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We document and explain the sharp performance deterioration of smart beta indexes after the corresponding exchange-traded funds (ETFs) are launched for investment. While smart beta is purported to deliver excess returns through factor exposures, the market-adjusted return of smart beta indexes drops from about 3% "on paper" before ETF listings to about −0.50% to −1% after ETF listings. This performance decline cannot be explained by variation in factor premia, strategic timing, or diminishing returns to scale. Instead, we find strong evidence of data mining in the construction of smart beta indexes, which helps ETFs attract flows, as investors respond positively to backtests. [ABSTRACT FROM AUTHOR]

Celebrity endorsement is a common advertising strategy, yet, as well-known scandals show, it is not without risk. Studies at the marketing–finance interface investigate how negative publicity surrounding a celebrity endorser affects firm value, though without determining how such events might spill over to the sponsor firms' competitors and their stock prices. To address this research gap, the authors assess the impact of celebrity endorser scandals on competitor stock returns with an event study approach. The unique sample of 121 celebrity scandals over a 35-year period reveals a contagion effect, such that competitor firms experience negative stock returns on average, though not to the same extent. According to univariate and regression analyses, the more negative the event affects the sponsor company and the more homogeneous the industry, the stronger the negative spillover effect from a scandal. These findings show that a contagion effect is a likely scenario and offer recommendations for managers regarding how they should adapt their risk management processes and communicate with their boards and shareholders. [ABSTRACT FROM AUTHOR]

Using the exclusion of business development companies (BDCs) from stock indexes, this paper studies the effectiveness of market discipline in the direct lending space. Amid share sell-offs by institutional investors, a drop in BDCs' valuations limits their ability to raise new equity capital. Following this funding shock, BDCs do not adjust their capital structure. At the same time, they are reducing the risk exposure of their portfolios. We document a greater reduction in risk for BDCs subject to stronger market discipline from their debtholders. BDCs pass through the capital shock to their portfolio firms by reducing their investment intensity. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to analyze the connectedness between the natural gas, wheat, gold, Bitcoin and Gulf Cooperation Council (GCC) stock indices with the advent of exogenous and unexpected shocks related to the health and political crises. Design/methodology/approach: For this end, a quantile-based connectedness method is applied on returns of different assets during the period 01/01/2016–05/01/2024. Findings: The empirical findings display that the existence of time-varying connectedness between markets is well-documented and seems to be stronger during the COVID-19 pandemic and the Russia–Ukraine war. The connectedness is fostered with extreme events, showing that shocks propagate increasingly during turbulent periods compared with calm ones. The connectedness is event-dependent. Practical implications: The empirical results offer insightful information for policymakers and investors about the contagion effect and volatility spillover among GCC stock markets and other asset classes during different crises. Originality/value: This study examines different asset classes' dynamism connection with sock prices in the GCC countries to better apprehend the (dis)similarities between different asset classes in terms of information transmission. It also investigates the connectedness structure among different asset classes under extreme market conditions and how spillover effects across GCC markets and other ones can be time- and event-dependent. [ABSTRACT FROM AUTHOR]

Despite the significance of materiality, its implementation has been perceived as ambiguous, opaque, and heterogeneous. This study focuses on analyzing materiality reporting among companies that utilize GRI and are listed on the Spanish stock exchange between 2018 and 2021. First, it examines the disclosure of materiality analysis by introducing a materiality disclosure assessment index (MDA) and exploring its determinants. Additionally, it investigates the alignment of material topics with GRI Topic Standards, analyzing the cross‐cutting nature of material topics. Our results indicate that MDA reached slightly over half of its maximum value. We found evidence that MDA is positively associated with membership in the IBEX 35, as well as with five out of the seven industries comprising the stock index. Furthermore, the years of disclosure experience in two industries significantly influenced MDA. Companies considered 20% of material topics as cross‐cutting, while another 20% had no direct correspondence with GRI standards. This study builds upon previous empirical findings on GRI materiality disclosure and provides valuable insights for research, practice, and standard setting. [ABSTRACT FROM AUTHOR]

Stock enhancement activities have many benefits but also negative impacts. The macrobenthic diversity index and the M-AMBI index were adopted to assess the effects of stock enhancement of Phascolosoma esculenta on macrobenthic communities and the local ecosystem in Yueqing Bay. The results revealed that the Shannon--Wiener diversity index (H') and Margalef richness index (d) increased in October 2022 after stock enhancement, indicating a positive ecological restoration effect. The average M-AMBI in the intertidal zone of Yueqing Bay was 0.59, indicating good benthic ecological quality. Excluding seasonal factors, the M-AMBI in the intertidal zone increased after stock enhancement, indicating that stock enhancement by Phascolosoma esculenta had a certain positive effect on the intertidal ecological environment of Yueqing Bay. The increase in the M-AMBI occurred mainly in the low-tide zone, increased slightly in the mid-tide zone, and remained basically unchanged in the high-tide zone. On the one hand, Phascolosoma esculenta can accumulate heavy metals from the environment and release a large amount of organic matter throughout its lifecycle, promoting nutrient cycling, which plays a positive role in the environment. On the other hand, Phascolosoma esculenta is an economic species; however, the planting of mangroves in the low tide zone causes fishers to fish in the mid- and high-tide zones, so the changes in the M-AMBI values in each tidal zone are related mainly to Phascolosoma esculenta and anthropogenic disturbances. [ABSTRACT FROM AUTHOR]

In this paper, we introduce detrended cross-correlation analysis (DCCA) method into visibility graph (VG) algorithm and propose VGDCCA method to reflect the time series irreversibility from a new perspective. The validity and reliability of the proposed VGDCCA method is supported by numerical simulations on synthesized short-term correlated chaotic systems and long-term correlated fractal processes, and by the empirical analysis on stock indices and traffic parameter. The VGDCCA planes suggest that autoregressive fractionally integrated moving average (ARFIMA) and fractional Gaussian noise (FGN) series show time reversible behavior, whatever the long-term correlation of the series is or however strong the persistence or anti-persistence of the series is. Meanwhile, Logistic map with a=3.3∼3.6 and Hénon map show more time irreversible behaviors than those of ARFIMA, FGN series and other Logistic maps. It can be found that the relationship between cross-correlation of ingoing degree sequence and outgoing degree sequence for the simulated series with different parameters is consistent with the complexity and autocorrelation behavior in the corresponding definition of the time series. For the empirical analysis, VGDCCA method declares the similarity and dissimilarity between stock indices on time series irreversibility and captures the time irreversibility of traffic time series recorded by the detectors in different locations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach to improving forecasts of stock market indexes by considering common stock prices as hierarchical time series, combining clustering with forecast reconciliation. We propose grouping the individual stock price series in various ways including via metadata and using unsupervised learning techniques. The proposed approach is applied to the Dow Jones Industrial Average Index and the Standard & Poor 500 Index and their component stocks, and the results obtained with different grouping approaches are compared. The results empirically demonstrate that the combined use of clustering and reconciliation improves the forecast accuracy of the stock market indexes and their constituents. [ABSTRACT FROM AUTHOR]

In this article, we consider the so‐called Fixed Shrinkage (FS) bootstrap for the class of GARCH models with explanatory variables (GARCH‐X). Under the assumption of stationary covariates, the proposed FS bootstrap does not require modeling the covariates, as these are kept fixed in the bootstrap generating process. Our main focus is on testing whether one or more of the covariates can be excluded in the GARCH‐X model. As is well‐known the limiting distribution of the likelihood‐ratio (LR) statistic in this setting is non‐standard and depends in particular on whether nuisance parameters are on the boundary or in the interior of the parameter space. In particular, and as detailed here, the non‐standard limiting distribution depends on correlations, or dependence, between the explanatory variables. The FS bootstrap takes the presence of nuisance parameters into account by implementing shrinking as proposed in Cavaliere et al. (2022) for pure ARCH models. We establish asymptotic validity of the FS bootstrap for GARCH‐X models, and demonstrate by simulations that the bootstrap‐based test performs extremely well even when nuisance parameters lie on the boundary of the parameter space. The empirical illustration amplifies that the presence of nuisance parameters (especially whether or not on the boundary) are vital for interpreting the dynamics of conditional volatility in financial stock market indices. [ABSTRACT FROM AUTHOR]

The realized recurrent conditional heteroscedasticity (RealRECH) model improves volatility prediction by integrating long short-term memory (LSTM), a recurrent neural network unit, into the realized generalized autoregressive conditional heteroskedasticity (RealGARCH) model. However, at present, there is no literature on the ability of the RealRECH model to fit and predict volatility in the Chinese market. In this paper, a study is conducted to test the in-sample explainability and out-of-sample prediction ability of the RealRECH model for the SSE50, CSI300, CSI500 and CSI1000 indices in the Chinese market and to determine whether it performs better than the RealGARCH model. The results of the in-sample analysis show that the RealRECH model not only provides better in-sample interpretability for all four indices but also captures the complex dynamics of time series volatility that the RealGARCH model cannot capture, such as long-term dependence and nonlinearity. The results of out-of-sample volatility prediction show that the RealRECH model better predicts the volatility of the CSI500 and CSI1000 indices but yields worse predictions for the SSE50 and CSI300 indices. Thus, the RealRECH model can be used for CSI500 and CSI1000 prediction. [ABSTRACT FROM AUTHOR]

This research paper analyzes how the pandemic caused by the coronavirus has affected eight MENA and three OIC stock market indices using advanced methodologies based on fractional integration. With this methodology we try to measure the persistence of the financial data in order to understand the long-term market dynamics of these two regions and to identify whether the COVID-19 shock has caused structural changes in the time series. Moreover, we want to identify if the shocks in the series have had permanent or transitory effects. The time period examined starts from pre-pandemic times at 2003, and first goes to the end of 2019; then, we also consider the COVID-19 pandemic (with data ending at December 2021) and then extend the analysis onwards to include the war between Russia and Ukraine (data ending at February 2023). The results indicate that there have not been significant long-lasting impacts from the COVID-19 pandemic or the Russia-Ukraine war on the degree of persistence in the series when using monthly data; however, as far as daily and weekly data are concerned the shocks have had some effect on the degree of persistence with daily data in the cases of Egypt, Indonesia, Malaysia, Turkey, and with weekly data for Bahrain, Egypt, Kuwait, Malaysia, Morocco and Turkey. [ABSTRACT FROM AUTHOR]

The prediction of stock market fluctuations is crucial for decision-making in various financial fields. Deep learning algorithms have demonstrated outstanding performance in stock market index prediction. Recent research has also highlighted the potential of the Transformer model in enhancing prediction accuracy. However, the Transformer faces challenges in multi-step stock market forecasting, including limitations in inference speed for long sequence prediction and the inadequacy of traditional loss functions to capture the characteristics of noisy, nonlinear stock history data. To address these issues, we introduce an innovative transformer-based model with generative decoding and a hybrid loss function, named "Galformer," tailored for the multi-step prediction of stock market indices. Galformer possesses two distinctive characteristics: (1) a novel generative style decoder that predicts long time-series sequences in a single forward operation, significantly boosting the speed of predicting long sequences; (2) a novel loss function that combines quantitative error and trend accuracy of the predicted results, providing feedback and optimizing the transformer-based model. Experimental results on four typical stock market indices, namely the CSI 300 Index, S&P 500 Index, Dow Jones Industrial Average Index (DJI), and Nasdaq Composite Index (IXIC), affirm that Galformer outperforms other classical methods, effectively optimizing the Transformer model for stock market prediction. [ABSTRACT FROM AUTHOR]

Foreign investors' transaction volume in the Tokyo Stock Exchange has grown steadily in the past three decades, and Japanese stocks have become a familiar instrument for international diversification. Rebalancing trades in a local national market by foreign institutional investors holding multi-country asset portfolios, often heavily represented by U.S. stocks, amplify the national market's stock index co-movements with the U.S. This paper examines the extent to which greater participation of foreign investors is associated with increases in the unconditional correlation of daily returns between the U.S. (S&P 500) and Japan (TOPIX). A Gaussian copula marginal regression model is applied to the monthly series of unconditional correlations. We find that the growth in foreign investors' transaction share of the Tokyo market's turnover approximately accounts for the rise in U.S.-Japan correlations for 1990–2019. Foreign investors' trades improve the informational efficiency of local national markets. Increased correlations lead to smaller potential gains from international diversification, however. [ABSTRACT FROM AUTHOR]

This study investigates the CAPE Ratio values of stock issuers in the LQ-45 Index compared to those in the ESG Leaders Index, employing a quantitative approach to assess whether the ESG Leaders Index yields higher returns. The findings reveal that most issuers in both indices are undervalued relative to the LQ-45 Index, indicating potential for future returns. However, the LQ-45 Index has a greater number of overvalued issuers than the ESG Leaders Index. Notably, the study acknowledges limitations, including a lack of exploration into the relationship between the CAPE Ratio and non-financial factors such as ESG scores, and a narrow focus on Indonesian indices without comparative analysis with foreign indices. Future research is recommended to explore these relationships, apply the CAPE Ratio across specific sectors, and compare Indonesia's ESG Index with those in Southeast Asia, thereby enriching understanding of valuation dynamics in relation to ESG factors. [ABSTRACT FROM AUTHOR]

This study investigates the influence of Dividend Yield on Stock Price Volatility in companies included in the IDX30 index on the Indonesia Stock Exchange (IDX) during the 2019-2023 period. Utilizing secondary data from annual reports of IDX30 companies, panel data regression analysis was conducted to examine the relationship between the variables. The findings reveal that Dividend Yield positively affects Stock Price Volatility, contradicting the initial hypothesis. The results indicate that an increase in Dividend Yield leads to higher Stock Price Volatility, while a decrease in Dividend Yield results in lower Stock Price Volatility. This research provides valuable insights for investors in understanding the dynamics between dividend policies and stock price fluctuations. [ABSTRACT FROM AUTHOR]

In this research, we are concerned with intraday volatilities computed by functional ARCH(1) (fARCH(1), for short) model for high-frequency financial time series. A conditional-Copula multiple change point detection (CPD) for intraday volatilities is proposed using fARCH(1), bivariate Gaussian Copula and t-Copula conditional distributions. We employ current available multivariate CPD models which include energy test based control chart (ETCC) and nonparametric multivariate change point model (NPMVCP) to implement the proposed CPD method for the intraday volatilities. A simulation study is conducted to demonstrate that the functional ARCH based conditional-Copula CPD for the intraday volatilities can be a useful econometrics method to detect abnormal intraday volatilities in the financial market. We analyze intraday volatilities of the Korea composite stock price index (KOSPI) and the Hyundai-Motor (HDM) company stock data with one minute high-frequency to illustrate our proposed CPD method. [ABSTRACT FROM AUTHOR]

The prediction of price trends in the stock market has always been a hot research topic in the financial field. However, due to the high instability and volatility of stock prices, it is very difficult to accurately predict stock trends. How to remove the noise of stock data, extract effective features, and pursue maximum value returns has always been a challenge. This paper proposes a hybrid model (DWT-DQN) that combines discrete wavelet transform with deep reinforcement learning to improve the accuracy and return rate of stock predictions. First, the model captures price fluctuation information on different scales by performing discrete wavelet transformation on the difference between long- and short-term moving averages of stock prices, and well extracts the changing characteristics of stock price data in the time domain and frequency domain. Then the feature data are input into the built DQN network for model training. The network can select the optimal trading action based on market status and historical experience and returns. At the same time, during the data sampling process, an attention mechanism is introduced to allow the model to further learn in the direction of maximizing returns. Through testing and verification on SSEC, HSI, NDX and SPX data sets, experiments show that the hybrid model proposed in this paper has excellent performance in terms of accuracy and return rate. [ABSTRACT FROM AUTHOR]

This paper explores the optimal management of defined contribution pension plans under multiple risks and strategic interactions using a stochastic differential game between two managers, accounting for model misspecification. Managers who are both competitive and averse to ambiguity can invest in a financial market that contains a risk-free asset, an inflation-linked bond, a market index, and a stock. These managers may have varying levels of aversion to ambiguous risks. The inflation index is computed using the Fisher equation. The market index and stock prices are described by a class of non-Markovian multivariate stochastic covariance models, with the market risk prices being reliant on a multivariate affine-diffusion factor process. Each manager aims to maximize the expected utility of inflation-adjusted terminal wealth relative to their competitors under the worst-case scenario of different measures, ensuring that the investment strategy is robust to model uncertainty. The robust Nash equilibrium investment strategies' explicit expressions, density generator processes under the well-defined worst-case scenarios, and corresponding value functions are derived using a backward stochastic differential equation approach to address this robust non-Markovian game. The admissibility of the robust equilibrium policies is confirmed under certain technical conditions. Finally, we provide numerical examples to demonstrate the effects of model parameters on robust investment policies and clarify the economic significance of our theoretical findings. [ABSTRACT FROM AUTHOR]

Recently, the coronavirus (COVID-19) pandemic has affected the economic situation all over the world. The objective of this research is to examine the effect of coronavirus spreading and vaccination rate on the stock market index in Turkey. To do that, we have applied several statistical methods, namely ridge, lasso, principal components, and partial least squares (PLS) regression versus elastic-net regression based on empirical mode decomposition, which can overcome the non-stationary problem and nonlinearity characteristics. The result of using the elastic net regression method based on empirical mode decomposition shows significant effects of coronavirus spreading on the stock market, and it varies based on the intrinsic mode function coefficients and frequencies. The findings of this research could assist practitioners and policymakers to design important strategies in the light of varying stock market dynamics during the coronavirus pandemic. [ABSTRACT FROM AUTHOR]

This paper analyzes the effects of central bank interventions via large-scale purchases of government debt securities on the pricing of stock market indices. This study examines the effects of changes in the size of the Federal Reserve's balance sheet in three intervention scenarios: during the 2008–2013 period, the 2020–2022 period, and in the years between by using the instrumental variables three-stage least squares (3SLS) method for a time series approach, and calculates the effects of these interventions on each index in a fund of funds setup using the panel data strategy. This study confirms that large-scale purchases of government debt securities in response to the Great Recession and COVID-19 crises influenced the pricing of equity markets via their effect on the pricing of treasury bonds, with different degrees of sensitivity of each index to the effects on yields. Although the findings apply to the U.S. market, the results indicate that the pricing of small capitalization indices such as the Russell 2000 are less sensitive to changes in treasury yields caused by central bank interventions than large capitalization indices such as the DJIA. This research contributes to the understanding of financial asset pricing, particularly by identifying price distortions within equity market portfolios. [ABSTRACT FROM AUTHOR]

In this manuscript, daily and weekly geometric Brownian motion forecasts are obtained and tested for reliability for three indexes, DJIA, NASDAQ and S&P 500. A twenty-year rolling window is used to estimate the drift and diffusion components, and applied to obtain one-period-ahead geometric Brownian motion index values and associated probabilities. Expected values are estimated by totaling up the product of the index value and its associated probabilities, and test for reliability. The results indicate that geometric Brownian-simulated expected index values estimated using one thousand simulations can be reliable forecasts of the actual index values. Expected values estimated using one or ten simulations are not as reliable, while those obtained using at least one hundred simulations could be useful. [ABSTRACT FROM AUTHOR]

This study's main purpose is to use Bayesian structural time-series models to investigate the causal effect of an earthquake on the Borsa Istanbul Stock Index. The results reveal a significant negative impact on stock market value during the post-treatment period. The results indicate rapid divergence from counterfactual predictions, and the actual stock index is lower than would have been expected in the absence of an earthquake. The curve of the actual stock value and the counterfactual prediction after the earthquake suggest a reconvening pattern in the stock market when the stock market resumes its activities. The cumulative impact effect shows a negative effect in relative terms, as evidenced by the decrease in the BIST-100 index of − 30%. These results have significant implications for investors and policymakers, emphasizing the need to prepare for natural disasters to minimize their adverse effects on stock market valuations. [ABSTRACT FROM AUTHOR]

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Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested that this problem be solved using sequential Monte Carlo (SMC, also known as particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper modifies the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance. [ABSTRACT FROM AUTHOR]

We present novel, robust methods for inference on market (non-)efficiency, volatility clustering, and nonlinear dependence in financial return series. In contrast to existing methodology, our proposed methods are robust against nonlinear dynamics and tail-heaviness of returns. Specifically, our methods only rely on return processes being stationary and weakly dependent (mixing) with finite moments of a suitable order. This includes robustness against power-law distributions associated with nonlinear dynamic models such as GARCH and stochastic volatility. The methods are easy to implement and perform well in realistic settings. We revisit a recent study by Baltussen, van Bekkum, and Da (2019 , J. Financ. Econ. , 132, 26–48) on autocorrelation in major stock indexes. Using our robust methods, we document that the evidence of the presence of negative autocorrelation is weaker, compared with the conclusions of the original study. [ABSTRACT FROM AUTHOR]

Copyright of European Journal of Management & Business Economics is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Stock index fluctuations are characterized by high noise and their accurate prediction is extremely challenging. To address this challenge, this study proposes a spatial–temporal–bidirectional long short-term memory (STBL) model, incorporating spatiotemporal attention mechanisms. The model enhances the analysis of temporal dependencies between data by introducing graph attention networks with multi-hop neighbor nodes while incorporating the temporal attention mechanism of long short-term memory (LSTM) to effectively address the potential interdependencies in the data structure. In addition, by assigning different learning weights to different neighbor nodes, the model can better integrate the correlation between node features. To verify the accuracy of the proposed model, this study utilized the closing prices of the Hong Kong Hang Seng Index (HSI) from 31 December 1986 to 31 December 2023 for analysis. By comparing it with nine other forecasting models, the experimental results show that the STBL model achieves more accurate predictions of the closing prices for short-term, medium-term, and long-term forecasts of the stock index. [ABSTRACT FROM AUTHOR]

Copyright of Arab Journal for Scientific Publishing is the property of Research & Development of Human Recourses Center (REMAH) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The stock market index typically mirrors the financial market's performance. Hence, accurate prediction of stock market index trends is essential for investors aiming to mitigate financial risk and enhance future investment returns. Traditional statistical approaches often struggle with the non-linear nature of stock market index data, leading to potential inaccuracies in long-term predictions. To address this issue, we introduce the TCN-LSTM-SNN (TLSNN) model, a hybrid framework that integrates Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) for robust feature extraction, within a highly efficient Spiking Neural Network (SNN) architecture. Additionally, we employ the Subtraction-Average-Based Optimizer (SABO) to refine the Variational Mode Decomposition (VMD) technique, thereby separating the periodic and trend components of stock indices, reducing noise interference, and establishing a decomposition ensemble framework to bolster the model's resilience. The experimental results show that the VMD-TLSNN hybrid model suggested in this study surpasses other individual benchmark models and their hybrid models in prediction accuracy. Additionally, it demonstrates notably lower energy consumption compared to other hybrid models. [ABSTRACT FROM AUTHOR]

This study uses the foreign exchange rate to investigate how the Indonesia Stock Exchange's Composite Stock Price Index is affected by growing oil prices and economic growth. The study's population consists of all data on the Indonesia Stock currency's Composite Stock Price Index (JCI), as well as all data on foreign exchange rates, GDP, and worldwide oil prices from Bank Indonesia for the quarters ending in 2010 and 2020. The analysis technique uses path analysis which is an extension of multiple linear analysis, or path analysis is the use of regression analysis to estimate the causal relationship between variables (causal model), the population used is all composite stock price index data and the sample used is a saturated sample of all combined stock price index data. The results of the study show that the Composite Stock Price Index is influenced by the World Oil Price. There is no contribution from Economic Growth to the Composite Stock Price Index. Foreign exchange rates have an effect on the Composite Stock Price Index based on the price of oil globally. Through foreign exchange rates, economic progress affects the composite stock price index. [ABSTRACT FROM AUTHOR]

Stock market indices are volatile by nature, and sudden shocks are known to affect volatility patterns. The autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models neglect structural breaks triggered by sudden shocks that may lead to an overestimation of persistence, causing an upward bias in the estimates. Different regime‐switching models that have abrupt regime‐switching governed by a Markov chain were developed to model volatility in financial time series data. Volatility modelling was also extended to spatially interconnected time series, resulting in spatial variants of ARCH models. This inspired us to propose a Markov switching framework of the spatio‐temporal log‐ARCH model. In this article, we discuss the Markov‐switching extension of the model, the estimation procedure and the smooth inferences of the regimes. The Monte Carlo simulation studies show that the maximum likelihood estimation method for our proposed model has good finite sample properties. The proposed model was applied to 28 stock indices' data that were presumably affected by the 2015–2016 Chinese stock market crash. The results showed that our model is a better fit compared to that of the one‐regime counterpart. Furthermore, the smoothed inference of the data indicated the approximate periods where structural breaks occurred. This model can capture structural breaks that simultaneously occur in nearby locations. [ABSTRACT FROM AUTHOR]

Crude oil prices crashed and dropped into negative territory at the onset of the COVID-19 pandemic. This extreme event triggered a series of great-magnitude aftershocks. We seek to investigate the cascading dynamics and the characteristics of the series immediately following the oil market crash. Utilizing a robust method named the Omori law, we quantify the correlations of these events. This research presents empirical regularity concerning the number of times that the absolute value of the percentage change in the oil index exceeds a given threshold value. During the COVID-19 crisis, the West Texas Intermediate (WTI) oil prices exhibit greater volatility compared to the Brent oil prices, with higher relaxation values at all threshold levels. This indicates that larger aftershocks decay more rapidly, and the period of turbulence for the WTI is shorter than that of Brent and the stock market indices. We also demonstrate that the power law's exponent value increases with the threshold value's magnitude. By proposing this alternative method of modeling extreme events, we add to the current body of literature, and the findings demonstrate its practical use for decision-making authorities—particularly financial traders who model high-volatility products like derivatives. [ABSTRACT FROM AUTHOR]

This study assesses the effects of the European Central Bank's (ECB) unconventional monetary policy (UMP) on the prices of selected European stock market indices during the European sovereign debt (2010–2012) and the COVID-19 pandemic (2020–2022) crises interventions. This research employs the instrumental variables (IV) two-stage least squares (2SLS) model approach to evaluate the effects of changes in the size of the ECB's balance sheet on the pricing of key equity market indices in Europe. The results of this study suggest that the ECB's asset value expansion had the opposite statistically significant effects on the European stock market indices' prices between the interventions. That is, an increase in the ECB's balance sheet size was associated with a decrease in the prices of the indices during the sovereign debt crisis and with a rise during the COVID-19 pandemic. This research pinpoints the price sensitivity of each of the European equity indices to the ECB's UMP and determines the different outcomes of the ECB's quantitative easing policy between the interventions. [ABSTRACT FROM AUTHOR]

Stock market indices are important tools to measure and compare stock market performance. The Selective Stock Price (SSP) index reflects fluctuations in a set value of financial instruments of Santiago de Chile's stock exchange. Stock indices also reflect volatility linked to high uncertainty or potential investment risk. However, economic shocks are altering volatility. Evidence of long memory in SSP time series also exists, which implies long-term persistence. In this paper, we studied the volatility of SSP time series from January 2010 to September 2023 using fractionally heteroskedastic models. We considered the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) innovations—the ARFIMA-GARCH model—for SSP log returns, and the fractionally integrated GARCH, or FIGARCH model, was compared with a classical GARCH one. The results show that the ARFIMA-GARCH model performs best in terms of volatility fit and predictive quality. This model allows us to obtain a better understanding of the observed volatility and its behavior, which contributes to more effective investment risk management in the stock market. Moreover, the proposed model detects the influence volatility increments of the SSP index linked to external factors that impact the economic outlook, such as China's economic slowdown in 2012 and the subprime crisis in 2008. [ABSTRACT FROM AUTHOR]

The study analyses the statistical relationship between the degree of seasonal depression and stock index returns. For this analysis, we examine the daily returns of two US and five European stock indices using OLS regression. The analysis found a statistically significant relationship between seasonal depression and the change in returns. However, due to the limited use of the reduced form, this only confirms the link between seasonal depression and returns, and further observations would be required to confirm a causal relationship. [ABSTRACT FROM AUTHOR]

The stock market data analysis has received interest as a result of technological advancements and the investigation of new machine learning models, since these models provide a platform for traders and business people to choose gaining stocks. The business price prediction is a challenging and extremely complex process due to the impact of several factors on company prices. The numerous patterns that the stock market goes, they have been the focus of extensive research and analysis by numerous experts. There are several large data sets accessible, an artificial intelligence and machine learning techniques are developing quickly, and because of the machine’s improved computational power, complex stock price prediction algorithms can be developed. This paper presents stock market index prediction based on market trend using long short-term memory (LSTM). Using built-in application programmable interface (API), Yahoo Finance offers a simple method to programmatically retrieve any historical stock prices of an organization using the ticker name. The standard and poor’s 500 index (S&P 500 index) include the firms that have been taken into consideration here. Utilizing the selected input variable, singlelayer and multi-layer LSTM models are implemented, and the measurement parameters of mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R) are used to compare each performance. Nearly all of the real closing price’s curve and the prediction curve’s closing price for test data overlap. A potential stock investor may benefit significantly from such a prediction by using it to make well-informed choices that would increase his earnings. [ABSTRACT FROM AUTHOR]

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Direct indexing (DI) combined with tax-loss harvesting (TLH) is a popular approach to generate capital losses to offset a client's capital gains while attempting to track a specified index. The goal is to increase after-tax excess returns by postponing or reducing tax payments. More TLH opportunities exist when the index constituents have a higher dispersion of returns and are therefore more likely to be at a loss. To create more TLH opportunities within DI, the authors propose a method to modify the target index to increase dispersion based on the realized volatility of stocks. They show that as the average constituent volatility increases, the effectiveness of DI TLH also increases against plain DI TLH that does not utilize this method. The authors further show that this method can significantly increase the after-tax excess returns versus plain DI TLH at the same tracking error per unit of additional returns. Further increases of dispersion can increase after-tax excess returns even more, although at a gradually higher cost of tracking error. [ABSTRACT FROM AUTHOR]

Although many investors are mindful of mutual funds and ETFs as investment instruments for indexing strategy, few are familiar with direct indexing strategies designed to replicate any stock market index using a separately managed account (SMA) format. Historically, the SMA format required significant computations as each client account required individual management. Recent advancements in computational power, however, allow for the growth of direct indexing, providing clients with customizability, not only to take the benefit of tax-loss harvesting but also to implement other tailored investment objectives. The results provided in this article suggest that there exist benefits associated with tax-loss harvesting even though tax-loss harvesting defers but does not discharge tax obligations. Furthermore, the article also reveals that maintaining a reasonable ex-post tracking error to achieve the indexing goal while obtaining a positive tax-loss benefit is feasible. [ABSTRACT FROM AUTHOR]

This study employs the Maximal Overlap Discrete Wavelet Transform technique to analyze the wavelet-based correlations between Bitcoin, bond markets, and thirteen sectoral stock indices in India over the period from 2O17 to 2O23, focusing on the comparison of pre -and post-COVID-19 pandemic effects. The aim is to investigate the dynamic interrelationships and to understand the impact of the COVID - 19 pandemic on these financial assets. The study period is divided into pre-COVID-19 and post-COVID-19. Findings from the study reveal a minimal negative correlation between Bitcoin, bond markets, and the sectoral stock indices in the pre -COVID era, indicating a lack of significant interdependence among these assets. However, the scenario changes markedly in the post-COVID period, shifting towards a positive correlation. This shift suggests that the COVID-19 pandemic has altered the relationship dynamics, leading to a more interconnected financial environment where movements in Bitcoin have begun to show a significant positive correlation with the movements in bond and sectoral stock indices in India. The study contributes to the existing literature by providing empirical evidence of how external shocks, such as the COVID-19 pandemic, can influence the correlation patterns among different financial assets. It highlights the importance of considering the changing dynamics in financial market correlations for investors, policymakers, and researchers in portfolio diversification, risk management, and financial stability analysis. Further, it underscores the role of alternative investments like Bitcoin in the evolving market landscape, particularly in response to global crises. [ABSTRACT FROM AUTHOR]

This paper uses Data Envelopment Analysis to assess the performance of stocks in terms of return on assets, risk and their Environmental‐Social‐Governance score. Next, latent class analysis (LCA) is used to determine groups with similar performances and relates group membership to a set of covariates. The empirical application employs a set of 139 European manufacturing companies from 2016 to 2021 period. The results suggest stocks on average perform 25% below their potential in each of the three dimensions. The LCA suggests five groups and membership of lower performance classes significantly relates with lower size, R&D expenses and Tobin's Q. [ABSTRACT FROM AUTHOR]

In this paper, we use quarterly stock price index data to test the weak form of the efficient market hypothesis for 13 art market indices. The fact that hypotheses can be efficiently tested by performing unit root tests is well known. The conventional unit root test results show that most art indices have unit roots, which means that these art markets are efficient. However, the results of the non-linear quantile unit root tests we conduct over a range of quantiles lead us to reject the null of the unit root test for several art markets. To acquire more information, we perform the test over sub-quantile intervals, finding asymmetric market efficiency between the low and high quantile intervals: we cannot reject the unit root null in upper quantile intervals for most art price indices. [ABSTRACT FROM AUTHOR]

In the face of changing investment intentions, the integration of environmental, social and governance (ESG) considerations into portfolio optimization is becoming the norm. Indeed, the purely financial dimension of investments is beginning to give way to the sustainable or responsible dimension which aims to generate long-term financial returns. Such an investment strategy has the constraint of investing only in companies which, according to the rating agencies, best meet these three criteria. To study the impact of similar investment strategies on portfolio performance, we compared the performance of 3 portfolios constructed with different ESG preferences. Built on the basis of the Best-in-class approach, the first is composed of the highest rated assets (AAA and AA) while the second has average ratings (A and BBB) and the third of the worst ESG ratings (BB, B and CCC). The results obtained in a robust mean-LPM theoretical framework, we managed to show that a preference for stocks with average ESG ratings in the composition of a portfolio to have the best risk-return trade-off. [ABSTRACT FROM AUTHOR]

This study measures the immediate impact of Russia–Ukraine war on the global stock markets for the first four months since Russia's first invasion attempt on February 24, 2022. Daily closing stock indices have been used from selected stock markets of six different continents. By applying event study method, it observes mixed impact on different stock markets. Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH 1,1) indicates the presence of significant volatility and leverage effect in all the markets. Regression estimates show significantly positive impact of VIX and negative impact of oil on the abnormal returns of the global stock markets. Diversifying energy supply and source, accelerating deployment of renewables and promoting electronic vehicles and machines might bring positive result for the financial market. It is expected that this research will provide policymakers, regulatory authorities, investors and all concerned stakeholders a precise guideline to handle the immediate impact of war on the stock prices and to formulate appropriate strategies to keep investment free from risk and uncertainties. [ABSTRACT FROM AUTHOR]

Enterprises and companies are also impacted by air pollution in addition to people and the environment. Air pollution can result in decreased output and sharp rises in healthcare costs. Publicly traded firms' profitability and stock prices are significantly impacted by air pollution. Dividend yield, which has significant importance to investors, is a critical component of stock market growth. The relationship between air pollution and dividend yield can give investors relevant information on the financial success of businesses. Pakistan has been chosen as a case study for research on how air pollution affects dividend yield. To better understand the possible effects of air pollution on financial performance in emerging nations, it is fascinating to look at Pakistan, which has a booming economy and a rising stock market. Pakistan stock exchange (PSX) and S&P 500 stock exchanges' dividend yields are compared to see how air pollution affects them. In their respective regions, they are two of the most well-known and recognized stock market indices. This study employed regression analysis to look at the relationship between dividend yield and air pollution. Finding relationships between the variables and examining how air pollution impacts dividend yield were the two goals of the investigation. The results of the investigation depict that dividend yield is significantly impacted by air pollution. [ABSTRACT FROM AUTHOR]

Stock index trend prediction is a very important topic in the finance. The purpose of this paper is to compare six ensemble learning related techniques for stock index direction prediction, including four boosting methods (Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost) and Gradient Boosting Decision Tree (GBDT)), one bagging method (Random Forest (RF)) and one tree-structured machine learning method (Decision Tree (DT)). The Shanghai Composite Index is chosen for experimental evaluation. A factor library of seventy-two technical factors, thirty-five macro factors and seven micro factors are our inputs. Our predictions are one month ahead, and each prediction model is evaluated by the Area Under Curve (AUC). The results indicate that ensemble learning techniques perform well in stock index prediction, with all AUC values above 0.5. RF is considered as the top algorithm with an AUC value of 0.7355 before feature selection and 0.6736 after feature selection. Also, we predict the stock index trend using a comprehensive factor library and three single factor libraries, respectively. The results show that forecasting stock index directions with a complete factor library is of great importance, which could achieve more stable forecasting results. This study contributes to literature in that it is, to the best of our knowledge, the first to make an extensive evaluation of ensemble learning related methods by constructing a comprehensive factor library and three single factor libraries. [ABSTRACT FROM AUTHOR]

Upper estuarine forested wetlands (UEFWs) play an important role in the sequestration of atmospheric carbon (C), which is facilitated by their position at the boundary of terrestrial and maritime environments but threatened by sea level rise. This study assessed the change in aboveground C stocks along the estuarine–riverine hydrogeomorphic gradient spanning salt-impacted freshwater tidal forested wetlands to freshwater forested wetlands in seasonally tidal and nontidal landscape positions. Standing stocks of C in forested wetlands were measured along two major coastal river systems, the Winyah Bay in South Carolina and the Savannah River in Georgia (USA), replicating and expanding a previous study to allow the assessment of change over time. Aboveground C stocks on these systems averaged 172.9 Mg C ha−1, comparable to those found in UEFWs across the globe and distinct from the terrestrial forested ecosystems they are often considered to be a part of during large-scale C inventory efforts. Groundwater salinity conditions as low as 1.3 ppt were observed in conjunction with losses of aboveground C. When viewed in context alongside expected sea level rise and corresponding saltwater intrusion estimates, these data suggest a marked decrease in aboveground C stocks in forested wetlands situated in and around tidal estuaries. [ABSTRACT FROM AUTHOR]

The technology (tech) sector outperformed all other sector stock price averages in 2023. This paper addresses two issues regarding the tech sector. First, relative to other firms, are there any managerial areas in which the tech sector outperforms to explain their superior performance relative to all other firms? Second, within the tech sector, what managerial practices lead to higher profits? Four areas of managerial performance are considered: customer satisfaction, employee engagement and development, innovation, and social responsibility. This paper utilizes Bloomberg data and takes the novel approach of utilizing the Drucker Institute Indexes to assess performance in the tech sector. The Drucker Institute has computed a Corporate Effectiveness Index since 2017. The overall effectiveness index is based on performance regarding five dimensions: customer satisfaction, employee engagement and development, innovation, social responsibility, and financial strength. The firms they track are United States companies whose shares are traded on the New York Stock Exchange or Nasdaq Stock Market and meet certain size requirements. Among the four dimensions tested, customer satisfaction is the one dimension that is positive and significant when comparing across tech firms. Second, employee engagement is more important for tech firms than for other firms. The major contribution of this paper's findings is that while innovation may be the hallmark feature of tech firms, innovation for its own sake does not drive firm performance. A focus on people (customers and employees) is the key to earning higher profits in the tech sector. [ABSTRACT FROM AUTHOR]

Electronic and digital trading models have made stock trading more accessible and convenient, leading to exponential growth in trading data. With a wealth of trading data available, researchers have found opportunities to extract valuable insights by uncovering patterns in stock price movements and market dynamics. Deep learning models are increasingly being employed for stock price prediction. While neural networks offer superior computational capabilities compared with traditional statistical methods, their results often lack interpretability, limiting their utility in explaining stock price volatility and investment behavior. To address this challenge, we propose a causality‐based method that incorporates a multivariate approach, integrating news event attention sequences and sentiment index sequences. The goal is to capture the intricate and multifaceted relationships among news events, media sentiment, and stock prices. We illustrate the application of this proposed approach using a Global Database of Events, Language, and Tone global event database, demonstrating its benefits through the analysis of attention sequences and media sentiment index sequences for news events across various categories. This research not only identifies promising directions for further exploration but also offers insights with implications for informed investment decisions. [ABSTRACT FROM AUTHOR]

In recent years, the semiparametric methods for the joint estimation and prediction of value at risk (VaR) and expected shortfall (ES) have triggered great interests and attention. Compared to existing literature which usually incorporates realized volatility (RV) into the dynamic semiparametric risk models, this paper considers three more robust proxies (medRV, BPV, and RK) of intraday volatility in the models to verify whether high‐frequency information can improve the joint prediction ability of risk measures. To strengthen the persuasion of conclusions, four international stock indices (S&P500, Nikkei225, GDAXI, and DJIA) are applied to these models to estimate and forecast VaR and ES at different probability levels (1%, 2.5%, 5%, and 10%). Then, the predicted VaR and ES are backtested by several methods individually, and the popular score function FZ0 and MCS test are used to compare the effects of jointly predicting risk measures. Our results confirm that these semiparametric models containing intraday information outperform the benchmark models for four stocks and various probability levels, and medRV is the best volatility measure in improving the effects of models. [ABSTRACT FROM AUTHOR]