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The stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors frequently use technical analysis or fundamental analysis. Data mining techniques coupled with fundamental and technical analysis types have the potential to give satisfactory results for stock market prediction. In the current paper, an effort is made to investigate the accuracy of stock market predictions by using the combined approach of variables from technical and fundamental analysis for the creation of a data mining predictive model.

We chose 381 companies from the National Stock Exchange of India's CNX 500 index and conducted a two-stage data analysis. The first stage is identifying key fundamental variables and constructing a portfolio based on that study. Artificial neural network (ANN), support vector machines (SVM) and decision tree J48 were used to build the models. The second stage entails applying technical analysis to forecast price movements in the companies included in the portfolios. ANN and SVM techniques were used to create predictive models for all companies in the portfolios. We also estimated returns using trading decisions based on the model's output and then compared them to buy-and-hold returns and the return of the NIFTY 50 index, which served as a benchmark.

The results show that the returns of both the portfolios are higher than the benchmark buy-and-hold strategy return. It can be concluded that data mining techniques give better results, irrespective of the type of stock, and have the ability to make up for poor stocks. The comparison of returns of portfolios with the return of NIFTY as a benchmark also indicates that both the portfolios are generating higher returns as compared to the return generated by NIFTY.

As stock prices are influenced by both technical and fundamental indicators, the current paper explored the combined effect of technical analysis and fundamental analysis variables for Indian stock market prediction. Further, the results obtained by individual analysis have also been compared. The proposed method under study can also be utilized to determine whether to hold stocks for the long or short term using trend-based research.

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.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

This paper aims to improve the life of the printed circuit boards (PCB) used in computers based on modal analysis by increasing the natural frequency of the PCB assembly.

In this work, through experiments and numerical simulations, an attempt has been made to increase the fundamental natural frequency of the PCB assembly as high as practically achievable so as to minimize the impacts of dynamic loads acting on it. An optimization tool in the finite element software (ANSYS) was used to search the specified design space for the optimal support location of the six fastening screws.

It is observed that by changing the support locations based on the optimization results the fundamental natural frequency can be raised up to 51.1% and the same is validated experimentally.

Manufacturers of PCBs used in computers fix the support locations based on symmetric feature of the board not on the dynamic behavior of the assembly. This work might lead manufacturers to redesign the location of other surface mount components.

This work provides guidelines for PCB manufacturers to finalize their support locating points which will improve the dynamic characteristics of the PCB assembly during its functioning.

This study provides a novel method to improve the life of PCB based on support locations optimization which includes majority of the surface mount components that contributes to the total mass the PCB assembly.

This work compares the performance of the three boundary element techniques for solving Helmholtz problems: dual reciprocity, multiple reciprocity and direct interpolation. All techniques transform domain integrals into boundary integrals, despite using different principles to reach this purpose.

Comparisons here performed include the solution of eigenvalue and response by frequency scanning, analyzing many features that are not comprehensively discussed in the literature, as follows: the type of boundary conditions, suitable number of degrees of freedom, modal content, number of primitives in the multiple reciprocity method (MRM) and the requirement of internal interpolation points in techniques that use radial basis functions as dual reciprocity and direct interpolation.

Among the other aspects, this work can conclude that the solution of the eigenvalue and response problems confirmed the reasonable accuracy of the dual reciprocity boundary element method (DRBEM) only for the calculation of the first natural frequencies. Concerning the direct interpolation boundary element method (DIBEM), its interpolation characteristic allows more accessibility for solving more elaborate problems. Despite requiring a greater number of interpolating internal points, the DIBEM has presented higher-quality results for the eigenvalue and response problems. The MRM results were satisfactory in terms of accuracy just for the low range of frequencies; however, the neglected higher-order primitives impact the accuracy of the dynamic response as a whole.

There are safe alternatives for solving engineering stationary dynamic problems using the boundary element method (BEM), but there are no suitable comparisons between these different techniques. This paper presents the particularities and detailed comparisons approaching the accuracy of the three important BEM techniques, aiming at response and frequency evaluation, which are not found in the specialized literature.

The purpose of this study is to examine factors contributing to family firms’ survival in the ongoing COVID-19 crisis; in this endeavour, the study espouses the underpinnings of social exchange theory and entrepreneurial resilience.

The views of 128 Italian family micro and small-sized firm owners/managers operating in different industries were gathered through an online questionnaire.

The analysis uncovers 12 fundamental factors contributing to firms’ survival; these are encapsulated in three dimensions and presented in two theoretical frameworks. The “beneficiary” dimension stresses the support from various internal and external stakeholders, while the “benefactor” dimension illustrates the commitment to extend the family tradition and be responsive to stakeholders. Finally, the “immersion/embeddedness” dimension denotes firms’ entrepreneurial behaviour, agility, decision-making and drive.

Firstly, and from a practitioner perspective, this study addresses recognised knowledge and research gaps in contemporary family business research, including how family firms are confronting the current unprecedented crisis. This response to current extant gaps provides first-hand empirical findings that could be primarily considered by industry stakeholders. Secondly, and from a theoretical angle, the aforementioned dimensions revealed through the analysis, coupled with the development of a theoretical framework, contribute to conceptual rigour and, therefore, a deeper understanding of family firms’ journey through an unprecedented event.

Building on new trends in tourism and smart city governance, this study aims to examine the degree of interrelation between stakeholder networks involved in tourism governance and smart city development. A model describing the transition towards smart tourism city governance is proposed.

The proposed model is tested through a multiple case study of seven European cities. This choice of sample makes the study highly representative. Data collection is based on an exhaustive search and analysis of available data on smart city initiatives, destination management organisations and tourism plans. Social network analysis using Gephi software is used to build stakeholder networks.

Analysis of the stakeholder networks that shape tourism governance and smart initiatives in several cities reveals a disconnection between the two types of networks. The results show limited progress towards the expected synergies of true smart tourism city governance.

Theoretically, the study contributes to the debate on new forms of governance for the complex evolution of urban tourism. In practice, the relationship between tourism governance and smart city initiatives needs to be redefined to achieve synergies that increase the inclusiveness and efficiency of urban tourism policies.

This study examines the under-researched topic of the interrelation between tourism governance and smart city initiatives. By comparing the networks of actors resulting from these two processes, it assesses the extent to which this interrelation helps the emergence of new governance models (smart tourism city governance).

This study aims to analyse the consistency between what companies say (talk) and what they do (walk) regarding the application of double materiality in their sustainability reports.

Sustainability reports of 76 European companies that reported the application of double materiality and are listed in the Dow Jones Sustainability Index were studied through content analysis.

In total, 67% of the companies studied claim to apply double materiality but do not comply with the guidelines in this respect proposed by the European Financial Reporting Advisory Group. Therefore, these companies should be considered label adopters.

This study presents evidence of the existence of label adopters when double materiality is adopted at an early stage, meaning that regulators should seek to control compliance with the minimum requirements established for double materiality. This finding also has implications for assurers, who should consider the degree of real compliance with double materiality requirements when expressing their opinion.

The existence of label adopters in the application of double materiality endangers the sustainable development pursued through agreements such as the Green Deal and through the Sustainable Finance policy proposed in Europe.

This work contributes to the emerging literature on double materiality. Unlike previous works, empirical evidence is provided on the changes that companies present in their material issues with the application of double materiality. Moreover, it confirms the existence of label adopters in the application of double materiality.

To simplify the circuit design and control complexity of the magnetic coupling resonant wireless charging system, the radio energy transmission constant current and constant voltage charging is realized.

The purpose of this study is to simplify the circuit design and control complexity of the magnetic coupling resonance wireless charging system, in order to achieve constant current and constant voltage charging for wireless energy transmission. First, the principle of LCC/S-S compensation structure is analyzed, and the equivalent mathematical model is established; then, the system characteristics under constant current and constant voltage mode are analyzed, and the design method of system parameters is given; finally, a simulation and experimental system is built to verify the correctness and feasibility of the theoretical analysis.

The results show that the proposed hybrid topology can achieve a constant current output of 2 A and a constant voltage output of 30 V under variable load conditions, and effectively suppress the current distortion problem under light load conditions. The waveform distortion rate of the inverter current is reduced from 33.97% to 10.45%.

By changing the high-order impedance characteristics of the compensation structure, the distortion of the current waveform under light load is suppressed, and the overall stability and efficiency of the system are improved.

This paper examines the impact of sharia-compliant debt financing on stock price crash risk. Unlike those previous studies that took Sukuk or sharia-compliant firms, this study tests the impact of the proportion reported sharia-compliant debt financing in the balance sheet on the risk of price crash of a firm.

Using the data from 2,752 firm-year observations of 344 Malaysian non-financial listed companies from 2012 to 2019, this article used a robust panel data estimation technique for statistical inferences. This study also employs panel GMM and quantile least squares as the robustness check.

This study established a negative relationship between sharia-compliant debt financing and stock price crash risk. The robustness checks with different estimation techniques confirm the results. It implies that firms with a more significant proportion of Sharia-compliant financing tend to have lower future stock price crash risk.

Consistent with the Islamic finance literature, the present study contributes to the existing literature on Islamic capital markets from the perspective of stock price crash risk because it is vital for risk management and investment decision-making as a measure of tail risk for stocks. The findings of this research will assist investors in developing portfolio strategies that incorporate firms with higher levels of sharia-compliant debt financing in their balance sheets. Additionally, the results of this study suggest that policymakers and regulatory bodies should consider revising their monitoring approaches for publicly listed firms.

This study is interesting and unique, as it is a pioneer in testing the impact of sharia-compliant debt financing on reducing stock price crash risk.