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The purpose of this paper is to investigate how bank-specific factors affect the riskiness of conventional and Islamic banks in response to shocks in major financial indices as market conditions change.

The authors use a multivariate quantile model using daily equity returns data to analyze financial risk spillovers in the values at risk that may occur between major financial indices and the equity prices of conventional and Islamic banks worldwide. Then, using both quantile and quantile-on-quantile models, the authors examine the effects of bank-specific variables such as leverage ratio, bank size, return on equity and capital adequacy ratio on the initial impact of shocks in major global financial indices on bank equity price returns at different quantiles of shocks and bank-specific variables.

The findings reveal that major financial indices can predict bank stock returns. Moreover, the authors find that the effect of bank-specific factors on the riskiness of banks is heterogeneous in that it depends on the bank type (Islamic vs conventional), the level of banking variable (high vs low) and, more importantly, market conditions.

To the best of the authors’ knowledge, this is the first study that compares the dual banking system with stock market performance while considering bank-specific variables as market conditions change. The results of this study reveal that the effect of bank-specific variables on bank performance varies according to different quantiles of shocks and bank-specific variables. Islamic banks may echo or differ from conventional banks depending on the specific factor under investigation.

This study aims to investigate the determinants of Islamic stock returns listed on Jakarta Islamic Index (Indonesia) between 2008 and 2018.

This study uses a quantile bounded autoregressive distributed lag (QBARDL) model to uncover relevant relationships.

This study finds that the Dow Jones Islamic Market Index, gold returns, world oil prices and exchange rates are the determinants of the Indonesia’s Islamic stock returns. However, the relationship is time varying developing intra-/inter-quantile bounded.

Integration of the Islamic stock returns with the real economic indicators changes over time. The findings have important implications for the policymakers, the fund managers and the investors to anticipate consequences when considering the macroeconomic conditions before participating in the Indonesian Islamic stock market.

Using a QBARDL, this study finds that the Islamic stock returns have on net and “time-varying intra-/inter-quantile developing” relationship with its determinants as data quantiles progressed from 25% to 75%.

This study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.

The study analyzes 314,864 tweets between January 1, 2017, to December 31, 2018, collected with the Tweepy library. The companies’ financial data were obtained from Refinitiv Eikon. Using the netnographic method, a Twitter Investor Sentiment Index (ISI) was constructed based on terms associated with the stocks. This Twitter sentiment was attributed through machine learning using the Google Cloud Natural Language API. The associations between Twitter sentiment and market performance were performed using quantile regressions and vector auto-regression (VAR) models, because the variables of interest are heterogeneous and non-normal, even as relationships can be dynamic.

In the contemporary period, the ISI is positively correlated with stock market returns, but negatively correlated with trading volume. The autoregressive analysis did not confirm the expectation of a dynamic relationship between sentiment and market variables. The quantile analysis showed that the ISI explains the stock market return, however, only at times of lower returns. It is possible to state that this effect is due to the informational content of the tweets (sentiment), and not to the volume of tweets.

The study presents unprecedented evidence for the Brazilian market that investor sentiment can be identified on Twitter, and that this sentiment can be useful for the formation of an investment strategy, especially in times of lower returns. These findings are original and relevant to market agents, such as investors, managers and regulators, as they can be used to obtain abnormal returns.

Recent studies suggested the ratio of option to stock volume reflected the private information. Informed traders were drawn to the options market for its leverage effect and relatively low transaction costs. Informed traders use different intervals of option moneyness to execute their strategies. The question is which types of option moneyness were traded by informed traders and what information was reflected in the market. In this study, the authors focused on this question and constructed a method for capturing the activity of informed traders in the options and stock markets.

The authors constructed the daily measure, moneyness option trading volume to stock trading volume ratio (MOS), to capture the activity of informed traders in the market. The authors formed quintile portfolios sorted with respect to the moneyness option to stock trading volume ratio and provided the capital asset pricing model and Fama–French five-factor alphas. To determine whether MOS had predictive ability on future stock returns after controlling for company characteristic effects, the authors formed double-sorted portfolios and performed Fama–Macbeth regressions.

The authors found that the firms in the lowest moneyness option trading volume to stock trading volume ratio for put quintile outperform the highest quintile by 0.698% per week (approximately 36% per year). The firms in the highest moneyness option trading volume to stock trading volume ratio for call quintile outperform the lowest quintile by 0.575% per week (approximately 30% per year).

The authors first propose the measures, moneyness option trading volume to stock trading volume ratio, that combined with the trading volume and option moneyness. The authors provide evidence that the measures have the predictive ability to the future stock returns.

The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is general market-wide sentiments, while the second is biased approaches toward specific assets.

To achieve the goal, the authors conducted a multi-step analysis of stock returns and constructed complex sentiment indices that reflect the optimism or pessimism of stock market participants. The authors used panel regression with fixed effects and a sample of the US stock market to improve the explanatory power of the three-factor models.

The analysis showed that both market-level and stock-level sentiments have significant contributions, although they are not equal. The impact of stock-level sentiments is more profound than market-level sentiments, suggesting that neglecting the stock-level sentiment proxies in asset valuation models may lead to severe deficiencies.

In contrast to previous studies, the authors propose that investor sentiments should be measured using a multi-level factor approach rather than a single-factor approach. The authors identified two distinct levels of investor sentiment: general market-wide sentiments and individual stock-specific sentiments.

This study aims to analyze and discuss whether and how consumer-to-manufacturer (C2M) mode empowered by e-commerce retail platforms’ big data affects the stock returns of firms in supply chains.

This study selects 195 companies affected by four C2M events as samples and empirically analyzes the impact mechanisms of C2M mode on supply chain firms’ stock returns by event study.

This paper finds that C2M announcements own a positive impact on the stock returns of supply chain firms. Further, the results show that the business and financial characteristics play a significant impact on the relationship between the C2M mode and firm stock return performance. For example, C2M mode leads to huge stock returns when firms cooperate with the platforms related to their business content. In addition, the business scope can strengthen the positive promotion of C2M mode in stock returns, while business concentration weakens the positive promotion of C2M mode in stock returns.

The results found in this paper can provide practical guidance for the firms in supply chains to further apply C2M mode from the business characteristics and financial performance view.

The purpose of this paper is to study the US stock market and try to explain why short-term contrarian profits have largely disappeared in the past two decades.

In this work, the authors decompose the short-term contrarian profits into cross-sectional variations, firm-level overreactions and lead-lag effects to study the changes in their shares. Then, the authors study the behavior of the subgroups in the winner and loser subportfolios of contrarian investment strategies.

The authors find that short-term contrarian profits have largely vanished since 2000. Changes in the shares of the three components of contrarian profits, which are cross-sectional variations, firm-level overreactions and lead-lag effects, are not the main reason for the disappearance of contrarian profits in the past two decades. Instead, the disappearance of short-term contrarian profits is primarily due to the heterogeneous evolution of subgroups in the portfolio, which leads to a decrease in the overall level of overreactions that drive the contrarian profit.

The work explains the disappearance of short-term contrarian profits in the US stock market.

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.

This study aims to examine the influence of stock liquidity on the trade credit of publicly listed companies in Saudi Arabia.

In this study various econometric models were used to test the data of 900 firms listed in Saudi Arabia during the period of 2010–2019.

The robust results of the various econometric models indicate that firms are more willing to offer trade credit to customers when stock liquidity is greater; however, they are less likely to rely on obtaining more payables from suppliers. The findings further indicate that payables and receivables are indeed related, but not exclusively, in the sense that more payables lead to more receivables. The study also reveals a pattern of persistence in payables and receivables during the period of study.

The sample of the present study is only made up of Saudi listed companies. Future research could extend the sample of this study taking into account listed firms in the Middle East and North Africa (MENA) region as a whole so as to gain more insights from the entire region including oil-producing and non–oil-producing countries. More studies are needed to further examine the impact of alternative options for credit access and their linkage to stock liquidity. Finally the difference in difference (DiD) method of analysis as quasi experimental method can be another extension of this research.

The findings would provide implications for managers and investors by recognizing the potential role of stock liquidity in affecting trade credit and understanding the association between the stock liquidity and trade credit. Management of the firms should look for the ways to enhance the stock liquidity of the firms so as to help in reducing the extreme debts usage and therefore, alternative source of funds can be available accordingly. Once the advantage of stock market is identified, firms' managers should search for chances and policies that can promote stock liquidity and hence make use of the advantages of being liquid.

This paper provides new evidence from the emerging market, particularly the Saudi Arabia. The attempt is one of the first in the region to broaden the knowledge about the effects of stock liquidity on trade credit. It provides market participants with insights on the role of stock liquidity in financial flexibility.

Bitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model.

In this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm.

The proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively.

In this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN.