporaneous US stock market returns. Fake news is typically positively biased in our context, increasing stock returns in the short term. We invalidate alternate explanations of the main relation, such as the notion that newly arrived information drives the relation. The mechanism of the relationship is the source used and the reliability of the fake news. Fake news matters to the extent that participants believe it is true. This positive relation reverses over the following days, indicating some evidence of correction. Overall, we find that a politician’s fake news influences financial markets temporarily.

Global power and Stock market co-movements: A study of G20 markets

It is well understood that a handful of countries such as members of G7 and G20 influence the direction of the trade policy of nations across the world. Such influence places significant pressure on other countries to adapt their own policies to suit G7 and G20 as these two groups of countries control international agencies such as the World Bank and the United Nations. This influence is also evident from the deliberations of G7 and G20 member countries and how such more powerful nations seem to shape and influence the global economic agenda. This study aims to investigate the relationship between global power (as measured by the global power index GPI) and globalisation (as measured by the co-movements of stock markets time varying correlations). Previous studies have investigated various factors influencing stock market correlations; however, the relationship between GPI and stock market correlations has not been addressed thus far. To investigate this relationship, we created an index of correlations of each stock market with other stock markets in G20 countries. Our empirical results indicate that GPI has a positive and statistically significant impact on the stock market correlations in G20 nations. This is the first study to establish such relationship between GPI and change in relative stock market performance. In the past changes in relative stock market returns were mainly attributed to the economic factors and relative volatility of the underlying stock markets. As such this study makes an important contribution to body of knowledge by developing a theoretical argument to show how change in relative global power, influences changes in stock market correlations via changes in relative risk premium and returns. The findings of the study have implications for the development of global policies as global power influences stock market co-movements. The findings of this study may also have implications for investors who aim to construct globally diversified portfolios.

Heterogeneous stock market impact of Russia–Ukraine War for oil and gas companies

Purpose This paper aims to examine the short-term market impact of the beginning of the military conflict between Russia and Ukraine (24 February 2022), the world’s largest oil and gas companies. Design/methodology/approach The authors examine the world’s 100 largest listed oil and gas companies at and around the beginning of the military conflict between Russia and Ukraine using an event study methodology. Findings The authors observe a positive and statistically significant stock price reaction at and around the military conflict. These results are consistent with the asset pricing perspective. Conversely, the stock market returns of Russian oil and gas companies, as well as those companies that were “forced” to divest in Russia due to corporate activism, exhibit a negative and statistically significant impact from the conflict. These reactions are reinforced or mitigated by company-specific characteristics such as size, profitability and institutional ownership. Finally, the findings indicate that companies engaged in oil and gas exploration and production report abnormally higher returns compared to firms in the other two subsectors of the industry. Originality/value The effect of the war on stock markets has been relatively little examined in the financial theory. This study intends to fill this gap in the literature.

Stock Returns And Inflation In Fiji

The relationship between stock returns and inflation has important implications for monetary policy and stock market investment and has attracted growing attention from scholars over recent years. Despite this, the empirical evidence so far has been inconclusive. The main objective of this paper is to investigate the impact of inflation on stock returns in the developing stock market of Fiji. We examine the impact of inflation on stock market returns within the GARCH and EGARCH modelling framework using monthly data from 2000:02 to 2018:06 and find that inflation negatively affects stock market returns. Our results indicate the importance of ensuring price stability and suggest that stock market investment will not help hedge against inflation in Fiji. The results suggest that Fiji’s stock market is likely to react more negatively to inflation in response to countercyclical monetary policy and emphasize the significance of portfolio diversification.

Research on the Dynamic Interrelationship between Economic Policy Uncertainty and Stock Market Returns

This paper employs the Panel Vector Autoregression (PVAR) method to examine the dynamic interrelationship between Economic Policy Uncertainty (EPU) and stock market returns. The existing literature has not reached a consensus on the relationship between EPU and stock market returns, and there is a lack of comparative analysis of domestic and foreign EPU. Therefore, this paper is the first to incorporate domestic and foreign EPU, stock market returns, and output into a unified framework, considering the dual impact of domestic and foreign EPU shocks. Additionally, the generalizability of the results is ensured by including a large sample of nine emerging and eleven advanced economies. The main findings are as follows: First, a positive shock to foreign EPU leads to a decline in stock market returns and is stronger than the impact of domestic EPU. Second, a positive shock to stock market returns reduces both domestic and foreign EPU. Third, a rise in stock market returns promotes domestic output growth, while increases in domestic and foreign EPU suppress domestic output growth. Finally, the United States is a net exporter of EPU rather than a net importer.

Business News and Business Cycles

ABSTRACTWe propose an approach to measuring the state of the economy via textual analysis of business news. From the full text of 800,000 Wall Street Journal articles for 1984 to 2017, we estimate a topic model that summarizes business news into interpretable topical themes and quantifies the proportion of news attention allocated to each theme over time. News attention closely tracks a wide range of economic activities and can forecast aggregate stock market returns. A text‐augmented vector autoregression demonstrates the large incremental role of news text in forecasting macroeconomic dynamics. We retrieve the narratives that underlie these improvements in market and business cycle forecasts.

A novel trading system for the stock market using Deep Q-Network action and instance selection

Stock trading is a complex decision-making process that involves predicting market price movements. Many investors attempt to buy at low prices and sell at high prices, which can be difficult due to numerous factors that can influence stock prices. Consequently, researchers have developed trading systems designed to generate high stock market returns using machine learning or deep learning techniques. However, building a trading system using supervised learning can be challenging in real-time due to noisy stock price data. Reinforcement learning trading systems can play an active role in the stock market and their performance depends heavily on how the learning environment is constructed. Therefore, this study proposes a Deep Q-Network (DQN) Action Instance Selection Trading System (DAIS) to improve the limitations of both supervised learning and reinforcement learning trading systems. DAIS learns effective trading timing by labeling the behavior of DQN models and using instance selection to effectively handle noise in stock price data. Our proposed model is evaluated by testing 72 companies listed on Korean Composite Stock Price Indexes (KOSPI) and comparing the results with those obtained in previous studies using supervised and reinforcement learning trading systems. The results indicate that the DAIS trading system is more efficient than the compared systems.

Assessing the extent of exchange rate risk pricing in equity markets: emerging versus developed economies

PurposeThis paper contributes to the literature on exchange rate exposure by assessing the extent to which exchange rate risk is priced in both African emerging and developed equity markets. It examines whether this risk leads to a premium or discount in market returns. The study uses the United States and South Africa as representatives for developed and emerging economies, respectively.Design/methodology/approachThe paper employs two-factor and three-factor conditional CAPM approaches with a two-stage estimation process. In the first stage, time-varying risk exposures are derived using the ICAPM model estimated through rolling regression. In the second stage, the impact of these risk exposures, particularly exchange rate risk exposure, is assessed on stock market returns using Generalized Linear Model (GLM) regression.FindingsUnlike previous studies that suggest exchange rate risk is not necessarily priced in the equity market due to hedging, this paper finds that exchange rate risk is indeed priced in both African and developed equity markets, albeit to different extents. The African equity market demands a higher premium compared to the developed equity market.Practical implicationsThe findings of this paper have significant implications for policymakers, asset managers, and investors. They provide insights for making more informed decisions, implementing effective risk management strategies, and fostering a more stable and appealing investment environment.Originality/valueTo the best of our knowledge, this is the first study to evaluate the degree of exchange rate exposure in environments characterized by high currency volatility versus those with low volatility, all within the context of the conditional ICAPM model.

Impact of firm characteristics and country-level governance on global energy stocks during crises

Two successive crises have raised the need to understand the dynamics of the global energy sector during these crises. This study examines the effects of the Russia-Ukraine War (RUW) and the COVID-19 pandemic on the energy sector across developed and emerging markets. Additionally, it examines the role of firm- and country-specific variables in stimulating energy stocks. We employ a rigorous event study methodology to analyse data from 387 firms spanning 44 countries and reveal that the RUW exerted a more significant global impact on the energy sector compared to the pandemic, both before and after the event day. Notably, emerging markets displayed early reactions before the global announcement of the COVID-19 pandemic. Remarkably, the American markets were hardest hit by the pandemic, while Asia and the Pacific, Europe, and the Middle East and Africa fared relatively better. Intriguingly, our cross-sectional analysis demonstrates that firm-specific characteristics (leverage and firm size) and the country-level governance variable, corruption control, influenced market performance during these events. Conversely, country-level development variables demonstrated minimal impact. This study enhances the understanding of how global crises uniquely impact the global energy sector. It also demonstrates how country-level governance variables could influence stock market returns.

Asymmetric Effects of COVID-19 Deaths and Oil Price Shocks on Stock Market Returns in Some Selected African Countries: Evidence From Nonlinear ARDL Approach

This study investigates the asymmetric impact of oil price shocks and COVID-19 total deaths on Kenya, Morocco, Nigeria and South Africa stock market returns. To achieve this objective employing weekly time series data from March 13, 2020 to September 23, 2022, first, we apply the BDS test which confirm the nonlinearity of each series. Second, stationarity tests are used to investigate each series for unit root. The results show that series are stationary in level and in first difference. Third, the bounds test reveals that the series have a cointegration relationship and we apply the nonlinear autoregressive distributed lag (ARDL) ARDL framework to decompose oil price into positive and negative partial sum to investigate a possible asymmetric effect of oil price on stock market returns. The main findings of the asymmetric ARDL model reveal that, in the short-run dynamic, a positive shock in the oil price have a negative and positive effect on stock market returns in Morocco and South Africa respectively. The result is insignificant for Kenya and Nigeria. A negative shock in the oil price have a negative effect on the stock returns in Nigeria, South Africa and in Kenya, Morocco with one period lag. Furthermore, in the long-run, a positive shock in oil price exerts a negative effect on the stock market performance in Morocco and Nigeria while a negative shock in oil price leads to a negative effect also on the stock market returns. The total deaths present a counterintuitive result. Both in the long-run and short-run dynamic, the total number of deaths due to COVID-19 have a significant increasing effect on stock market returns in Kenya, Morocco and South Africa except for Nigeria.

Informed Trading and Return Predictability in China: Research Based on Ensemble Neural Network

ABSTRACT We construct a new informed trading index based on the high-frequency trading data of the Chinese A-share market using the ensemble neural network algorithm. We find that the informed trading index is a strong negative predictor of future aggregate stock market returns, with monthly in-sample and out-of-sample R 2 of 5.45% and 3.53%, respectively, which is far greater than the predictive power of other previously studied informed trading indicators and macroeconomic variables. The asset allocation strategy based on our index can generate large economic gains for the mean-variance investors, with annualized CER (certain equivalent return) gains ranging from 10.91% to 7.80% as the investor’s risk appetite varies. The driving force of the predictive power appears to stem from the liquidity provider role that informed traders play, which decreases the market’s illiquidity risk and lowers the risk premium of equity. Our analysis complements the returns predictability study by adding a new predictor on the one hand and informs market timing strategies on the other.

Analysis of Istanbul Stock Market Returns Volatility with ARCH and GARCH Models

Analysis of Istanbul Stock Market Returns Volatility with ARCH and GARCH Models

High frequency monitoring of credit creation: A new tool for central banks in emerging market economies

This study utilizes weekly datasets on loan growth in Colombia to develop a daily indicator of credit expansion using a two-step machine learning approach. Initially, employing Random Forests (RF), missing data in the raw credit indicator is filled using high frequency indicators like spreads, interest rates, and stock market returns. Subsequently, Quantile Random Forest identifies periods of excessive credit creation, particularly focusing on growth quantiles above 95 %, indicative of potential financial instability. Unlike previous studies, this research combines machine learning with mixed frequency analysis to create a versatile early warning instrument for identifying instances of excessive credit growth in emerging market economies. This methodology, with its ability to handle nonlinear relationships and accommodate diverse scenarios, offers significant value to central bankers and macroprudential authorities in safeguarding financial stability.

A Study of the Impact of Investor Sentiment on Stock Investment Returns

This study examines the influence of investor sentiment on stock investment returns in China's rapidly developing yet volatile securities market. Utilizing principal component analysis, a comprehensive investor sentiment index is constructed from six key indicators. The dynamic relationship between investor sentiment and stock market return volatility is explored through a Vector Autoregression (VAR) model and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The findings reveal a significant bidirectional correlation, with positive optimism exerting a greater impact on stock returns than negative pessimism. The findings of this study indicate that investor sentiment exhibits a heightened sensitivity to declining returns as opposed to rising ones, offering valuable insights for both investors and policymakers.