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Behavioral Biases in Financial Markets: Understanding the Impact of Cognitive Heuristic-Driven Biases and Emotional Biases in Shaping Investment Decisions
Conventional finance theories believe that the stock market is organized and that stock valuations provide all relevant facts. On the other hand, behavioral finance theories argue that stock valuations can be affected by behavioral biases, explicitly cognitive heuristic-driven biases and emotional biases. The stock market displays the current wellness of an economy, and investment decisions represent it. Investors unveil irrational actions in their investment decision strategies. The investment decision strategy itself is a cognitive procedure, as stock investors must form decisions informed by several possibilities that are available to them. This chapter provides theoretical underpinnings and an overview of the effect of behavioral biases on investors investment decision-making. This research provides an in-depth insight into cognitive heuristic-driven biases (Illusion of Control, Hindsight, Conservatism, House Money Effect, Self-Attribution, Gamblers Fallacy, Confirmation, Recency, Familiarity, and Religiosity) and emotional biases (Disposition Effect, Loss Aversion, Regret Aversion, Risk Perception, and Mental Accounting) impact investment decisions. The implications of this study could be helpful for financial markets and institutions as well as practitioners, such as equity investors and traders, portfolio and asset managers, securities analysts, wealth advisors, money managers, securities bankers, and brokers. In addition, it benefits regulators, policymakers, academicians, and researchers. The overall chapter offers a positive impact between behavioral biases and investment decisions, with distinct themes from earlier research, and contributes to generalization. Copyright 2026 by Nova Science Publishers, Inc. -
Behavioral Biases as Drivers of Complexity in Stock Markets: An Agent-Based Modeling Approach
By modeling financial systems as Complex Adaptive Systems, this study investigates how behavioral biases influence emergent complexity in stock markets. The study integrates heterogeneous agents, such as rational traders, herding agents, overconfident traders, and anchoring/disposition-driven investors, within a Limit Order Book framework calibrated to both U.S. and Indian market conditions using an Agent-Based Modeling (ABM) approach implemented through the high-fidelity ABIDES simulation environment. Price dynamics, volatility patterns, and liquidity structures were analyzed by Monte Carlo simulation experiments with different behavioral compositions. The results show that behavioral biases cause nonlinear price reactions, produce heavy-tailed return distributions that distort order-book complexity, and greatly increase volatility. The market shifts from a stable, rational regime to a highly volatile, complex regime characterized by contagion and fragile liquidity as the proportion of biased actors rises. Overall, the findings show that the complexity and systemic instability of emerging markets are primarily driven by behavioral heterogeneity. 2026 Binghamton University Libraries. All rights reserved. -
Behavioral Bias as an Instrumental Factor in Investment Decision-An Empirical Analysis
Investment decisions are always complex in nature. Investment assets are volatile in nature there are less volatile, medium volatile and high volatile investment assets in the financial market. In the current study how, the behavioral biases of the investors affecting their investment decisions in the less volatile asset classes is examined using an extensive survey method among the IT professionals in the Bangalore city. The relationship between the demographic variables and behavioral biases is tested. Also, a detailed study is conducted to examine the risk-taking behavior of the investors in the less volatile assets. There are basically three type of investors on the basis of their risk-taking behavior i.e. Risk seeking, Risk Neutral and Risk averse investors. Current study reveals that investors in the less volatile asset classes are very much cautious about the risk factor and therefore they are risk averse in nature. The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024. -
Behavioral Analytics for Predictive Modeling of Mental Health Disorders: A Review of Machine Learning Techniques and Challenges
Mental health issues, including anxiety, stress, and depression, may remain untreated until they escalate to a severe level. The issues significantly impact an individual's overall well-being and productivity. Timely identification is crucial for the effectiveness of both intervention and therapy. The application of machine learning techniques makes behavioral analytics a powerful tool for mental health disease prediction modeling. By analyzing behavioral data, this technology facilitates the early detection of various illnesses. This work aims to provide a thorough overview of the use of machine learning techniques, including models that employ Deep structured learning as well as both unsupervised as well as supervised learning, to behavioral data, including activity levels, speech patterns, and facial movements, in order to identify signs of mental health. The benefits and drawbacks of a broad range of machine learning algorithms are examined, with a focus on how these computer algorithms may be applied to identify patterns linked to illnesses like stress, anxiety, and emotional depression. This study looks into the problems that this business encounters as well. These difficulties include combining behavioral data with extra environmental issues and physiological features from the immediate surroundings, the necessity for large and diverse datasets, the need for security of information, and the capacity to understand models. 2025 IEEE. -
Behavioral analysis of malicious activities in AI comprehensive analysis
The chapter provides a detailed overview of behavioral analysis evolved in AI security systems, from rule- based methods to advanced AI- driven approaches with verified threat prediction accuracy. Finance, healthcare, and telecommunications sectors show empirical evidence of modern systems by processing huge data volumes with exceptional threat detection capabilities. Research from Microsoft, IBM, and Google confirms AI- enabled security significantly reduces the time of threat identification compared to traditional approaches. The technical analysis reveals cloud-n ative solutions offer greater cost- efficiency and performance than on-premise alternatives, with measurable ROI improvements. The study examines behavioral analysis integration with machine learning, advanced persistent threat detection challenges, implementation strategies across different organizational contexts, and ethical considerations essential for developing effective security systems. 2026, IGI Global Scientific Publishing. All rights reserved. -
Before the Gig Economy Tracing the Transformations in Delhis Taxi Industry
Studying Delhis radio taxi industry, this paper traces the transitional process of traditional taxi services in the capital to radio taxi services and finally to the current app-based taxi aggregators. The radio taxi companies ruptured old kinship ties and informal relations with a combination of technology, surveillance, and financea process app-based taxi aggregators have further refined. There is also an account of the labour struggles in the industry that preceded the advent of the platform economy. 2022 Economic and Political Weekly. All rights reserved. -
Bed shear stress distribution across a meander path
Laboratory experimentation for bed shear stress distribution has been carried out in two sets of meandering channels. The channels have crossover angles of 110 and 60 constructed by sine-generated curves over a flume of 4 m width. Variations in bed roughness were studied for the meandering main channel. Bed shear stress distribution across a meandering length for the 110 and 60 channels was examined for different sinuosities and roughnesses. The boundary shear stress study illustrated the position of maximum shear along the apex section and across the meandering path. These variations were observed for different flow depths. A comparison of the bed shear among the three experimental channels was conducted, and the results were analyzed. 2024, IWA Publishing. All rights reserved. -
Becoming knowledge societies: A happiness framework for institutions of higher education in India
The transformation of Indian Higher Education Institutions (IHEIs) to knowledge societies require multiple coordinated interventions and actions on both the local and the global levels of institution administration, management, supply and demands of the economy and society. A vibrant knowledge society will not only require institutions support to plan and amend practices but also require the engagement of all stakeholders and the ability of individuals and society to imbibe new ways of thinking, working, and acting. It is vital to chart a direction and an approach that is in alignment with the local context and culture. At the supply front, IHEIs should initiate intervention programmes to enhance human capital through investment in a Happiness Framework and a shift in the workplace culture that requires conscious measures of intervention, which will drive institutional effectiveness and improve student experiences. This happiness framework should be integral and reinforced, first as an induction-training programme, and practised as institutional culture. Individuals, who are thus, trained at the local level of institutions, while participating in the global labour market with their increased skills and competencies will drive the IHEIs towards a fully functioning knowledge-based society. A knowledge-based society thus built to generate, disseminate, and use knowledge to improve the standard of living and the quality of life of citizens in an ethical and sustainable way will certainly make happiness as its ultimate goal and will focus on happiness as a process to improve efficiency and efficacy of the work force. 2019 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore),. -
Becoming an insider: The transformative power of learning local languages
Learning a language doesnt require masteryeven basic proficiency makes a difference. A shopkeepers delight when greeted in Kannada, or a rickshaw drivers surprise at hearing directions in the local tongue, turns routine interactions into shared experiences. These small gestures promote goodwill, making daily life more pleasant. -
BCI Radiology Images Converting into Report Using BERT and GPT
The construction of precise radiology reports from medical images is an essential aspect of Contemporary healthcare. Medical images such as X-rays, MRIs, CT scans, or ultrasounds. Also, it can make use of medical reports. Medical report has a bunch of details about each patients medical history, diagnosis, treatment plan, lab results, and more. This paper represents a theoretical examination. The paper mainly focuses on two prominent NLP models. One is BERT (Bidirectional Encoder Representations from Transformers) and the other one is GPT (Generative Pre-trained Transformer). This paper is going to validate their applicability to transforming brain-computer interfaces (BCI). This paper will utilize these radiology images in perfectly framed medical reports. By differentiating these models based on their Architectural properties, Linguistic processing abilities, and capability for clinical integration, this papers goal is to establish the most effective method for automated medical reporting. Merging of these insights from existing studies recommends that when BERT leads in context-based precision and getting an idea of complex medical terminology, GPT offers outstanding text-generation potential. This paper proposes that an intermixture procedure taking advantage of the strengths of both models may offer the most supreme solution for automated medical reporting, balancing precision with readability and clinical applicability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
BC-MBINet: A Novel Architecture for Accurate Classification of Breast Cancer with Microscopic Biopsy Images Using Deep Convolutional Neural Networks
Breast cancer (BC) is the second most frequent malignancy, accounting for roughly 25% of all cases of cancer. BC is caused by genetic, epigenetic, and environmental factors, and their interaction too. The diagnosis of a BC is a critical step in the treatment process, and histopathological imaging is required to determine the type of illness. Identifying a disease is an important stage in the treatment process. However, this time-consuming task is exhausting, and people are prone to making mistakes that go unnoticed, making it difficult to determine the severity of the condition and this diagnosing step also relies on a pathologists expertise. In this paper, we have developed a novel BC with microscopic biopsy images network (BC-MBINet) model using deep convolutional neural networks. Feature extraction is handled by a sequence of convolutional layers, nonlinearity is handled using LeakyReLU activations, and learning is stabilized by batch normalization. A last Softmax layer is employed for binary classification into benign and malignant tumors, and dropout layers are included to decrease overfitting. The model achieves state-of-the-art accuracy and resilience in discriminating BC types by being trained on a publically available dataset of microscopic biopsy images. The proposed model is capable of classifying between the benign and malignant BC tumors with 99.04% accuracy. The model gives state-of-the-art results in its accuracy in classifying BC tumors into Benign or Malignant. 2025 World Scientific Publishing Company. -
BAYESIAN SPATIAL TEMPORAL TREND ANALYSIS FOR DECISION MAKING AND RISK ASSESSMENT IN DENGUE INCIDENCE STUDIES: A CASE OF TAMILNADU
This study presents a Bayesian spatial-temporal analysis for studying Dengue incidence in Tamil Nadu, aiming to provide insights into decision-making and risk assessment strategies. Statistical models that allow a more accurate depiction of true disease rates by borrowing information from neighboring regions will help mitigate the effects of sparsely populated regions and deliver better inference. Perhaps the most conspicuous manner of modeling spatial dependence is to introduce spatially associated random effects within a Bayesian hierarchical setting. The Bayesian modeling and inferential framework are flexible and extremely rich in its capabilities to accumulate various scientific hypotheses and assumptions. The spatial and spatial temporal epidemiology is concerned with the description and analysis of spatial and spatial temporal variations in disease risk with respect to risk factors. As the primary aim of this work is to quantify the spatial disease pattern of dengue incidences apart from the mapping of disease modelling the disease and finding spatial clusters/hotpots is one important aspect in epidemiology is to find the temporal trends in or outside of clusters. In this study, a spatial-temporal trends model is fitted using the Leroux CAR priors set up for studying the spatial-temporal disease patterns with the estimation of the temporal trends with reference to dengue incidences in Tamil Nadu, India. 2025, Gnedenko Forum. All rights reserved. -
Bayesian and Non-Bayesian Parameter Estimation for the Bivariate Odd Lindley Half-Logistic Distribution Using Progressive Type-II Censoring with Applications in Sports Data
The Bivariate Odd Lindley Half-Logistic (BOLiHL) distribution with progressive Type-II censoring provides a powerful statistical tool for analyzing dependent data effectively. This approach benefits society by enhancing engineering systems, improving healthcare decisions, and supporting effective risk management, all while optimizing resources and minimizing experimental burdens. In this paper, the likelihood function derived under progressive Type-II censoring is generalized for the BOLiHL distribution. The well-known maximum likelihood estimation method and Bayesian estimation are applied to evaluate the parameters of the distribution. A study utilizing simulation techniques is performed to evaluate the performance of the estimators, using statistical analysis metrics for censored observations under a progressive Type-II censoring scheme with varying sample sizes, failure times, and censoring schemes. Additionally, a real dataset is studied to validate the proposed model, delivering impactful analyses for practical applications. 2025 by the authors. -
Bayesian and non-bayesian inference of the generalized Lomax distribution under a unified hybrid censoring scheme with applications in failure times in biomedical and aerospace materials
The unified hybrid censoring scheme is a combination of different types of censoring schemes used in reliability testing. This paper presents the statistical inference of generalized Lomax distribution under unified hybrid censoring scheme. The point and interval estimates of the parameters ?,?, and ? of the generalized Lomax distribution have been studied for unified hybrid censored data. In point estimation, the maximum likelihood estimation method is used for computing the estimates, and Tierney and Kadane estimation method is used for Bayes estimation. A 100(1-?)% approximate confidence interval and Bayesian credible intervals for the parameters ?,?, and ? have been computed in the interval estimation part. Mean squared errors are computed for all the estimates and comparison of estimates have been done. The results indicate that the Bayesian estimation method yields more accurate and reliable parameter estimates compared to the maximum likelihood approach. Finally, data representing failure times of fatigue fracture of Kevlar 373/epoxy and failure times of aircraft windshields have been used for point and interval estimations of all parameters as application of real-life scenarios. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025. -
Bayesian and Frequentist Estimation of Stress-Strength Reliability from a New Extended Burr XII Distribution
In this article, we propose and study a new three-parameter heavy-tailed distribution that uni-fies the Burr type XII and power inverted Topp-Leone distributions in an original manner. This unification is made through the use of a simple shift parameter. Among its interesting function-alities, it exhibits possibly decreasing and unimodal probability density and hazard rate functions. We examine its quantile function, stochastic dominance, ordinary moments, weighted moments, incomplete moments, and stress-strength reliability coefficient. Then, the classical and Bayesian approaches are developed to estimate the model and stress-strength reliability parameters. Bayes estimates are obtained under the squared error and entropy loss functions. Simulated data are considered to point out the performance of the derived estimates based on the mean squared error. In the final part, the potential of the new model is exemplified by the analysis of two engineering data sets, showing that it is preferable to other reputable and comparable models. 2025, National Statistical Institute. All rights reserved. -
Battle fatigue of Covid 19 warriors Heal the healers
[No abstract available] -
Basic human values of Indian management professionals: a demographic profile
This study tries to check the degree of basic human values among management professionals in India with considerable cultural and linguistic differences and how it varies across the different demographic influences. We have checked the impact of demographic variables like gender, age, education, type of organisation, place of residence, and work experience on basic human values. Hypotheses testing were conducted using MANOVA. It was inferred that the perception regarding the degree of basic human values differs among different management professionals based on their age, gender, education, type of organisation, and place of residence. Surprisingly, the work experience of the person does not have a significant influence on basic human values. Consequently, we imply that the demographics of an individual carve their basic human values. The findings and inferences of the proposed study will be of great importance to policymakers and recruiting managers to fetch the right candidate. Copyright 2023 Inderscience Enterprises Ltd. -
Base mediated spirocyclization of quinazoline: One-step synthesis of spiro-isoindolinone dihydroquinazolinones
A novel approach for the spiro-isoindolinone dihydroquinazolinones has been demonstrated from 2-aminobenzamide and 2-cyanomethyl benzoate in the presence of KHMDS as a base to get moderate yields. The reaction has been screened in various bases followed by solvents and a gram scale reaction has also been executed under the given conditions. Based on the controlled experiments a plausible reaction mechanism has been proposed. Further the substrate scope of this reaction has also been studied. This journal is The Royal Society of Chemistry. -
Basalt Fiber Composites in High-Performance Sports Equipment
This chapter explores the revolutionary role of basalt fiber composites in the development and production of high-performance sports equipment. Basalt fibers, produced from naturally occurring volcanic rocks, are a strong alternative to conventional reinforcement materials such as carbon and glass fibers. Their outstanding resistance to heat, moisture, and chemicals is complemented by their remarkable mechanical properties, including high tensile strength, impact resistance, and good vibration damping. These attributes make basalt fiber composites particularly suited for sports equipment subjected to dynamic loads and harsh environments, such as bicycles, tennis rackets, skis, snowboards, and surfboards. The chapter explores the most modern scientific and commercial advancements, demonstrating how basalt fiber composites can improve user comfort, reduce weight, and increase performance, durability, and safety. Illustrative examples compare basalt fiber composites with traditional materials to demonstrate their cost-effectiveness, reduced environmental impact, and successful applications. The manufacturing processes and potential challenges in adopting basalt fibers are discussed in detail. Finally, the chapter addresses emerging trends and prospects, positioning basalt fiber composites as a key material in the evolution of sustainable, high-performance sports equipment. This comprehensive overview provides valuable insights for researchers, manufacturers, and sports technology innovators. 2026 American Chemical Society -
Barriers to Sustainable Practices in the Construction IndustryA Bibliometric Analysis and Thematic Classification
Several studies explored the challenges involved in adopting sustainable practices in the construction industry from the perspective of different stakeholders, mainly developers, architects, consultants, and contractors. These challenges include financial constraints, lack of awareness, human resource issues, government policies, and market dynamics. However, a comprehensive bibliometric analysis and thematic classification of these barriers covering different aspects of sustainable construction is scarce owing to the fragmented nature of the literature. The objectives of the study are(1). To examine the literature on barriers to incorporating sustainable practices in building construction, operation and demolition (2). To provide a thematic classification of the barriers (3). To identify research gaps and suggest avenues for further research. The articles were retrieved from the Scopus database and refined using PRISMA guidelines. 221 studies were included in the bibliometric analysis. Biblioshiny was used to identify the publication trends, most relevant countries, authors, publications, and highly cited articles. Furthermore, 30 empirical studies were analysed using NVivo 12 software to classify the barriers into the following themes: finance-related, attitude and behaviour-related, knowledge and awareness-related, government-related and market-related. The sub-themes cover various issues related to the development of sustainable buildings: prefabrication, Building Information Modelling, Cloud Computing, procurement of sustainable materials, energy management, managing sustainable projects and construction and demolition waste management. These comprehensive insights could help practitioners and policy-makers develop strategies to drive the construction industry towards achieving its sustainability goals. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.