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The purpose of this paper is to present a new constrained optimization algorithm based on a particle swarm optimization (PSO) algorithm approach.

This paper introduces a hybrid approach based on a modified ring neighborhood with two new perturbation operators designed to keep diversity. A constraint handling technique based on feasibility and sum of constraints violation is adopted. Also, a special technique to handle equality constraints is proposed.

The paper shows that it is possible to improve PSO and keeping the advantages of its social interaction through a simple idea: perturbing the PSO memory.

The proposed algorithm shows a competitive performance against the state‐of‐the‐art constrained optimization algorithms.

The proposed algorithm can be used to solve single objective problems with linear or non‐linear functions, and subject to both equality and inequality constraints which can be linear and non‐linear. In this paper, it is applied to various engineering design problems, and for the solution of state‐of‐the‐art benchmark problems.

A new neighborhood structure for PSO algorithm is presented. Two perturbation operators to improve PSO algorithm are proposed. A special technique to handle equality constraints is proposed.

This paper investigates how various strategies for combining forecasts, both simple and optimised approaches, are compared with popular individual risk models in estimating value-at-risk (VaR) and expected shortfall (ES) in emerging market at alternative risk levels.

Using the case study of the Vietnamese stock market, the author produced one-day-ahead VaR and ES forecast from seven individual risk models and ten alternative forecast combinations. Next, the author employed a battery of backtesting procedures and alternative loss functions to evaluate the global predictive accuracy of the different methods. Finally, the author investigated the relative performance over time of VaR and ES forecasts using fluctuation test.

The empirical results indicate that, although combined forecasts have reasonable predictive abilities, they are often outperformed by one individual risk model. Furthermore, the author showed that the complex combining methods with optimised weighting functions do not perform better than simple combining methods. The fluctuation test suggests that the poor performance of combined forecasts is mainly due to their inability to cope with periods of instability.

This study reveals the limitation of combining strategies in the one-day-ahead VaR and ES forecasts in emerging markets. A possible direction for further research is to investigate whether this finding holds for multi-day ahead forecasts. Moreover, the inferior performance of combined forecasts during periods of instability motivates further research on the combining strategies that take into account for potential structure breaks in the performance of individual risk models. A potential approach is to improve the individual risk models with macroeconomic variables using a mixed-data sampling approach.

First, the authors contribute to the literature on the forecasting combinations for VaR and ES measures. Second, the author explored a wide range of alternative risk models to forecast both VaR and ES with recent data including periods of the COVID-19 pandemic. Although forecast combination strategies have been providing several good results in several fields, the literature of forecast combination in the VaR and ES context is surprisingly limited, especially for emerging market returns. To the best of the author’s knowledge, this is the first study investigating predictive power of combining methods for VaR and ES in an emerging market.

Using various proxies for the firms' return on equity (ROE) and retention ratios (b) the authors calculate 36 sustainable growth rates, on a rolling basis, for a comprehensive set of firms over a 52-year period. The authors then assess the ability of these different sustainable growth rates to predict the actual, out-of-sample, five-year growth rates of the firms' earnings.

The authors compare the forecast to determine which method of estimating ROE and b produce the lowest mean-squared-errors and then determine the estimation method that works best for firms with different characteristics and for firms in different industries.

Overall, using the median ROE of all firms in the market and the 5-year average of the specific firm's retention ratio produces the lowest, statistically significant, forecast errors. Variations are documented based on firm characteristics, including dividend payout, level of ROE and industry.

The findings can guide practitioners in using the best earnings forecasting method.

Financial textbooks seem universally to suggest that one method of estimating the growth rate of a firm's earnings is to calculate the “sustainable growth rate” by multiplying the firm's ROE by the firm's b. At the same time, multiple methods of proxying for both ROE and b have been suggested; therefore, it is an interesting and useful empirical question, which, heretofore, has not been addressed in the literature, as to which estimation of the sustainable growth rate best approximates the actual future growth of the firm's earnings. The findings can guide practitioners in using the best earnings forecasting method.

The purpose of the article is to show the changing behavior of investors in the post-pandemic period, the continued development of “emotional communities” in the financial market, as well as the factors contributing to their formation and the role of such communities in the elaboration of investors' decisions.

The research includes an analysis of the popularity of various terms searched in the US segment of Google in the financial category from 2004 to 2022, their correlation with financial market indicators and theoretical observations around these data.

The results obtained by the author allow him to draw the following conclusions: (1) the change in investors' behavior indicates the formation of the new distributed community-centric model of the financial market; (2) the main distinguishing feature of the behavior of many retail investors is gamification; (3) the networking of investors contributes to a significant change in their priorities in the elaboration of investment decisions; (4) the fundamental indicators of the financial market play an ever decreasing role in the decision-making of individual investors.

To the best of the author's knowledge, the formation of emotional communities of investors and their role in the elaboration of mass investor decisions is not widely covered in the literature. The paper develops a framework for further studies on the role of emotional communities in the financial market and in changing behavior of retail investors.

The purpose of this paper is to perform Shariah review of Brownian motion that is used for prediction of Islamic stock prices and their volatility.

It uses the Shariah compliant development model guidelines to review the Brownian motion and its applications.

The model of Brownian motion does not involve any variable that renders it non-Shariah compliant; neither all applications of Brownian motion are Shariah compliant. Because the model is based on stochastic properties that involve randomness, therefore the issue of gharar takes the utmost important to handle in the applications of the model. The results need to be analyzed strictly in accordance with the Shariah whether they create any element of gharar or uncertainty in case of expected price and volatility estimates.

The research suffers from the limitation that it analyses only one model of physics, i.e. Brownian motion model from Shariah perspective.

The research opens an area for Shariah analysis of results generated from the application of advanced models of physics on matters related to Islamic financial markets.

The originality of this study stems from the fact that to the best of the authors’ knowledge, it is the first study that extends Shariah guidelines into Financial physics for making the foundations of Islamic econophysics.

The purpose of this paper is to determine the impact of corporate investments in corporate social responsibility (CSR), measured by the environmental, social and government (ESG) rating, on the market valuation of a firm's stocks and to explain the regional differences in the degree of this influence.

The empirical study uses linear and non-linear panel regression models for a panel sample of 951 firms listed in Asia, North America and Europe operating in innovative industries.

The CSR score was found to be significant in terms of stock excess return on the regional level. However, this finding cannot be extrapolated to the global scale. ESG rating is priced by the European and North American markets negatively, while in the Asian market, it is positive. This penalty (negative influence) is greater than the reward for one point increase in ESG rating.

The results of this empirical study could be used by firms' managers to adjust strategies aimed at stock value growth and by investors to select an investment strategy to maximize return.

The impact of investments in CSR on stock excess return over a defined benchmark is assessed. The study reveals regional differences in the impact of CSR investment using a sample of Asian, European and North American firms. The authors apply a more advanced lagged CSR performance (d.ESG) assessment based on the methodology of Zhang and Rajagopalan (2010).

This work aims at designing an indicator for detecting and forecasting price volatility and speculative bubbles in three markets dealing with agricultural and soft commodities, i.e. Intercontinental Exchange Futures market Europe, (IFEU), Intercontinental Exchange Futures market United States (IFUS) and Chicago Board of Trade (CBOT). This indicator, designed as a demand/supply odds ratio, intends to overcome the subjectivity limits embedded in sentiment indexes as the Bull and Bears ratio by the Bank of America Merrill Lynch.

Data evidence allows for the parameter estimation of a Jacobi diffusion process that models the demand share and leads the forecast of speculative bubbles and realised volatility. Validation of outcomes is obtained through the dynamic regression with autoregressive integrated moving average (ARIMA) error. Results are discussed in comparison with those from the traditional generalized autoregressive conditional heteroskedasticity (GARCH) models. The database is retrieved from Thomson Reuters DataStream (nearby futures daily frequency).

The empirical analysis shows that the indicator succeeds in capturing the trend of the observed volatility in the future at medium and long-time horizons. A comparison of simulations results with those obtained with the traditional GARCH models, usually adopted in forecasting the volatility trend, confirms that the indicator is able to replicate the trend also providing turning points, i.e. additional information completely neglected by the GARCH analysis.

The authors' commodity demand as discrete-time process is capable of replicating the observed trend in a continuous-time framework, as well as turning points. This process is suited for estimating behavioural parameters of the agents, i.e. long-term mean, speed of mean reversion and herding behaviour. These parameters are used in the forecast of speculative bubbles and realised volatility.

This study aims to explore the connectedness of price return index spillovers across eight economic sectors in the Malaysian stock market (Bursa Malaysia).

The analysis uses daily data of sectoral price index from 10 May 2005 to 24 February 2021. The study uses Bayesian time-varying parameter vector autoregressive.

The degree of price return index spillovers varies over time, reaching unprecedented heights during the COVID-19 pandemic in 2020. The industrial economic sector is the main transmitter of price index return shock, whereas the utilities economic sector is the dominant receiver of index return spillovers.

The findings are critical for investors, market participants, businesses and policymakers in developing action plans for the vulnerable sectors. It further enhances investors’ confidence in making investment decisions.

It has been demonstrated in the US market that expected market excess returns can be predicted using the average higher-order moments of all firms. This study aims to empirically test this theory in emerging markets.

Two measures of average higher moments have been used (equal-weighted and value-weighted) along with the market moments to predict subsequent aggregate excess returns using the linear as well as the quantile regression model.

The authors report that both equal-weighted skewness and kurtosis significantly predict subsequent market returns in two countries, while value-weighted average skewness and kurtosis are significant in predicting returns in four out of nine sample markets. The results for quantile regression show that the relationship between the risk variable and aggregate returns varies along the spectrum of conditional quantiles.

This is the first study that investigates the impact of third and fourth higher-order average realized moments on the predictability of subsequent aggregate excess returns in the MSCI Asian emerging stock markets. This study is also the first to analyze the sensitivity of future market returns over various quantiles.

Heuristics are a less complex and more understandable way to a more straightforward, astute and brisk basic decision-making strategy. The purpose of this study is the development of a rule of thumb called the “Crocodile rule” based on downside risk.

The crocodile rule is developed and tested in two steps by using data in the form of stock portfolios of the Pakistan Stock Exchange from January 2000 to November 2017. In the first phase of the study, researchers have forecasted the probabilities, while in the second phase, the researchers have used these probabilities to test the crocodile rule.

The findings show the acceptance of the null hypothesis, forecasting error for all categories of stocks for the first phase. The results also show that the minimum recovery chance is 58%, and the maximum recovery chance is 81% with an overall average of 69% chance of recovery. All recovery probabilities are above 50% for all portfolios; this is particularly impressive for a volatile market like Pakistan.

The study also proposes another performance measure such as “value-at-risk” and compare it with present results to yield better outcomes. Furthermore, other categories of stock like profitability and growth can be tested as well.

The practical application of this rule is a choice between a “Buy-and-hold” strategy and showing myopic behavior as another extreme.

This pioneering research focuses on the development of the “Crocodile rule” by using the lower partial moments as a proxy of downside risk. This research adds value to the existing literature on performance measures. Furthermore, it also highlights and indicates which strategy should be used by the investors in case of falling trends in the market.