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This paper aims to analyze the role and performance of different artificial intelligence (AI) algorithms in forecasting future movements in the main indices of the world’s largest stock exchanges.

Drawing on finance-based theory, an empirical and experimental study was carried out using four AI-based models. The investigation comprised training, testing and analysis of model performance using accuracy metrics and F1-Score on data from 34 indices, using 9 technical indicators, descriptive statistics, Shapiro–Wilk, Student’s t and Mann–Whitney and Spearman correlation coefficient tests.

All AI-based models performed better than the markets' return expectations, thereby supporting financial, strategic and organizational decisions. The number of days used to calculate the technical indicators enabled the development of models with better performance. Those based on the random forest algorithm present better results than other AI algorithms, regardless of the performance metric adopted.

The study expands knowledge on the topic and provides robust evidence on the role of AI in financial analysis and decision-making, as well as in predicting the movements of the largest stock exchanges in the world. This brings theoretical, strategic and managerial contributions, enabling the discussion of efficient market hypothesis (EMH) in a complex economic reality – in which the use of automation and application of AI has been expanded, opening new avenues of future investigation and the extensive use of technical analysis as support for decisions and machine learning.

The AI algorithms' flexibility to determine their parameters and the window for measuring and estimating technical indicators provide contextually adjusted models that can entail the best possible performance. This expands the informational and decision-making capacity of investors, managers, controllers, market analysts and other economic agents while emphasizing the role of AI algorithms in improving resource allocation in the financial and capital markets.

The originality and value of the research come from the methodology and systematic testing of the EMH through the main indices of the world’s largest stock exchanges – something still unprecedented despite being widely expected by scholars and the market.

This paper aims to focus on exploring and understanding the practice of analyzing the determinants of the Moroccan Dirham by foreign exchange professionals in trading rooms in the context of transitioning to a more flexible regime initiated by Moroccan authorities. The objective of this study is to highlight how foreign exchange operators analyze the determinants of the Moroccan Dirham in the context of exchange rate liberalization, focusing primarily on qualitative data rather than quantitative data.

Therefore, this paper opted for a methodological approach using interview surveys to understand the underlying behavior of Moroccan foreign exchange operators, conducting a content analysis. This paper targeted six foreign exchange operators from nine Moroccan banks authorized as market makers by Bank Al-Maghrib.

The results indicate that the fluctuations of the Moroccan Dirham are closely linked to two main factors: the analysis of the EUR/USD exchange rate and market liquidity analysis. Furthermore, content analysis revealed five essential aspects regarding the practice of analyzing the determinants of the Dirham: “Dirham determinants,” “complementarity between technical analysis and fundamental analysis,” “trends and reversals,” “utility of macroeconomic models” and “psychological factors.”

Certainly, this methodology allows for exploring and understanding the underlying behavior of currency operators but inherently generates a certain degree of subjectivity that can affect the research validity. Indeed, the subjectivity can arise from the responses of the currency operators themselves. They may present the phenomenon coherently or selectively choose the elements they remember to respond to. On the other hand, the validity of this type of research relies on the researcher's ability to cultivate empathy throughout the knowledge creation process. The empathetic stance adopted in this study proved to be complex due to the uniqueness of operators and interaction, sometimes making it challenging to combine empathy, respect and critical thinking (Olivier De Sardan, 2004). Furthermore, the researcher is often faced with an interpretation bias, which can manifest not only during the coding of collected data but also during the analysis of the constructed content. To mitigate this interpretation bias, this paper subjected the collected data to a double coding procedure.

This study aims to narrow the gap in opinions between academics and practitioners by providing a practical overview for change novices.

This study is the pioneering inquiry exploring the process of determining the Moroccan dirham within the transition to a flexible exchange rate regime, using an exploratory methodological approach.

China is a large country with different regions due to regional differences and project characteristics, and the selection of prefabricated building technology according to local conditions is the key to its sustainable development in China. The purpose of this paper is to develop the suitability evaluation system of prefabricated building technology from the perspective of the suitability concept and to analyze the selection path of prefabricated building technology and to provide a reference for selecting and developing prefabricated building technology schemes that meet regional endowments.

Based on relevant literature, technical specifications, and standards, this paper constructs an index system for analyzing the technical suitability of prefabricated buildings. It includes 23 indicators, 7 dimensions, and 3 aspects through the semantic clustering method. Following this, the comprehensive weight of each index is determined using the order relation method (G1) and the continuous ordered weighted averaging (COWA). The selection of technical schemes is comprehensively evaluated using Visekriterjumska Optimizacija Ikompromisno Resenje (VIKOR) and Fuzzy Comprehensive Evaluation Method.

 (1) The technical suitability of prefabricated buildings is influenced by 7 core factors, such as adaptability of resources and environment, project planning and design level, and economic benefit; (2) When selecting the appropriate technology for prefabricated buildings, economic suitability should be considered first, followed by regional suitability, and then technical characteristic; (3) The prefabricated building technology suitability evaluation model constructed in this paper has high feasibility in the technical suitability selection of the example project.

The comprehensive evaluation model of prefabricated building technology suitability constructed in this paper provides technical selection support for the promotion and development of prefabricated buildings in different regions. In addition, the model can also be widely used in areas related to prefabricated building consulting and decision-making, and provides theoretical support for subsequent research.

This study provides a new decision support tool for prefabricated building technology suitability selection, which helps decision makers to make more rational technology choices.

This study has a positive impact on the advancement of prefabricated building technology, the improvement of construction industry standards, and the promotion of sustainable development.

The contribution of this study is twofold: (1) Theoretically, this paper provides technical evaluation indicators and guidelines for provincial and regional governments to cultivate model cities, plan industrial bases, etc. (2) In practice, it offers project-level appropriate technology system solutions for the technology application of assemblers in various regions.

This study aims to determine the contributing factors to technical knowledge valuation at the related quadruple levels of commercialisation, including the idea, benchtop technical knowledge, prototype technical knowledge and commercialised technical knowledge, and then classify the factors by the valuation objectives.

The study method is descriptive-causal, and documentation tools of published scientific research articles in authentic local and international journals were used to extract the contributing factors to technical knowledge valuation. Moreover, the Likert spectrum-based questionnaire is used to determine the weight of each determined component. On the other hand, hierarchical analysis is used based on the extracted results from the distributed classification questionnaire among scholars to determine the allocable weight of each component.

The results indicate that at the idea step, the highest ranks among the contributing factors to technical knowledge valuation are for the indicators of innovation rate enhancement, novelty, creation of new products, profitability growth and dependence decline. In the benchtop technical knowledge step, the indicators of profitability growth, product quality enhancement, novelty, production risk drop, innovation rate enhancement, production costs drop, product price competitiveness and independence from rare machinery have the highest impact coefficients on valuation. Moreover, the prioritisation of factors in prototype technical knowledge shows that the indicators of productive risk decline, infrastructure, decrease in product delivery time, productivity growth and profitability growth are the most critical factors in technical knowledge valuation. Finally, profitability growth factors, production cost drop, productive risk drop, creating a new product, product price competitiveness and dependence decline determine the most valuable technical knowledge in the commercialisation phase.

The most salient innovation of the study involves the development levels of technical knowledge in the commercialisation cycle for determining the contributing factors to technical knowledge valuation and using multivariate decision-making methods to classify the so-called factors. The major limitation can be the context of the study because the paper was carried out by Iranian assessors and specialists using the experiences, opinions and approaches of opinion leaders based on the dominant social, cultural and accounting background of a developing country, not a developed one.

This paper is applicable because it elucidates the technical knowledge valuation factors for managers and owners of technological and knowledge-based companies to facilitate value determination and register the technical knowledge of innovative products in financial statements for the logical presentation of available intangible assets in the economic unit. Besides, in the high-tech area, collecting information from the contributing factors to technical knowledge valuation provides an opportunity to support intellectual property rights and facilitate transaction processes. Finally, in legal areas, in cases of breaching intellectual property rights relative to technical knowledge, the determination of technical knowledge value provides a solid basis for estimating the damage rate.

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.

Information and communication technologies are being increasingly used across various sectors including the tourism industry. However, equitable access to online information remains a significant challenge, especially for people with disabilities (PwD). There is a pressing need for research into the accessibility of the internet to promote social equality. This study aims to identify patterns in both the technical accessibility and the content information related to accessibility and disability that is available on the official websites of leading global tourist destinations.

A cluster analysis assessed the technical accessibility of the websites, while a principal component analysis evaluated the content information concerning accessibility and disability.

There has been a substantial improvement in the technical accessibility of tourism websites over that described in earlier studies. There have been no advances in content information on accessibility and disability, which continues to be very heterogeneous and dispersed.

This evaluation of the technical accessibility and content related to accessibility and disability on tourism websites provides a basis for developing strategies to eliminate barriers that PwD encounter in accessing tourism information. To augment the efficacy of big data inputs, it is imperative to homogenise variables associated with technical access and content information on accessibility. Such standardisation will improve the functionality of algorithms critical to the Internet of Things and artificial intelligence technologies. These enhancements are likely to spur innovations that bridge the inequality gap and promote environments where technology serves as a cornerstone of social inclusion and equality.

信息和通信技术在包括旅游业在内的很多行业的应用越来越广泛。 互联网是游客不可或缺的工具, 但并非每个人（在本研究中为残疾人、PwD）都能以相同的方式获取可用信息。有必要对无障碍使用互联网进行研究, 以促进社会平等。本研究旨在识别全球主要旅游目的地官方网站的技术可及性以及网站内容上有关可及性和残疾信息的规律。

聚类分析评估了网站的技术可及性, 主成成分分析评估了网站的可及性和残疾的相关内容信息。

与早期研究中描述的相比, 旅游网站的技术可访问性有了实质性的改善。关于无障碍和残疾的内容信息没有任何改善, 仍然非常异质性和分散性。

本研究对旅游网站的技术可及性以及有关可及性和残障人士的内容信息的评估为制定以消除残疾人旅游所面临的障碍的未来战略奠定了基础。为了提高大数据输入的有效性, 技术可及性和可及性内容信息相关的变量必须标准化和同质化。这将提高关键算法的效率,以增加物联网和人工智能技术的功能。这些改进可以促进创新, 缩小不平等差距, 并营造让技术成为社会包容和平等基石的环境因素。

Las tecnologías de la información y la comunicación (TIC) se utilizan cada vez más en diversos sectores, incluido el turístico. Sin embargo, el acceso equitativo a la información online sigue siendo un reto importante, especialmente para las personas con discapacidad. Existe una necesidad acuciante de investigar la accesibilidad de Internet para promover la igualdad social. Este estudio identifica patrones en la accesibilidad técnica y en el contenido de la información sobre accesibilidad y discapacidad disponible en las páginas web oficiales de los principales destinos turísticos mundiales.

Un análisis de conglomerados evaluó la accesibilidad técnica y un análisis de componentes principales analizó el contenido de la información sobre accesibilidad y discapacidad en los sitios web.

Se constata una mejora sustancial en la accesibilidad técnica de las páginas web de turismo con respecto a los resultados de estudios anteriores. No ha habido avances en el contenido de la información sobre accesibilidad y discapacidad, que sigue siendo muy heterogénea y dispersa.

Esta evaluación de la accesibilidad técnica y del contenido de la información relativo a la accesibilidad y la discapacidad en las páginas web turísticas proporciona una base para desarrollar estrategias que eliminen las barreras con las que se encuentran las personas con discapacidad para acceder a la información turística. Para mejorar la eficacia de las entradas de big data, es necesario estandarizar las variables relacionadas con la accesibilidad técnica y el contenido de la información sobre accesibilidad. Esta normalización mejorará la funcionalidad de los algoritmos fundamentales para el internet de las cosas y las tecnologías de inteligencia artificial. Es probable que estas mejoras impulsen innovaciones que reduzcan la brecha de la desigualdad y promuevan entornos en los que la tecnología sirva como piedra angular de la inclusión social e igualdad.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

In this chapter, we consider the possibility that a firm may use costly resources to improve its technical efficiency. Results from static analyses imply that technical efficiency is determined by the configuration of factor prices. A dynamic model of the firm is developed under the assumption that managerial skill contributes to technical efficiency. Dynamic analysis shows that the firm can never be technically efficient if it maximizes profits, the steady state is always inefficient, and it is locally stable. In terms of empirical analysis, we show how likelihood-based methods can be used to uncover, in a semi-non-parametric manner, important features of the inefficiency-management relationship using a flexible functional form accounting for the endogeneity of inputs in a production function. Managerial compensation can also be identified and estimated using the new techniques. The new empirical methodology is applied in a data set previously analyzed by Bloom and van Reenen (2007) on managerial practices of manufacturing firms in the UK, US, France and Germany.

This study presents a systematic framework for maintenance strategy development of manufacturing process machinery. The framework is developed based on the reliability-centred maintenance (RCM) approach to minimise the high downtime of a production line, thus increasing its reliability and availability. A case study of a production line from the ghee and soap manufacturing industry in Taiz, Yemen, is presented for framework validation purposes. The framework provides a systematic process to identify the critical system(s) and guide further investigation for functional significant items (FSIs) based on quantitative and qualitative analyses before recommending appropriate maintenance strategies and specific tasks.

The proposed framework integrates conventional RCM procedure with the fuzzy computational process to improve FSIs criticality estimation, which is the main part of failure mode effect criticality analysis (FMECA) applications. The framework consists of four main implementation stages: identification of the critical system(s), technical analysis, Fuzzy-FMECA application for FSIs criticality estimation and maintenance strategy selection. Each stage has its objective(s) and related scientific techniques that are applied to systematically guide the framework implementation.

The proposed framework validation is summarised as follows. The first stage results demonstrate that the seaming system (top and bottom systems) caused 50% of the total production line downtime, indicating it is a critical system that requires further analysis. The outcomes of the second stage provide significant technical information on the subject (seaming system), helping team members to identify and understand the structure and functional complexities of the seaming system. This stage also provides a better understanding of how the seaming system functions and how it can fail. In stage 3, the application of FMECA with the fuzzy computation integration process presents a systematic way to analyse the failure mode, effect and cause of items (components of the seaming system). This stage also includes items’ criticality estimation and ranking assessment. Finally, stage four guides team members in recommending the appropriate countermeasures (maintenance strategies and task selection) based on their priority level.

This paper proposes an original maintenance strategies development framework based on the RCM approach for production system equipment. Specifically, it considers a fuzzy computational process based on the Gaussian function in the third stage of the proposed framework. Adopting the fuzzy computational process improves the risk priority number (RPN) estimation, resulting in better criticality ranking determination. Another significant contribution is introducing an extended item criticality ranking assessment process to provide maximum levels of criticality item ranking. Finally, the proposed RCM framework also provides detailed guidance on maintenance strategy selection based on criticality levels, unique functionality and failure characteristics of each FSI.

Efficiency and quality are primary factors for the survival of health systems. The evaluation of the efficiency of the healthcare system is a crucial component of promoting long-term health policy actions. Healthcare capacity indicators provide a basis for evaluating and comparing the performance of different healthcare organizations. Intrinsic quality indicators are Donabedian (1980)’s structural and process elements of quality of healthcare. This study aims to integrate capacity and intrinsic quality indicators of healthcare while measuring the efficiency of provinces by using radial and non-radial efficiency measurement techniques.

Efficiency analysis performed in Turkey from 2015 to 2020 by performing input-oriented radial, nonradial, and super-efficiency estimates for 81 provinces of Turkey by incorporating capacity and intrinsic quality indicators into the different model specifications.

Radial and nonradial efficiency results have an increasing trend over the study years obtained from the efficiency models showing high average scores obtained from the models that include intrinsic quality of care indicators. Statistically significant mean rank differences are observed between different radial efficiency models for all study years (p < 0.001). Negative and moderate level correlations were observed between radial efficiency results and quality of care indicators (r < 0.70).

Under long-term centralized health policies, increases in efficiency result in decreased intrinsic quality of care indicators. A better synthesis of health system capacity and intrinsic healthcare quality indicators is necessary to generate evidence-based health systems.