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Using data from business reorganization processes under Act 1116 of 2006 in Colombia during the period 2008 to 2018, a model for predicting the success of these processes is proposed. The paper aims to validate the model in two different periods. The first one, in 2019, characterized by stability, and the second one, in 2020, characterized by the uncertainty generated by the COVID-19 pandemic.

A set of five financial variables comprising indebtedness, profitability and solvency proxies, firm age, macroeconomic conditions, and industry and regional dummies are used as independent variables in a logit model to predict the failure of reorganization processes. In addition, an out-of-sample analysis is carried out for the 2019 and 2020 periods.

The results show a high predictive power of the estimated model. Even the results of the out-of-sample analysis are satisfactory during the unstable pandemic period. However, industry and regional effects add no predictive power for 2020, probably due to subsidies for economic activity and the relaxation of insolvency legislation in Colombia during that year.

In a context of global reform in insolvency laws, the consistent predictive ability shown by the model, even during periods of uncertainty, can guide regulatory changes to ensure the survival of companies entering into reorganization processes, and reduce the observed high failure rate.

Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction.

Specific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques.

The experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL).

It should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies.

The authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.

Using intraday data, the authors explore the forecast ability of one high frequency order flow imbalance measure (OI) based on the volume-synchronized probability of informed trading metric (VPIN) for predicting the realized volatility of the index futures on the China Securities Index 300 (CSI 300).

The authors employ the heterogeneous autoregressive model for realized volatility (HAR-RV) and compare the forecast ability of models with and without the predictive variable, OI.

The empirical results demonstrate that the augmented HAR model incorporating OI (HARX-RV) can generate more precise forecasts, which implies that the order imbalance measure contains substantial information for describing the volatility dynamics.

The study sheds light on the relation between high frequency trading behavior and volatility forecasting in China's index futures market and reveals the underlying market mechanisms of liquidity-induced volatility.

The primary objective of this research is to explore the potential of utilizing Global Consciousness Project (GCP) data as a tool for understanding and predicting market sentiment. Specifically, the study aims to assess whether incorporating GCP data into econometric models can enhance the comprehension of daily market movements, providing valuable insights for traders.

This study employs econometric models to investigate the correlation between the Standard & Poor's 500 Volatility Index (VIX), a common measure of market sentiment and data from the GCP. The focus is particularly on the largest daily composite GCP data value (Max[Z]) and its significant covariation with changes in VIX. The research employs interaction terms with VIX and daily returns from global markets, including Europe and Asia, to explore the relationship further.

The results reveal a significant relationship with the GCP data, particularly Max[Z] and VIX. Interaction terms with both VIX and daily returns from global markets are highly significant, explaining about one percent of the variance in the econometric model. This finding suggests that variations in GCP data can contribute to a better understanding of market dynamics and improve forecasting accuracy.

One limitation of this study is the potential for overfitting and P-hacking. To address this concern, the models undergo rigorous testing in an out-of-sample simulation study lasting for a predefined one-year period. This limitation underscores the need for cautious interpretation and application of the findings, recognizing the complexities and uncertainties inherent in market dynamics.

The study explores the practical implications of incorporating GCP data into trading strategies. Econometric models, both with and without GCP data, are subjected to an out-of-sample simulation where an artificial trader employs S&P 500 tracking instruments based on the model's one-day-ahead forecasts. The results suggest that GCP data can enhance daily forecasts, offering practical value for traders seeking improved decision-making tools.

Utilizing data from the GCP is found to be advantageous for traders as noteworthy correlations with market sentiment are found. This unanticipated finding challenges established paradigms in both economics and consciousness research, seamlessly integrating these domains of research. Traders can leverage this innovative tool, as it can be used to refine forecasting precision.

This paper aims to introduce a new perspective on long-term stock return predictability by focusing on the relative (individual and hybrid) informative power of a wide range of accounting (firm-related), technical and macroeconomic factors while considering the past performance of the stocks using machine learning algorithms.

The sample includes a panel data set of 94 non-financial firms listed in Egyptian Exchange 100 index from 2014: Q1 to 2019: Q4. Relativity has been investigated by comparing relevant factors’ individual and combined informative power and differentiating between losers and winners based on historical stock returns. To predict the quarterly stock returns, Gaussian process regression (GPR) has been used. The robustness of the results is examined through the out-of-sample test. This study also uses linear regression (LR) as a benchmark model.

The past performance and the presence of other predictors influence the informative power of relevant factors and hence their predictive ability. The out-of-sample results show a trade-off between GPR and LR with proven superiority to GPR in limited experiments. The individual informative power outperforms the hybrid power, in which macroeconomic indicators outperform the remaining sets of indicators for losers, while winners show mixed results in terms of various performance evaluation metrics. Prediction accuracy is generally higher for losers than for winners.

This study provides interesting insight into the dynamic nature of the predictor variables in terms of stock return predictability. Hence, this study also deepens the understanding of asset pricing in a way that directly contributes to practitioners’ portfolio diversification strategies.

In concern of the chaos of factors in the literature and its accompanying misleading conclusions, this study takes another look at the approach that studies stock return predictability. To the best of the authors’ knowledge, this is the first study in the Egyptian context that re-examines the predictive power of the previously discovered factors from a different perspective that highlights their relative nature.

The paper models the financial interconnectedness and systemic risk of shadow banks using Granger-causal network-based measures and takes the Indian shadow bank crisis of 2018–2019 as a systemic event.

The paper employs pairwise linear Granger-causality tests adjusted for heteroskedasticity and return autocorrelation on a rolling window of weekly returns data of 52 financial institutions from 2016 to 2019 to construct network-based measures and calculate network centrality. The Granger-causal network-based measure ranking of financial institutions in the pre-crisis period (explanatory variable) is rank-regressed with the ranking of financial institutions based on maximum percentage loss suffered by them during the crises period (dependent variable).

The empirical result demonstrated that the shadow bank complex network during the crisis is denser, more interconnected and more correlated than the tranquil period. The closeness, eigenvector, and PageRank centrality established the systemic risk transmitter and receiver roles of institutions. The financial institutions that are more central and hold prestigious positions due to their incoming links suffered maximum loss. The shadow bank network also showed small-world phenomena similar to social networks. Granger-causal network-based measures have out-of-sample predictive properties and can predict the systemic risk of financial institutions.

The study considers only the publicly listed financial institutions. Also, the proposed measures are susceptible to the size of the rolling window, frequency of return and significance level of Granger-causality tests.

Supervisors and financial regulators can use the proposed measures to monitor the development of systemic risk and swiftly identify and isolate contagious financial institutions in the event of a crisis. Also, it is helpful to policymakers and researchers of an emerging economy where bilateral exposures' data between financial institutions are often not present in the public domain, plus there is a gap or delay in financial reporting.

The paper is one of the first to study systemic risk of shadow banks using a financial network comprising of commercial banks and mutual funds. It is also the first one to study systemic risk of Indian shadow banks.

Given the importance of credit rating agencies’ (CRAs) assessment in affecting international financial markets, it is useful for policymakers and investors to be able to forecast it properly. Therefore, this study aims to forecast sovereign risk perception of the main agencies related to Brazilian bonds through the application of different machine learning (ML) techniques and evaluate their predictive accuracy in order to find out which one is best for this task.

Based on monthly data from January 1996 to November 2018, we perform different forecast analyses using the K-Nearest Neighbors, the Gradient Boosted Random Trees and the Multilayer Perceptron methods.

The results of this study suggest the Multilayer Perceptron technique is the most reliable one. Its predictive accuracy is relatively high if compared to the other two methods. Its forecast errors are the lowest in both the out-of-sample and in-sample forecasts’ exercises. These results hold if we consider the CRAs classification structure as linear or logarithmic. Moreover, its forecast errors are not statistically associated with periods of changes in CRAs’ opinion of any sort.

To the best of the authors’ knowledge, this study is the first to evaluate the performance of ML methods in the task of predicting sovereign credit news, including not only the sovereign ratings but also the outlook and credit watch status. In addition, the authors investigate whether the forecasts errors are statistically associated with periods of changes in sovereign risk perception.

Existing empirical evidence suggests that episodes of financial stress (crises) can act as driver of growth of inequality. Consequently, in this study, the authors explore the time-varying predictive power of an index of financial stress for growth in income (and consumption) inequality in the UK. The authors focus on the UK since income (and consumption) inequality data are available at a high frequency, i.e. on a quarterly basis for over 40 years (June, 1975 to March, 2016).

The authors use Wang and Rossi's approach to analyze the time-varying impact of financial stress on inequality. Hence, the method provides a more appropriate inference of the effect rather than a constant parameter Granger causality method. Besides, understandably, the time-varying approach helps to depict the time-variation in the strength of predictability of financial stress on inequality.

This study’s findings point that financial distress correspond to subsequent increases in inequality, with the index of financial stress containing important information in predicting growth in income inequality for both in and out-of-sample periods. Interestingly, the strength of the in-sample predictive power is high post the period of the global financial crisis, as was observed in the early part of the sample. The authors believe these findings highlight an important role of financial stress for inequality – an area of investigation that has in general remained untouched.

Accurate prediction of inequality at a higher frequency should be more relevant to policymakers in designing appropriate policies to circumvent the wide-ranging negative impacts of inequality, compared to when predictions are only available at the lower annual frequency.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

The authors extend the World Bank's Logistics Performance Index (LPI) for 30 additional countries and 13 additional years. The authors develop an inexpensive method for extending survey data when frequent, universal surveys are unavailable. The authors identify groups of country characteristics that influence LPI scores.

Using data from the World Development Indicators—the broadest global dataset of country socioeconomic features—the authors test machine learning algorithms for their ability to predict the LPI. The authors examine importance scores to identify factors that influence LPI scores.

The best performing algorithm produces predictions on unseen data that account for nearly 90% of observed variation, and are accurate to within 6%. It performs twice as well as an OLS model with per capita income as the only predictor. Explanatory factors are business environment, economic structure, finance, environment, human development, and institutional quality.

Machine learning offers a simple, inexpensive way of extending the coverage of survey data. This dataset provides a richer picture of logistics performance around the world. The factors the authors identify as predicting higher LPI scores can help policymakers and practitioners target interventions.

This paper is one of the first applications of machine learning to extend coverage of an index based on an international survey. The authors use the new data to provide the most wide-ranging analysis of logistics performance across countries and over time. The output is an important resource for policymakers tracking performance, and researchers particularly in smaller and lower income countries. The authors also examine a wider range of explanatory factors for LPI scores than previous work.