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The prediction of stock market (SM) indices is a fascinating task. An in-depth analysis in this field can provide valuable information to investors, traders and policy makers in attractive SMs. This article aims to apply a correlation feature selection model to identify important technical indicators (TIs), which are combined with multiple deep learning (DL) algorithms for forecasting SM indices.

The methodology involves using a correlation feature selection model to select the most relevant features. These features are then used to predict the fluctuations of six markets using various DL algorithms, and the results are compared with predictions made using all features by using a range of performance measures.

The experimental results show that the combination of TIs selected through correlation and Artificial Neural Network (ANN) provides good results in the MADEX market. The combination of selected indicators and Convolutional Neural Network (CNN) in the NASDAQ 100 market outperforms all other combinations of variables and models. In other markets, the combination of all variables with ANN provides the best results.

This article makes several significant contributions, including the use of a correlation feature selection model to select pertinent variables, comparison between multiple DL algorithms (ANN, CNN and Long-Short-Term Memory (LSTM)), combining selected variables with algorithms to improve predictions, evaluation of the suggested model on six datasets (MASI, MADEX, FTSE 100, SP500, NASDAQ 100 and EGX 30) and application of various performance measures (Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error(RMSE), Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE)).

The aim of this paper is double: firstly, to examine the presence of herd behavior in four MENA stock markets (the Egyptian, Jordanian, Moroccan and Tunisian markets), and secondly, to study the anchoring behavior in these markets.

The authors employ quantile regression analysis for testing herding bias in the MENA region, following the methodology of Chiang and Zheng (2010). Regarding the evaluation of anchoring bias, the authors follow the methodology of Lee et al. (2020). The study uses daily stock index returns ranging from April 1, 2011, to July 31, 2019, as well as CAC40 and NASDAQ returns.

The authors find evidence of herding during down-market periods in the lower tail for Egypt, Jordan and Tunisia, while this bias is detected during up-market periods in the lower tail for Morocco. In addition, based on historical returns, the authors conclude that there is a momentum effect in these markets, and they are dependent on the CAC40 and NASDAQ indices.

This paper confirms the findings of previous works devoted to some emerging markets such as China, Japan and Hong Kong, where anchoring and herding are considered the most important and impactful heuristic and cognitive biases in making decisions under uncertainty, particularly during down-market periods.

The paper contributes to the empirical literature in herding and anchoring biases for MENA countries. The absence of empirical work on the effect of these biases on stock prices in emerging markets and those of the MENA zone leads to the discussion of the impact of psychological biases on these of markets.

Increasing focus on socially responsible investments (SRIs) and green projects in recent times, coupled with the arrival of COVID pandemic, are the main drivers of this study. The authors conduct a post-factum analysis of investor choice between sin and green investments before and through the COVID outbreak.

A passive investor is introduced who seeks maximum risk-adjusted return and/or investment variance. When presented an opportunity to add sin and/or green investments to her initial one-asset market-only investment position, she views and handles this issue as a portfolio problem (MPT). She estimates value-at-risk (VaR) and conditional-value-at-risk (CVaR) for portfolios to account for downside risk.

Green investments offer better overall risk-return optimization in spite of major inter-period differences in return-risk dynamics and substantial downside risk. Portfolios optimized for minimum variance perform just as well as the ones optimized for minimum downside risk. Return and risk have settled at higher levels since the onset of COVID, resulting in shifting the efficient frontier towards north-east in the return-risk space.

The study contributes to the literature in two ways: One, it examines investor choice between sin and green investments during a global health emergency and views this choice against the one made during normal times. Two, instead of using the principles of modern portfolio theory (MPT) explicitly for diversification, the study uses them to identify investor preference for one over the other investment type. This has not been widely done thus far.

The aim of this study is to analyse the factors that contribute to the disclosure of relational liabilities (RLs) of the US companies.

The study uses content analysis to examine the disclosure of RLs in annual reports of the US companies listed on the Nasdaq-100 index from 2013 to 2015.

The study finds a positive correlation between the disclosure of RLs and gender diversity of the board of directors as well as the education level of the CEO. By contrast, the disclosure of RLs is negatively associated with the age of the CEO. Companies in knowledge-intensive industries also tend to disclose more information about their RLs than those in other industries.

This study focuses on the determinants of RLs, whereas previous research has mainly examined the positive impact of voluntary disclosure of intellectual capital on financial performance. The main objective of this study is to shed light on the factors that influence the disclosure of RLs.

The purpose of this paper is to study the impact of the 2016 presidential election polls on the stock market.

The empirical model includes daily stock returns as the dependent variable and past asset prices, 10-year treasury rates, opinion polls and VIX (market uncertainty) as explanatory variables with a one-year lag. The model was estimated using two sets of daily polling data: from July 1, 2015, to November 8, 2016, and from June 1, 2016, to November 8, 2016. Additional descriptive statistics, such as means and standard deviations, were also calculated.

The estimated results did not reveal any statistically significant effects of opinion polls in favor of one candidate over another on stock returns. Simple statistical tests, however, show that the market performed better when Trump held a polling advantage over Clinton.

To the best of the authors’ knowledge, this is the only study that has examined the effects of the 2016 presidential election polls on the US stock market. This study adds value to the understanding of the relationship between election polls and the stock market in the USA.

This study is the first to investigate the causal relationship between Bitcoin and equity price returns by sectors. Previous studies have focused on aggregated indices such as S&P500, Nasdaq and Dow Jones, but this study uses mixed frequency and disaggregated data at the sectoral level. This allows the authors to examine the nature, direction and strength of causality between Bitcoin and equity prices in different sectors in more detail.

This paper utilizes an Unrestricted Asymmetric Mixed Data Sampling (U-AMIDAS) model to investigate the effect of high-frequency Bitcoin returns on a low-frequency series equity returns. This study also examines causality running from equity to Bitcoin returns by sector. The sample period covers United States (US) data from 3 Jan 2011 to 14 April 2023 across nine sectors: materials, energy, financial, industrial, technology, consumer staples, utilities, health and consumer discretionary.

The study found that there is no causality running from Bitcoin to equity returns in any sector except for the technology sector. In the tech sector, lagged Bitcoin returns Granger cause changes in future equity prices asymmetrically. This means that falling Bitcoin prices significantly influence the tech sector during market pullbacks, but the opposite cannot be said during market rallies. The findings are consistent with those of other studies that have established that during market pullbacks, individual asset prices have a tendency to decline together, whereas during market rallies, they have a tendency to rise independently. In contrast, this study finds evidence of causality running from all sectors of the equity market to Bitcoin.

The findings have significant implications for investors and fund managers, emphasizing the need to consider the asymmetric causality between Bitcoin and the tech sector. Investors should avoid excessive exposure to both Bitcoin and tech stocks in their portfolio, as this may lead to significant drawdowns during market corrections. Diversification across different asset classes and sectors may be a more prudent strategy to mitigate such risks.

The study's findings underscore the need for investors to pay close attention to the frequency and disaggregation of data by sector in order to fully understand the true extent of the relationship between Bitcoin and the equity market.

The purpose of this study is to compare market reactions to the change in the demand by index funds between large and small company stocks by examining the transition of the S&P 500, S&P 400 MidCap and S&P 600 SmallCap indexes from market capitalization to free-float weighting. This unique information-free event allows not only avoiding confounding information signaling and investor awareness effects but also comparing the effect of the decrease in demand on stocks of different sizes.

This study uses the event study methodology to calculate abnormal returns and trading volume around the full-float adjustment day. It also tests for significant changes in institutional ownership and liquidity. Multivariate regressions are used to examine the relation of liquidity changes and price elasticity of demand to the cumulative abnormal returns around the full-float adjustment day.

This study finds significant decreases in stock price accompanied with significant increases in trading volume on the full-float adjustment day, and significant gains in quasi-indexer institutional ownership and liquidity. The main finding is that cumulative abnormal returns around the event period are related to changes in the number of quasi-indexer and transient institutional shareholders, not to changes in liquidity or price elasticity of demand.

This study provides the first comprehensive comparison analysis of stock market reactions to the decline in demand between large and small company stocks. As an important implication for future studies of the index effect, changes in institutional ownership should be considered in the analysis.

With the rapid development of the financial market, stock index futures have been the one of important financial instruments. Predicting stock index futures accurately can bring considerable benefits for investors. However, traditional models do not perform well in stock index futures forecasting. This study put forward a novel hybrid model to improve the predictive accuracy of stock index futures.

This study put forward a multivariate deep learning framework based on extreme gradient boosting (XGBoost) for stock index futures price forecasting. First, the original sequences were decomposed into several sub-sequences by variational mode decomposition (VMD), and these sub-sequences were reconstructed by sample entropy (SE). Second, the gradient boosting decision tree (GBDT) was used to rank the feature importance of influential factors, and the top influential factors were chosen for further prediction. Next, reconstructed sequence and the multiple factors screened were input into the bidirectional gate recurring unit (BiGRU) for modeling. Finally, XGBoost was used to integrate the modeling results.

For the sake of examining the robustness of the proposed model, CSI 500 stock index futures, NASDAQ 100 index futures, FTSE 100 index futures and CAC 40 index futures are selected as sample data. The empirical consequences demonstrate that the proposed model can serve as an effective tool for stock index futures prediction. In other words, the proposed model can improve the accuracy of stock index futures.

In this paper, an innovative hybrid model is proposed to enhance the predictive accuracy of stock index futures. Meanwhile, this method can be applied in other financial products prediction to achieve better forecasting results.

This study aims to examine the relationship between the individual auditor’s industry specialization and the audit report lag (hereafter ARD). Further, it explores whether changing in the audit reporting requirement (i.e. the adoption of ISA701) influences the auditor’s industry specialization effect on the ARD.

A large data set of companies listed on the NASDAQ OMX Stockholm over the period 2010–2019 has been analyzed. Least squares regressions have been estimated to provide empirical evidence for the researched hypotheses.

The research findings indicate that the ARD is shorter for client firms audited by an industry specialist audit partner. Testing for the moderating role of changing in the auditing reporting regulation on the relation between the audit partner’s industry specialization and the ARD, the authors reveal that all client firms (except client firms with industry specialist audit partners) experienced an increase in the ARD. Overall, the baseline regression findings are found to be robust to the endogenous auditor choice and multiple measures of both the ARD and the auditor’s industry specialization.

This paper provides novel evidence on the relationship between the audit reporting lag and industry specialization from the individual auditor perspective, an issue that has hitherto been unexplored. The regression results further contribute to the upsurge debate about the consequences of changing in the audit reporting model by providing consistent support for the importance of industry specialization of the audit partner in minimizing costs derived from the former requirement.

Value-added intellectual coefficient (VAIC) is extensively used as a measure of intellectual capital (IC), but it is criticized for not capturing the totality of IC. Therefore, this study aims to analyse critiques of the original VAIC and proposes a modified VAIC by adding missing IC components and adjusting for exogenous factors. The study uses a modified VAIC model to investigate the relationship between IC, firm performance (FP) and market value (MV) for US non-financial firms.

This study employed fundamental data of US non-financial firms listed on the NYSE and NASDAQ from 1980 to 2019. A final sample consisted of 6,019 firms and 62,686 firm-year observations.

The results provide a significant positive effect of aggregate and components of modified VAIC on FP and MV. Moreover, results validate the modified VAIC model and find that the modified VAIC explains changes in shareholders' MV. In addition, findings indicate that modified VAIC serves as an additional intangible factor to explain firms' capital structure decisions.

The findings have important implications for management, owners, researchers and investors.

The modified VAIC model differs from the original VAIC model in four ways: first, it corrects the measurement of structural capital efficiency (SCE) following the accounting principle. Second, it replaces SCE with innovation capital efficiency (InVCE) and relational capital efficiency (RCE) to account for missing components of information of structural capital (SC). Third, the modified VAIC model adjusts for exogenous factors like business cycles and cross-industry variations. Finally, with the addition of InVCE and RCE as components of SCE, innovation capital (InVC) and relational capital (RC) are added to the calculation of value-added (VA) as components of IC.