Stock Market Returns Research Articles

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.

Application of Machine Learning in Stock Market Return Forecasting

With the development of our country's financial market, people pay more attention to the stock market return forecast, which can bring the investors a lot of income and also become a tool of risk management. Machine learning (ML) and artificial intelligence (AI) are becoming increasingly sophisticated and have achieved amazing results in many fields. Machine learning and deep learning algorithm models can process huge amounts of data, from which they can quickly process and analyze laws, many scholars try to apply various machine learning models to the financial field. This paper first expounds the theoretical basis of stock market return prediction, and summarizes the main literature in this field in recent years, from the traditional stock prediction model to the machine learning prediction model. Finally, the traditional stock prediction model ARIMA model and the deep learning LSTM neural network model are selected for comparative empirical research. The sample data are from the Tushare big data community, taking the new energy vehicle stock BYD (002594) as an example. The empirical results show that the prediction accuracy and stability of LSTM neural network are far higher than ARIMA model, and it will have broad application prospects in financial prediction and other fields in the future.

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.

Predicting consumption-wealth ratio changes and stock market returns

We show that the ability of Lettau and Ludvigson’s cay ratio to predict stock market returns could be greatly improved when adjustment is made for variations in expected cay ratio changes. We use the present-value identity of Campbell and Mankiw (1989) to derive the appropriate form of the adjustment and model the adjustment using a handful of macroeconomic variables. We find that the expected cay ratio changes vary in a predictable manner. Using our model to adjust cay improves the annual R̄2 values from 4.42% to 9.55% for in-sample prediction of excess market returns, and the out-of-sample pseudo R2 values increase from -1.88% and -1.72% to 2.38% and 3.69% with the first estimations using information up to 1985 and 1990, respectively. The superior performance also exists in medium-term market return predictions.

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.

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

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.