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Today, energy sustainability, which is one of the most significant concerns in the energy industry, is of utmost importance. In this context, investments and interest in renewable energy sources are growing. As a nation with vast wind energy potential, Türkiye is at the forefront of expanding investments in this sector. This study highlights the significance of wind power plants in electricity market and the relevance of wind energy forecasts, as well as the significance of ensuring the imbalance in energy supply and enhancing electricity market stability. Parallel to this, the transient system simulation (TRNSYS) model was used to determine annual energy generation of a wind power plant in Izmir with a capacity of 18 MW, and the obtained results were compared with the real-time generation data from EPİAŞ transparency platform. The model had two approaches, one based on standard data from the second generation of a typical meteorological year (Plan (1)), and the other on actual field data collected in the plant (Plan (2)). The numerical findings indicate that the annual energy generation values for Plan (1) and Plan (2) are 24,018.1 MWh and 61,699.1 MWh, respectively. Additionally, the real-time production yields a total of 60,176.2 MWh. In a meantime, Plan (1) generated a positive imbalance value of 45,726.7 MWh, whereas Plan (2) has 6,651.3 MWh over the course of one year. In contrast, the annual sum of negative imbalance values was determined to be 9,475.9 MWh for Plan (1) and 8,368.6 MWh for Plan (2). The analysis yielded annual figures of 2,379,110.4 TL and 351,318.3 TL for positive and negative imbalance penalties, respectively, for Plan (1). For Plan (2), the corresponding amounts were 310,875.9 TL and 337,186.4 TL. Consequently, the total penalty payments for Plan (1) amounted to 2,730,428.8 TL, while for Plan (2) it reached 648,062.3 TL. [ABSTRACT FROM AUTHOR]

This study assesses the application of artificial intelligence (AI) algorithms for optimizing resource allocation, demand-supply matching, and dynamic pricing within circular economy (CE) digital marketplaces. Five AI models—autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), random forest (RF), gradient boosting regressor (GBR), and neural networks (NNs)—were evaluated based on their effectiveness in predicting waste generation, economic growth, and energy prices. The GBR model outperformed the others, achieving a mean absolute error (MAE) of 23.39 and an R 2 of 0.7586 in demand forecasting, demonstrating strong potential for resource flow management. In contrast, the NNs encountered limitations in supply prediction, with an MAE of 121.86 and an R 2 of 0.0151, indicating challenges in adapting to market volatility. Reinforcement learning methods, specifically Q-learning and deep Q-learning (DQL), were applied for price stabilization, resulting in reduced price fluctuations and improved market stability. These findings contribute a conceptual framework for AI-driven CE marketplaces, showcasing the role of AI in enhancing resource efficiency and supporting sustainable urban development. While synthetic data enabled controlled experimentation, this study acknowledges its limitations in capturing full real-world variability, marking a direction for future research to validate findings with real-world data. Moreover, ethical considerations, such as algorithmic fairness and transparency, are critical for responsible AI integration in circular economy contexts. [ABSTRACT FROM AUTHOR]

The System Marginal Price (SMP) is the cost of the last unit of electricity supplied to the grid, reflecting the supply–demand equilibrium and serving as a key indicator of market conditions. Accurate SMP forecasting is essential for ensuring market stability and economic efficiency. This study addresses the challenges of SMP prediction in Turkey by proposing a comprehensive forecasting framework that integrates machine learning, deep learning, and statistical models. Advanced feature selection techniques, such as Minimum Redundancy Maximum Relevance (mRMR) and Maximum Likelihood Feature Selector (MLFS), are employed to refine model inputs. The framework incorporates time series methods like Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional LSTM (ConvLSTM) to capture complex temporal patterns, alongside models such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM) for modeling non-linear relationships. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) across regular weekdays, weekends, and public holidays. XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating exceptional accuracy and robustness. Among all of the models, XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating superior accuracy and robustness. The results highlight the inadequacy of traditional models like ARIMA and SARIMA in capturing non-linear and highly volatile patterns, reinforcing the necessity of using advanced techniques for effective SMP forecasting. Overall, this study presents a novel and comprehensive approach tailored for complex electricity markets, significantly enhancing predictive reliability by incorporating economic indicators and sophisticated feature selection methods. [ABSTRACT FROM AUTHOR]

Working hours have been discussed for over 150 years from a variety of perspectives such as worker health, corporate productivity, and job market stability. Despite extensive research in working hour reduction, there is limited evidence on the effect of hours worked by industry characteristics. This study examined the relationship between technical efficiency and working hours. For this, 514 Korean companies were divided into labour-intensive and knowledge-intensive industries, and changes in technical efficiency in each industry were investigated after implementing a working hour reduction policy. The efficiency of the labour-intensive industry improved after a reduction of working hours, whereas that of the knowledge-intensive industry decreased. These results imply that working hours should vary by industry. This study empirically illustrates the effect of reducing working hours and suggests practical guidelines for each country and company to increase technical efficiency by industry. [ABSTRACT FROM AUTHOR]

Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability, and the development of the artificial intelligence industry. To provide investors with a more reliable reference in terms of artificial intelligence index investment, this paper selects the NASDAQ CTA Artificial Intelligence and Robotics (AIRO) Index as the research target, and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics. Specifically, this paper uses the ensemble empirical mode decomposition (EEMD) method and the modified iterative cumulative sum of squares (ICSS) algorithm to decompose the index returns and identify the structural breakpoints. Furthermore, it combines the least-square support vector machine approach with the particle swarm optimization method (PSO-LSSVM) and the generalized autoregressive conditional heteroskedasticity (GARCH) type models to construct innovative hybrid forecasting methods. On the one hand, the empirical results indicate that the AIRO index returns have complex structural characteristics, and present time-varying and nonlinear characteristics with high complexity and mutability; on the other hand, the newly proposed hybrid forecasting method (i.e., the EEMD-PSO-LSSVM-ICSS-GARCH models) which considers these complex structural characteristics, can yield the optimal forecasting performance for the AIRO index returns. [ABSTRACT FROM AUTHOR]

The increasing adoption of renewable energy sources and the emergence of distributed generation have significantly transformed the traditional energy landscape, leading to the rise of local energy markets. These markets facilitate decentralized energy trading among different market participants at the community level, fostering greater energy autonomy and sustainability. As local energy markets gain momentum, the application of artificial intelligence techniques, particularly reinforcement learning, has gained substantial interest in optimizing energy trading strategies by interacting with the environment and maximizing the rewards by addressing the decision complexities by learning. This paper comprehensively reviews the different energy trading projects initiated at the global level and machine learning approaches and solution strategies for local energy markets. State-of-the-art reinforcement learning algorithms are classified into model-free and model-based methods. This classification examines various algorithms for energy transactions considering the agent type, learning methods, policy, state space, action space, and action selection for state, action, and reward function outputs. The findings of this work will serve as a valuable resource for researchers, stakeholders, and policymakers to accelerate the adoption of the local energy market for a more efficient, sustainable, and resilient energy future. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is increasingly used to support or automate decision processes in critical decision systems such as self driving cars or systems for medical diagnosis. These systems require decisions in which human lives are at stake and the decisions should therefore be well founded and very reliable. This need for reliability contrasts with the black-box nature of many ML models, making it difficult to ensure that they always behave as intended. In face of the high stakes involved, the resulting uncertainty is a significant challenge. Explainable artificial intelligence (XAI) addresses the issue by making black-box models more interpretable, often to increase user trust. However, many current XAI applications focus more on transparency and usability than on enhancing safety of ML applications. In this work, we therefore conduct a systematic literature review to examine how XAI can be leveraged to increase safety of ML applications in critical decision systems. We strive to find out for what purposes XAI is currently used in critical decision systems, what are the most common XAI techniques in critical decision systems and how XAI can be harnessed to increase safety of ML applications in critical decision systems. Using the SPAR-4-SLR protocol, we are able to answer these questions and provide a foundational resource for researchers and practitioners seeking to mitigate risks of ML applications. Essentially, we identify promising approaches of XAI which go beyond increasing trust to actively ensure correctness of decisions. Our findings propose a three-layered framework to enhance safety of ML in critical decision systems by means of XAI. The approach consists of Reliability, Validation and Verification. Furthermore, we point out gaps in research and propose future directions of XAI research for enhancing safety of ML applications in critical decision systems. [ABSTRACT FROM AUTHOR]

This article explores the transformative potential of artificial intelligence (AI) tools across the agricultural value chain, highlighting their applications, benefits, challenges, and future prospects. With global food demand projected to increase by 70% by 2050, AI technologies—including machine learning, big data analytics, and the Internet of things (IoT)—offer critical solutions for enhancing agricultural productivity, sustainability, and resource efficiency. The study provides a comprehensive review of AI applications at multiple stages of the agricultural value chain, including land use planning, crop selection, resource management, disease detection, yield prediction, and market integration. It also discusses the significant challenges to AI adoption, such as data accessibility, technological infrastructure, and the need for specialized skills. By examining case studies and empirical evidence, the article demonstrates how AI-driven solutions can optimize decision-making and operational efficiency in agriculture. The findings underscore AI's pivotal role in addressing global agricultural challenges, with implications for farmers, agribusinesses, policymakers, and researchers. This article aims to advance the evolving research and discussions on sustainable agriculture, contributing insights that promote the adoption of AI technologies and influence the future of farming. [ABSTRACT FROM AUTHOR]

The rapid growth of distributed energy resources (DERs) in smart grids has necessitated innovative strategies to manage and optimize energy markets. This paper introduces an architectural framework that leverages game theory and reinforcement learning (RL) as foundational methodologies to enhance the efficiency and robustness of distributed energy markets. Through simulations and case studies, we demonstrate how these approaches can facilitate improved decision-making among market participants, leading to better energy distribution and consumption. This exploratory approach is intended to lay the groundwork for more complex implementations that account for physical and regulatory constraints. Our preliminary results indicate a 25% reduction in energy costs and a 30% improvement in energy distribution efficiency compared to traditional methods. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to propose a model for democratization of Islamic home financing to tackle the issue of sustainability of homeownership affordability. Design/methodology/approach: A conceptual framework and fractional equity model (FEM) are developed to incorporate big data analytics, artificial intelligence and blockchain technology in an ecosystem for affordability and sustainability of homeownership via the proposed financing model. In addition, the FEM adopts the simulation approach to show its validity in terms of liquidity when compared with traditional home financing. In this regard, this paper is focused on developing and demonstrating the feasibility of a new financing model, rather than testing specific hypotheses or relationships. This is to propose the democratization model for Islamic Home Financing that will not benefit the prospective home buyers without compromising the profitability of the financial institutions. Findings: The findings indicate that the proposed end-to-end solution within the financing ecosystem can lead to more efficient matching market between the buyers and sellers of houses, reduced transaction costs, greater transparency and enhanced efficiency which in the end could lead to lower costs of owning homes and sustained financial resilience among house owners. The findings indicate that the FEM model is able to increase homeownership with more elements of liquidity, marketability and sustainability for homebuyers. Research limitations/implications: This research highlights the potential of big data and blockchain technology in democratizing Islamic home financing and evidence that the transfer of ownership is possible through tokenization. However, this will require a mature financing environment to adapt the technology for practical application. Practical implications: The model proposes a solution to propagate shared prosperity among stakeholders such as the house buyers/owners, sellers, investors as well the government agencies. The proposed FEM model provides alternative home financing that is more marketable, flexible and sustainable for households/buyers and financiers. Social implications: It is hoped that with the proposed financing ecosystem to promote affordability and sustainability of homeownership via big data analytics, artificial intelligence and blockchain technology can lead to greater financial resilience for homeowners which can then be translated to enhanced well-being, increased productivity and can further promote economic growth. Originality/value: This research is a concept paper based on academic research and industry collaboration with a technology provider. [ABSTRACT FROM AUTHOR]

Background/Objectives: The ongoing avian influenza (H5N1) outbreak, one of the most widespread and persistent in recent history, has significantly impacted public health and the poultry and dairy cattle industries. This review covers lessons from past outbreaks, risk factors for transmission, molecular epidemiology, clinical features, surveillance strategies, and socioeconomic impacts. Since 1997, H5N1 has infected over 900 individuals globally, with a fatality rate exceeding 50%. Key factors influencing infection rates include demographic, socioeconomic, environmental, and ecological variables. The virus's potential for sustained human-to-human transmission remains a concern. The current outbreak, marked by new viral clades, has complicated containment efforts. Methods: This review discusses how to integrate technological advances, such as mathematical modeling and artificial intelligence (AI), to improve forecasting, hotspot detection, and early warning systems. Results: We provide inventories of data sources, covering both conventional and unconventional data streams, as well as those of mathematical and AI models, which can be vital for comprehensive surveillance and outbreak responses. Conclusion: In conclusion, integrating AI, mathematical models, and technological innovations into a One-Health approach is essential for improving surveillance, forecasting, and response strategies to mitigate the impacts of the ongoing avian influenza outbreak. Strengthening international collaboration and biosecurity measures will be pivotal in controlling future outbreaks and protecting both human and animal populations from this evolving global threat. [ABSTRACT FROM AUTHOR]

This study examines the significant growth of e-commerce in Romania, which has surpassed the rates of expansion observed in other more developed countries of the European Union. Based on market analysis by sector-specific companies, the Romanian e-commerce market has reached over €6.5 billion. This rapid growth trajectory is expected to continue, driven by various factors, including the impact of the COVID-19 pandemic and the natural evolution of the market. The main purpose of this study is to assess the expansion of the e-commerce market in Romania, identify the key factors behind this growth, and project future market values. Data for this analysis has been collected from industry reports, market analysis, and relevant statistical databases. The study uses a quantitative approach, utilizing financial data and growth rates to forecast future market trends. The dataset includes financial figures from e-commerce sales, digital services such as bill payments, and airline and hotel bookings from 2018 to 2023. Projections for 2024 and beyond were derived from this historical data. In 2019, the e-commerce market in Romania was valued at €4.68 billion, representing a significant increase compared to previous years. By 2020, amid the pandemic, the market value increased to €5.5 billion, marking a 38.4% increase from the previous year. Forecasts for 2024 estimate that the market will exceed €8 billion. In addition, when related digital services are included, the total market value could exceed €10 billion, illustrating the substantial economic impact of the online sector and the growth potential. This study highlights the dynamic nature of the e-commerce landscape in Romania and underlines the significant economic opportunities it presents. [ABSTRACT FROM AUTHOR]

In this paper, we applied machine learning techniques to analyze the default probability in financial institutions using a large dataset of variables collected from 2325 banks over 17 years, extracting the most relevant variables using a feature selection method, predicting default and systemic risk, and finally investigating the contributions of each relevant feature to the overall financial stress of banking institutions using explainable artificial intelligence techniques. We found that the most important variables for the default risk prediction include the bailout probability, the market share in terms of assets, and the market-to-book ratio, highlighting the relevance of issues like moral hazard and market leverage. On the other hand, for systemic risk prediction, the number of banks in the country and interest rates level are among the most relevant features, indicating that markets with more competition fare better against systemic risks. The findings of this research provide an empirical assessment of the main factors that explain the presence of financial stress in banking institutions, conciliating the versatility of machine learning models with practical interpretability and causal inference, being of potential interest to researchers in quantitative finance and market practitioners. [ABSTRACT FROM AUTHOR]

The present research aims to improve the detection of financial frauds in enterprises through the utilization of Artificial Intelligence (AI) methods. The proposed framework employs machine learning algorithms and data analytics to accurately identify patterns, anomalies, and signs of fraudulent activity. Exploratory data analysis approaches were employed to identify instances of missing values and imbalanced data. The random forest was based on its ability to consistently capture intricate patterns and efficiently tackle the multicollinearity problem. The isolation forest approach yielded an accuracy of 99.7%, while the local outlier factor method achieved an accuracy of 99.8%. Similarly, the random forest algorithm demonstrated an accuracy of 99.9%. [ABSTRACT FROM AUTHOR]

The continued progress of Artificial Intelligence (AI) can benefit different aspects of society and various fields of the economy, yet pose crucial risks to both those who offer such technologies and those who use them. These risks are emphasized by the unpredictability of developments in AI technology (such as the increased level of autonomy of self-learning systems), which renders it even more difficult to build a comprehensive legal framework accounting for all potential legal and ethical issues arising from the use of AI. As such, enforcement authorities are facing increased difficulties in checking compliance with applicable legislation and assessing liability, due to the specific features of AI, – namely: complexity, opacity, autonomy, unpredictability, openness, data-drivenness, and vulnerability. These problems are particularly significant in areas, such as financial markets, in which consequences arising from malfunctioning of AI systems are likely to have a major impact both in terms of individuals' protection, and of overall market stability. This scenario challenges policymaking in an increasingly digital and global context, where it becomes difficult for regulators to predict and face the impact of AI systems on economy and society, to make sure that they are human-centric, ethical, explainable, sustainable and respectful of fundamental rights and values. The European Union has been dedicating increased attention to filling the gap between the existing legal framework and AI. Some of the legislative proposals in consideration call for preventive legislation and introduce obligations on different actors – such as the AI Act – while others have a compensatory scope and seek to build a liability framework – such as the proposed AI Liability Directive and revised Product Liability Directive. At the same time, cross-sectorial regulations shall coexist with sector-specific initiatives, and the rules they establish. The present paper starts by assessing the fit of the existing European liability regime(s) with the constantly evolving AI landscape, by identifying the normative foundations on which a liability regime for such technology should be built on. It then addresses the proposed additions and revisions to the legislation, focusing on how they seek to govern AI systems, with a major focus on their implications on highly-regulated complex systems such as financial markets. Finally, it considers potential additional measures that could continue to strike a balance between the interests of all parties, namely by seeking to reduce the inherent risks that accompany the use of AI and to leverage its major benefits for our society and economy. [ABSTRACT FROM AUTHOR]

With the high-frequency trading process, which is a subclass of algorithmic trading transactions, intraday information has increasing importance. Traditional statistical methods often fall short in capturing the intricate patterns and volatility inherent in such high-frequency data. In contrast, artificial neural network (ANN) models demonstrate remarkable capability in handling these challenges, and vector autoregressive (VAR) models provide insights into short-term relationships among variables. This study highlights the importance of using both ANN and VAR models for processing these short time intervals. BIST100 index, which is the main index of Borsa Istanbul, is predicted with two different models in different data ranges with ANN models and VAR models. Both generated ANN models successfully complete the training stages, with extremely high precision, and exhibit exceptionally low error values in their predictions. Although both models are effective, the evidence favors the model evaluated using 5-min data for both the training and prediction phases of ANN models. However, the relative importance of 15-min data in explaining the variation of BIST100 is higher. Moreover, the VAR model results indicate that the short-term relationship between variables can be influenced by the range of data and the 15-min interval data of the variables play a more significant role in explaining the BIST100 index over the longer term. [ABSTRACT FROM AUTHOR]

Emerging forms of automation using artificial intelligence (AI) and distributed ledgers are raising transformative questions for the practice of law. Deal lawyers are wellsituated to understand the convergence of various modes of automation and their implications for their clients and the markets they facilitate. This Article contends that digesting threats and leveraging opportunities associated with new technologies calls for granular, context-specific assessment. It presents one instance of automation in one predominant market—the market for asset-backed securities (ABS)—by comparing securitization to a blockchain-based analog, tokenization. It considers how lawyers support the ABS market and how automation of lawyers’ functions could intersect with automated issuances. This context-specific inquiry yields broader questions surrounding deal lawyers’ roles in the face of evolving technologies. Asset-backed issuances often create value with legal ambiguity. Lawyers facilitate these issuances by structuring legally complex assignments and then opining on the positions of investors. Automated platforms can alter the consequences of deliberate legal ambiguity, and as such can affect risks associated with asset-backed issuances. If established forms developed for non-automated deals serve as baselines to be digitized for automated transactions, then investors’ choices about platform may adversely affect issuers and their stakeholders. Lawyers have the capacity to understand and explain these dynamics. If AI models trained on existing deal documentation generate work-products for automated transactions, what kinds of effects and concerns should lawyers identify? The value and function of business lawyers came under scrutiny after corporate scandals at the start of the millennium and after the 2007–08 financial crisis, but the scrutiny subsided when market activity re-trenched. Automation, now, revives questions around the responsibilities of deal lawyers. [ABSTRACT FROM AUTHOR]

This study examines the impact of the COVID-19 pandemic on sector volatility in sub-Saharan Africa by drawing evidence from two large and two small stock exchanges in the region. The analysis included stock-specific data, COVID-19 metrics, and macroeconomic indicators from January 2019 to July 2022. This study employs generalized autoregressive conditional heteroskedasticity (GARCH) models to estimate volatility and Explainable Artificial Intelligence (XAI) in the form of SHapley Additive exPlanations (SHAP) to identify significant factors driving stock volatility during the pandemic. The findings reveal significant volatility increases at the onset of the pandemic, with government stringency measures leading to increased volatility in larger exchanges, while the introduction of vaccination programs helped to reduce volatility. Weaker macroeconomic fundamentals impact volatility in smaller exchanges. The healthcare sector has emerged as the most resilient, while non-essential sectors, such as consumer discretionary, materials, and real estate, face greater vulnerability, especially in smaller exchanges. The research findings reveal that the heightened stock market volatility observed was mainly a result of the government's actions to combat the spread of the pandemic, rather than its outbreak. We recommend that governments introduce sound policies to balance public health measures and economic stability, and that investors diversify their investments to reduce the impact of pandemics. [ABSTRACT FROM AUTHOR]

Agriculture can ensure food security and enhance monetary benefits if practiced with modern technologies and supported with artificial intelligence (AI). Modern advancements in farming practices have revolutionized the production of food vegetation. However, crop cultivation faces several threats including insect and pest attacks and disease infections on plant leaves. For example, one of the most consumed foods vegetables universally—potatoes, are vulnerable to diseases like Late Blight (LB), Early Blight (EB), and others. These infections must be controlled to enhance food quality and yield. Conventional disease detection techniques are slow and depend on human involvement, which may be laborious and erroneous. However, AI tools, for instance, Machine Learning (ML) and Deep Learning (DL), offer precise and well-timed solutions for disease detection, classification, and eradication. A comprehensive review of literature has been conducted by examining over 400 articles to focus on 72 studies including 14 reviews publications on ML and DL models about potato disease forecasting using different techniques. It highlights the need for proficient disease control by integrating image and climate data. It further aids in addressing challenges like data availability and geographical variations. It has been learned that image-processing techniques overwhelm the existing research and have the potential to integrate meteorological data. The most widely used algorithms incorporate Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and MobileNet with accuracy rates between 64.3 and 100%. The importance of accurate disease detection and eradication has been reported for food security, financial stability, and sustainable farming practices. Progressions in disease forecasts aid farmers in making informed decisions, minimizing crop losses, and reducing pesticide use through targeted application of agrochemicals with the use of AI-driven variable rate sprayers. This leads to healthier crops, market stability, and a more sustainable farming environment.

There is an imbalance in the development of artificial intelligence between industries. Compared to non-AI enterprise, AI- enterprise will save labor, enhance innovation capabilities, and improve production efficiency. By constructing a two-industry model of AI and non-AI enterprise, this paper finds that with the development of artificial intelligence in the same industry, the AI enterprise will occupy a dominant position, attracting labor and capital from the non-AI enterprise into the AI enterprise. In different industries, the development of artificial intelligence improves the production efficiency of the enterprise. However, due to the price effect, non-AI enterprise benefits more. Labor and capital flow from AI enterprise to non-AI enterprise. In order to promote the improvement of production efficiency in the whole society, the government can tax non-AI enterprise and subsidize them to AI enterprise. Taxation promotes the degree of automation and the improvement of production efficiency, but it has only a short-term effect on the development of AI. At the same time, taxation inhibits the development of non-AI enterprise, and there is a high risk of unemployment. When both industries use artificial intelligence for production, the labor share and the capital share of the two industries will tend to the same value. The convergence of technology measures is conducive to increasing labor income share and reducing income inequality, but it is not conducive to innovation. [ABSTRACT FROM AUTHOR]

Does the ascendance of the artificially intelligent corporation threaten the integrity and legitimacy of democracy? The question seems particularly important as the 2024 presidential election approaches. Hardly a day passes without a new report regarding the disruptive impact of harnessing artificial intelligence ("AI") technologies. A cascading cadre of academics, business leaders, and politicians warn that unchecked development and dissemination of AI could irreparably damage vital institutions of civil society. Despite the warnings about existential threats AI poses to human agency and democratic processes, reliance on AI technologies proliferates at break-neck speed. The concern about AI's destructive impact gets exacerbated by the increasing dominance of corporations in politics. Following the decision in Citizens United v. FEC, corporations enjoy essentially the same speech rights as sentient human beings. With increasing zeal, corporations attempt to dominate the political realm in an effort to enhance the bottom line. Because extant law generally does not require disclosure of corporate political expenditures, corporations clandestinely manipulate voters to increase sales or secure a more favorable regulatory environment. This Article argues that the proliferation of AI combined with the increasing dominance of corporations in our society calls for revamping basic principles of corporate governance. In particular, the Article examines whether interpreting corporate fiduciary duties through the lens of political "discourse theory" could better ensure corporate practices meaningfully align with the preferences of shareholders and other corporate stakeholders. Considering some of the most important decisions governing our daily lives already get made behind boardroom doors rather than in the public sphere, the rapid integration of AI into corporate decision making and operations threatens the very legitimacy of our democratic society. Without reinvigorating governance structures around democratic discourse, we might surrender political sovereignty to artificially intelligent corporations. [ABSTRACT FROM AUTHOR]

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Artificial intelligence (AI) and big data are transforming the credit market around the world. Algorithmic credit scoring (ACS) is increasingly used to assess borrowers' creditworthiness, using technology to glean non-traditional data from smartphones and analyze them through machine-learning algorithms. These processes promise efficiency, accuracy, and cost-effectiveness compared with traditional credit scoring. However, this technology raises public concerns about opacity, unfair discrimination, and threats to individual privacy and autonomy. Many countries in Southeast Asia are introducing ACS in consumer finance markets, although—even with the significant concerns raised—there is an ongoing and concerning lag in oversight and regulation of the process. Regulation is vital to delivering big data and AI promises in the financial services market, while ensuring fairness and public interest. This article utilizes Vietnam, where the lending industry deploys ACS but in a situation of legal limbo, as a case-study to analyze the consequences of this technology. Vietnam is one of the foremost Southeast Asian countries in which ACS usage is spreading rapidly, and this provides an excellent opportunity to review the regulation, or lack thereof, and determine the implications that this may have for other countries that are currently introducing ACS in consumer finance markets. The article concludes with a proposal to regulate ACS in Vietnam based on international regulation and guidelines on ACS, data privacy, and AI to enable a transparent, accessible, and fair process. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has proliferated in the last few years due to the vast data we pro-duce daily and available computing power. AI can be applied in many different sectors, such as transportation, education, healthcare, banking, and finance, among many others. The financial industry is rapidly embracing AI due to its potential for high-cost savings in financial services. AI could transform the financial sector by creating opportunities for tailored, faster, and more cost-effective services. Saudi Arabia is emerging as a fast-growing market in this industry with a strong commitment to technology-driven institutions. While AI is gaining prominence and receiving government support, it has not yet become a critical component for enhancing the efficiency of financial transactions. Limited published research on AI adoption in the Saudi Arabian financial industry calls for a comprehensive literature review to examine the current state of AI implementation in this sector. Therefore, this study explores the benefits, limitations, and challenges of leveraging AI in finance, highlighting the importance of ethical and regulatory considerations for successful AI adoption in the sector. This study's findings reveal that research has been conducted on how AI improves processes in the financial sector by integrating critical components and efficient algorithms tailored to the industry's needs. Based on these findings, this study proposes a sequential framework at the macro and micro levels of management to guide AI's development and integration into the financial sector. Additionally, the framework draws insights from the existing literature to provide a detailed understanding of opportunities, challenges, and areas for improvement to maximize AI's potential in the Saudi Arabian financial sector. [ABSTRACT FROM AUTHOR]

Research background: Fintech companies should optimize banking sector performance in assisting enterprise financing as a result of firm digitalization. Artificial IoT-based fintechbased digital transformation can relevantly reverse credit resource misdistribution brought about by corrupt relationship chains. Purpose of the article: We aim to show that fintech can decrease transaction expenses and consolidates firm stock liquidity, enabling excess leverage decrease and cutting down information asymmetry and transaction expenses across capital markets. AI- and IoT-based fintechs enable immersive and collaborative financial transactions, purchases, and investments in relation to payment tokens and metaverse wallets, managing financial data, infrastructure, and value exchange across shared interactive virtual 3D and simulated digital environments. Methods: AMSTAR is a comprehensive critical measurement tool harnessed in systematic review methodological quality evaluation, DistillerSR is harnessed in producing accurate and transparent evidence-based research through literature review stage automation, MMAT appraises and describes study checklist across systematic mixed studies reviews in terms of content validity and methodological quality predictors, Rayyan is a responsive and intuitive knowledge synthesis tool and cloud-based architecture for article inclusion and exclusion suggestions, and ROBIS appraises systematic review bias risk in relation to relevance and concerns. As a reporting quality assessment tool, the PRISMA checklist and flow diagram, generated by a Shiny App, was used. As bibliometric visualization and construction tools for large datasets and networks, Dimensions and VOSviewer were leveraged. Search terms were "fintech" + "artificial intelligence", "big data management algorithms", and "Internet of Things", search period was June 2023, published research inspected was 2023, and selected sources were 35 out of 188. Findings & value added: The growing volume of financial products and optimized operational performance of financial industries generated by fintech can provide firms with multifarious financing options quickly. Big data-driven fintech innovations are pivotal in banking and capital markets in relation to financial institution operational efficiency. Through datadriven technological and process innovation capabilities, AI system-based businesses can further automated services. [ABSTRACT FROM AUTHOR]

Many real-world applications require prediction that takes the most advantage of data. Classic data mining mechanisms tend to feed a prediction model pivotal data to achieve a promising result, which needs to be adjusted in different application scenarios. Recent studies have shown the potential of I Ching mechanism to improve prediction capacity. However, the I Ching prediction mechanism has several issues, including underutilized I Ching knowledge and incomplete data conversion. To address these issues, the authors designed a model to leverage I Ching knowledge and transform traditional I Ching prediction processing into data mining. The authors' investigation revealed promising results in the stock market compared to popular machine learning and deep learning algorithms such as support vector machine (SVM), extreme gradient boosting (XGBoost), and long short-term memory (LSTM). [ABSTRACT FROM AUTHOR]

As the paradise of robo-advisors, the US has the most advanced robo-advisors, but regulators and robo-advisory institutions still face issues. From the cases of Wealthfront, Betterment and Charles Schwab, various problems occur in regulating robo-advisors, especially from a fiduciary perspective. The US establishes a framework with the Investment Advisers Act, FINRA Rules and ERISA combined with specific state rules, which is comprehensive and economically efficient in general, and, in the meantime, the issues of regulatory uncertainty, technological imbalance, as well as fragmented organisational models still exist. The US adopts a multilayer and polycentric financial regulatory approach, and supervises robo-advisors via case law and administrative acts, which helps develop this concrete financial supervision system and leaves some problems. Looking forward, US regulators need to establish a unified licensed regime, make clear fiduciary compliance and liabilities mechanisms and strengthen the cooperation of self-regulation, federal and state initiatives. [ABSTRACT FROM AUTHOR]

Currently, explainability represents a major barrier that Artificial Intelligence (AI) is facing in regard to its practical implementation in various application domains. To combat the lack of understanding of AI-based systems, Explainable AI (XAI) aims to make black-box AI models more transparent and comprehensible for humans. Fortunately, plenty of XAI methods have been introduced to tackle the explainability problem from different perspectives. However, due to the vast search space, it is challenging for ML practitioners and data scientists to start with the development of XAI software and to optimally select the most suitable XAI methods. To tackle this challenge, we introduce XAIR, a novel systematic metareview of the most promising XAI methods and tools. XAIR differentiates itself from existing reviews by aligning its results to the five steps of the software development process, including requirement analysis, design, implementation, evaluation, and deployment. Through this mapping, we aim to create a better understanding of the individual steps of developing XAI software and to foster the creation of real-world AI applications that incorporate explainability. Finally, we conclude with highlighting new directions for future research. [ABSTRACT FROM AUTHOR]

In the era of globalization and the Internet, due to the unprecedented business pressure from competition, in order to make long-term benefits and achieve sustainable development, enterprises should select competitive markets for their products to maximize the benefits of limited resources. The key to corporate survival and development is to find the most profitable customer bases all over the world and develop products to meet their demands. Traditionally, market targeting relies on the decisions of a small number of senior managers in enterprises; however, due to novel and changing customer demands, the business environment has become increasingly complex. If previous traditional methods are adopted, enterprises may select the wrong markets, which can lead to complete destruction. Therefore, this study proposes a new market targeting method and replaces human decisions with artificial intelligence (AI) algorithms, in order to render market targeting more scientific and systematic, improve the quality of marketing decisions, maximize corporate profits, occupy the optimal market with limited resources, and achieve the goal of sustainable business. This study applied three AI algorithms, the naive Bayes algorithm, J48 algorithm, and OneR algorithm, for model training and analytical prediction of the testing datasets. According to the results, the model accuracies of the naive Bayes algorithm, J48 algorithm, and OneR algorithm are 100, 91.7, and 83.3%, respectively; the F-measures of the naive Bayes algorithm, J48 algorithm, and OneR algorithm are 1, 0.909, and 0.8, respectively, which indicates that the three algorithms have reliable predictions. The results show that AI algorithms can help enterprises in market targeting. [ABSTRACT FROM AUTHOR]

Artificial intelligence and robotic process automation are among the next generation of digital technologies that are transforming the workplace and our daily lives. And digital transformation has become a critical strategy for many businesses and their leadership. In today's fast-paced and ever-changing business environment, the cornerstone of organizational success is innovation. As the business environment is in perpetual modification and development, it requires businesses to be adaptive to both external and internal factors. The instability of the organizational environment increases the necessity and benefits of employee innovation. The authors analyze the extensive literature defining artificial intelligence, focusing on automation and specifically the role robotic process automation has in increasing organizational efficiency, reducing cost, and ensuring quality. In expanding on the scientific literature surrounding robotic process automation, the article analyses a case study of a Romanian tech company whose complex internal technology ecosystem has resulted in a disconnected landscape which required constant multi-system manual intervention in order to ensure process and data cohesion. The authors present the company's response to the increased consumption of human capital on repetitive tasks that were increasingly subject to errors, in the form of robotic process automation and levels of machine learning and artificial intelligence. In following the progress of the company towards adopting robotic process automation to increase efficiency, speed, accuracy, and security of processes and tasks, the authors identify the benefits of directing human labor towards more meaningful activities and allowing the repetitive tasks to be shifted to robotic labor. The authors followed the progress of a service technology solutions organization that has established an IT intelligent operation ecosystem, with new strategies across various key departments such as finance, human resources, and Internal Audit, followed by their leadership perspective and thinking for the company's next steps in this case study. [ABSTRACT FROM AUTHOR]

The banking, financial services, and insurance (BFSI) sector is one of the earliest and most prominent adopters of artificial intelligence (AI). However, academic research substantially lags behind the adoption of AI in practice. At the beginning of this century, AI research has been centered on the sector's credit risk. In the 2010s decade, expert systems were increasingly replaced by data‐driven, "algorithmic" AI. Big data enjoyed much hype in that decade, which diminished later mostly due to unsuccessful implementations. Much published research on big data actually relates to machine and deep learning but not to big data per se. These terms are often found to be conflated in research and practice. The insurance sector is substantially underrepresented in published AI research, and current research is dominated by banking and investments. Governance frameworks for "responsible AI" (RAI) are yet to be incorporated into practice by fintech companies as well as incumbent organizations. RAI is a particular issue for decentralized finance (DeFi). The most successful implementations of AI in BFSI practice, as well as dominant academic research areas, are in investments, securities, market making, customer relationships, lending, risk management, and compliance. [ABSTRACT FROM AUTHOR]

The Private Banking & Wealth Management (PWM) industry is generally seen as embodying traditional, oldfashioned and even archaic values. Upheld for centuries, its business model, which is based on intensive, comprehensive and discreet personal interactions between financial advisors and wealthy clients, is put to the test today. In today's dynamic and highly connected world, a large number of HNWIs (High Net Worth Individuals) want faster and more convenient value propositions and a cutting-edge digital experience - a trend that the pandemic has amplified many times over. In order to meet the increased expectations of this clientele, private banks and other institutions in the sector are increasingly investing in a number of new technologies and tools, artificial intelligence (AI) taking a leading place among them. In addition to enabling a more complete and qualitative satisfaction of user needs, AI promises benefits for PWM companies in a number of other areas: risk management, compliance, cost reduction, etc. [ABSTRACT FROM AUTHOR]

Behavioral finance is an area of study in finance and investment, which in the recent years is in constant focus with rising importance in the social sciences, creating a link between the economy, psychology, sociology and neurosciences in a broader aspect. Behavioral finances provides an opportunity for further research and empirical analyzes in the decision-making process of individuals, groups, and organizations. The main hypothesis in the research is that the irrational behavior of Macedonian investors while making investment decisions is present and leading behavior in certain periods while making investment' decisions. The hypothesis was examined with a survey questionnaire, additionally analyzing the results with the Kolmogorov-Smirnov Test (KS test). The results undoubtedly showed that investors' decisions are made irrationally by the influence of non-financial and psychological reasons, i.e. excessive self-confidence of investors or political influence on the decisions. The key issue for behavioral finance in the future is not only understanding the irrational behavior but also finding a solution for predicting and preventing the negative effects on the financial markets, thus creating an opportunity for stable financial markets. The future in the economy is not only in artificial intelligence and technology, but in understanding and managing the decision-making process to rational decisions that would lead to better economic environment with opportunity for economic growth and efficient-markets with speculations at minimum level as possible. [ABSTRACT FROM AUTHOR]

Several socio-economic problems have been hidden by the COVID-19 pandemic crisis. Particularly, the agricultural and food industrial sectors have been harshly affected by this devastating disease. Moreover, with the worldwide population increase and the agricultural production technologies being inefficient or obsolete, there is a great need to find new and successful ways to fulfill the increasing food demand. A new era of agriculture and food industry is forthcoming, with revolutionary concepts, processes and technologies, referred to as Agri-food 4.0, which enables the next level of agri-food production and trade. In addition, consumers are becoming more and more aware about the origin, traceability, healthy and high-quality of agri-food products. The integration of new process of production and data management is a mandatory step to meet consumer and market requirements. DNA traceability may provide strong approach to certify and authenticate healthy food products, particularly for olive oil. With this approach, the origin and authenticity of products are confirmed by the means of unique nucleic acid sequences. Selected tools, methods and technologies involved in and contributing to the advance of the agri-food sector are presented and discussed in this paper. Moreover, the application of DNA traceability as an innovative approach to authenticate olive products is reported in this paper as an application and promising case of smart agriculture. [ABSTRACT FROM AUTHOR]

CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term Big Video Data (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognitionmethods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real-world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this article, we draw researchers' attention toward the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook toward future research directions derived from our critical assessment of the efforts invested so far in this exciting field. [ABSTRACT FROM AUTHOR]

Since 2009, when Bitcoin was invented, a private currency has returned to the world stage, offering the advantages of absolute secrecy to its owner. Imitations soon emerged: they do not have the same characteristics, but incorporate increasingly refined financial technologies that has drawn special attention following the lockdown triggered by the pandemic crisis in 2019. As supply is fixed, the prices of bitcoins spiral upward, as well as the prices of other cryptocurrencies do although to a lesser extent. Platforms for custody and conversion of cryptocurrency are set up, and they expanded their business to grant credit either with their own or others cryptocurrencies and to use them as collateral in derivative contracts. Consistently with Gresham's law bad money starts driving out good money and Central Banks are considering the issue of digital sovereign currencies, but the interests around virtual products and fears for the stability of banks and financial intermediaries are slowing down the decision-making process. Only China is taking advantage of an own digital currency and has prohibited any other digital currency out of its control. The topic is also relevant to geopolitical balances and it triggers more international conflicts. The attempt to include cryptocurrency and cryptoassets into the scope of the existing legislation does not lead to a solution. The suggested solution is twofold: we must reiterate the legal nature of sovereign money and the lack of any protection for crypto-users. Legitimacy by public authorities, which is currently granted, either implicitly or explicitly, should be avoided. Crypto-assets (or STO-Security Tokens) are free to operate, but the decentralized ledgers they use must make information accessible to regulatory and supervisory Authorities, and States must guarantee cybersecurity of digital processes. [ABSTRACT FROM AUTHOR]

Consumer interaction with the financial market ranges from applying for credit cards, to financing the purchase of a home, to buying and selling securities. And with each transaction, the lender, bank, and brokerage firm are likely utilizing artificial intelligence (AI) behind the scenes to augment their operations. While AI's ability to process data at high speeds and in large quantities makes it an important tool for financial institutions, it is imperative to be attentive to the risks and limitations that accompany its use. In the context of financial markets, AI's lack of decision-making transparency, often called the "black box problem," along with AI's dependence on quality data, present additional complexities when considering the aggregate effect of algorithms deployed in the market. Owing to these issues, the benefits of AI must be weighed against the particular risks that accompany the spread of this technology throughout the markets. Financial regulation, as it stands, is complex, expensive, and often involves overlapping regulations and regulators. Thus far, financial regulators have responded by publishing guidance and standards for firms utilizing AI tools, but they have stopped short of demanding access to source codes, setting specific standards for developers, or otherwise altering traditional regulatory frameworks. While regulators are no strangers to regulating new financial products or technology, fitting AI within the traditional frameworks of prudential regulation, registration requirements, supervision, and enforcement actions leaves concerning gaps in oversight. This Article examines the suitability of the current financial regulatory frameworks for overseeing AI in the financial markets. It suggests that regulators consider developing multi-faceted approaches to promote AI accountability. This Article recognizes the potential harms and likelihood for regulatory arbitrage if these regulatory gaps remain unattended and thus suggests focusing on key elements for future regulation--namely, the human developers and regulation of data to truly "hold AI accountable." Therefore, holding AI accountable requires identifying the different ways in which sophisticated algorithms may cause harm to the markets and consumers if ineffectively regulated, and developing an approach that can flexibly respond to these broad concerns. Notably, this Article cautions against reliance on self-regulation and recommends that future policies take an adaptive approach to address current and future AI technologies. [ABSTRACT FROM AUTHOR]

This article is about explainable artificial intelligence (XAI) for rule-based fuzzy systems [that can be expressed generically, as $y({{\bf x}}) = f({{\bf x}})$ ]. It explains why it is not valid to explain the output of Mamdani or Takagi–Sugeno–Kang rule-based fuzzy systems using IF-THEN rules, and why it is valid to explain the output of such rule-based fuzzy systems as an association of the compound antecedents of a small subset of the original larger set of rules, using a phrase such as “these linguistic antecedents are symptomatic of this output.” Importantly, it provides a novel multi-step approach to obtain such a small subset of rules for three kinds of fuzzy systems, and illustrates it by means of a very comprehensive example. It also explains why the choice for antecedent membership function shapes may be more critical for XAI than before XAI, why linguistic approximation and similarity are essential for XAI, and, it provides a way to estimate the quality of the explanations. [ABSTRACT FROM AUTHOR]

This paper discusses the design of the legal and regulatory framework for using artificial intelligence (AI) in the financial services markets to enhance access to finance (financial inclusion). The author argues that the development of AI should continue to adhere to the regulatory objectives of market safety, consumer protection, and market integrity. However, to ensure equality and fairness, access to finance should be made a clear policy choice. In the first part, the author discusses how AI can lead to systemic risks and market manipulation on trading platforms. For example, by examining the use of algorithms for trading on the capital market, the author discerns the regulatory objectives and the possible methods of regulation for peer-to-peer platforms. In the second part, the author discusses how the use of AI to provide consumers with investment advice, such as financial advice provided from robo-advisers, can close the investment advisory gap and provide consumers with access to finance. The current regime does not provide adequate protection to financial consumers in this regard. In the third part, the author discusses how AI can be used as a form of RegTech to streamline compliance processes, thereby increasing competition in financial markets and providing a benefit to consumers. However, this use may be in conflict with privacy, data protection, and ethical concerns. The author makes policy recommendations and suggests some directions for governance in the use of AI in financial services to enhance access to finance. The findings of this paper are relevant to research on the future governance of AI in financial services, public policy innovation, and urban development. [ABSTRACT FROM AUTHOR]

Despite an emerging international consensus on principles of AI governance, lawmakers have so far failed to translate those principles into regulations in the financial sector. Perhaps, in order to remain competitive in the global race for AI supremacy without being typecast as stifling innovation, typically cautious financial regulators are unusually allowing the introduction of experimental AI technology into the financial sector, with few controls on the unprecedented risks to consumers and financial stability. Once an unregulated AI software causes serious economic harm, a public and regulatory backlash would lead to over-regulation that could harm innovation of this potentially beneficial technology. Artificial intelligence is rapidly influencing the financial sector with innumerable potential benefits, such as enhancing financial services and improving regulatory compliance. This article argues that the best way to encourage a sustainable future in AI innovation in the financial sector is to support a proactive regulatory approach prior to any financial harm occurring. This proactive approach should implement rational regulations that embody jurisdiction-specific rules in line with carefully construed international principles. [ABSTRACT FROM AUTHOR]

Stock-market prediction using machine-learning technique aims at developing effective and efficient models that can provide a better and higher rate of prediction accuracy. Numerous ensemble regressors and classifiers have been applied in stock market predictions, using different combination techniques. However, three precarious issues come in mind when constructing ensemble classifiers and regressors. The first concerns with the choice of base regressor or classifier technique adopted. The second concerns the combination techniques used to assemble multiple regressors or classifiers and the third concerns with the quantum of regressors or classifiers to be ensembled. Subsequently, the number of relevant studies scrutinising these previously mentioned concerns are limited. In this study, we performed an extensive comparative analysis of ensemble techniques such as boosting, bagging, blending and super learners (stacking). Using Decision Trees (DT), Support Vector Machine (SVM) and Neural Network (NN), we constructed twenty-five (25) different ensembled regressors and classifiers. We compared their execution times, accuracy, and error metrics over stock-data from Ghana Stock Exchange (GSE), Johannesburg Stock Exchange (JSE), Bombay Stock Exchange (BSE-SENSEX) and New York Stock Exchange (NYSE), from January 2012 to December 2018. The study outcome shows that stacking and blending ensemble techniques offer higher prediction accuracies (90–100%) and (85.7–100%) respectively, compared with that of bagging (53–97.78%) and boosting (52.7–96.32%). Furthermore, the root means square error (RMSE) recorded by stacking (0.0001–0.001) and blending (0.002–0.01) shows a better fit of ensemble classifiers and regressors based on these two techniques in market analyses compared with bagging (0.01–0.11) and boosting (0.01–0.443). Finally, the results undoubtedly suggest that an innovative study in the domain of stock market direction prediction ought to include ensemble techniques in their sets of algorithms. [ABSTRACT FROM AUTHOR]

The main contribution of this paper is to perform sensitivity analysis using artificial intelligence methods on the US stock market using alternative psychological indicators. The Takagi-Sugeno fuzzy model applies investor sentiment represented by VIX index and monitors the impact of economic optimism, political stability and control of the corruption index on the S&P 500 stock index. Alternative psychological indicators have been chosen that have not been explored in the context of stock index performance sensitivity. Investors primarily use fundamental and technical analysis as a source to determine when and what to buy into an investment portfolio. However, psychological factors that may indicate the strength of reaction to the market are often neglected. Fuzzy rules are determined and tested using a neuro-fuzzy inference system and then the rules are reduced by fuzzy clustering to improve performance of ANFIS. The membership function is defined as a Gaussian function because it has the least RMSE value. The sensitivity analysis confirmed that there is a significant impact of the political stability index and the economic optimism index on the S&P 500 performance. Conversely, the sensitivity analysis, unlike the previous study, did not confirm the strong impact of VIX on equity index performance. Results indicate that incorporating psychological indicators in macroeconomic models leads to better supervision and control of the financial markets. [ABSTRACT FROM AUTHOR]