SciPap, Volume 34 (2026)

Open Access Article SciPap-2417

by G Prasanna Kumar, Valluri Venkata Rao, Parla Suresh

Abstract: Derivatives markets combine leverage, nonlinearity, and short holding horizons, creating conditions in which behavioral distortions may translate into amplified losses and market stress. This study develops an interpretable, episode-based machine-learning framework to detect bias-consistent trading behavior in exchange-traded derivatives and to translate micro-level signals into a market-level monitoring indicator. Anonymized order- and trade-level records are transformed into position episodes and merged with contract characteristics and microstructure controls. Theory-aligned proxy rules generate weak labels for disposition/loss aversion (time-to-close asymmetry, roll-loser patterns), overconfidence (turnover, leverage-at-entry, short-gamma exposure), lottery demand (low-delta preference), and herding (crowding/flow correlation). Models are trained using time-respecting splits with nested cross-validation and randomized hyperparameter search, and evaluated on a strictly out-of-time test window. Tree-based methods provide moderate but stable discrimination (e.g., Gradient Boosting ROC-AUC ≈ 0.62) with acceptable calibration, while explanation analysis indicates dominance of loss-related and leverage/turnover signals over market-state variables. Aggregating episode probabilities yields a daily Bias Index that is positively associated with next-session realized volatility and supports an alert rule capturing a meaningful share of volatility spikes with manageable false alarms. Limitations include proxy-label noise, market-specific coverage, and associative (non-causal) inference. Practical value arises from transparent scoring and channel attribution that can support targeted nudges, suitability prompts, and surveillance prioritization in periods of elevated behavioral risk.

Keywords: Behavioral Finance, Machine Learning, Derivatives, Investor Biases, Options Market, Interpretable Ai.

JEL classification: G12, G13, G41, C45, C55, C58

Open Access Article SciPap-2482

by Quyet Xuan Nguyen, Thi Bich Thuy Nguyen

Abstract: Artificial intelligence (AI) applications are currently a new trend, capable of supporting the synthesis, analysis, and discovery of new knowledge. However, concerns have also arisen regarding the unauthorized use of research results and the increase in plagiarism if not controlled. This study develops and tests a new model, with a system of validated indicators, to analyze survey data from 322 public lecturers in the research area. Using a mixed methodology (qualitative and quantitative), a measurement scale system was developed, and a structural equation modeling (SEM) was used to test hypotheses and analyze the suitability of the research model. The research results show that all six factors of AI application, including supporting conditions; experiential habits; usage skills; application policies; application expectations; and application trends, significantly influence the variables of scientific research and lecturer work performance. Therefore, implications for enhancing the application of AI in scientific research, aimed at improving the work performance of lecturers, are proposed. In addition, the study also found that scientific research is one of the criteria for evaluating the performance of public university lecturers, helping higher education managers to develop appropriate policy implications to enhance the application of AI in scientific research and improve lecturer work performance.

Keywords: Vietnam, Scientific Research, Application Of Artificial Intelligence In Education (Aied), Technology Management, Digital Transformation In Education

JEL classification: I23, I29, O32, O33

Open Access Article SciPap-2547

by Viktor Prokop, Marek Brokes, Mahnoor Minhas, Dominika Brozkova, Jan Stejskal, Cali Nuur, Carlo Giglio, Jens Horbach, Wolfgang Dieter Gerstlberger, Vikas Kumar

Abstract: Over the past few decades, the circular economy (CE) has shifted from a normative sustainability vision to a dominant framework in policy agendas, corporate strategies, and academic research. In Europe, for instance, CE has evolved into a pivotal strategy for tackling environmental issues and decreasing the negative impact of high-emitting sectors by translating CE principles into proactive tools encouraging the green transition of European economies. However, while widely promoted as a response to climate change, biodiversity loss, and resource depletion, the CE has also attracted increasing scrutiny due to limited real-world impacts, rebound effects, and the persistence of efficiency oriented approaches that leave prevailing production and consumption patterns largely unchallenged. To address this tension, this paper presents an analysis of 1,533 CE papers in the context of European countries, focusing on dominant research themes, intellectual structures, and emerging trends in the field. Our findings demonstrate the growing role of the “utopia – paralysis” tension, the relational turn beyond technocentric circularity, circular business model innovation, coopetition initiatives, platformization of the CE, just transition, and circular justice. Furthermore, alongside these dominant themes, we assess the visibility and positioning of sufficiency and regenerative economy perspectives within the broader CE literature. By situating these emerging concepts within the evolving research landscape, the paper contributes to ongoing debates on the maturity, direction, and transformative ambition of the CE as a sustainability paradigm.

Keywords: Bibliometric Analysis, Circular Economy, Sufficiency, Regenerative Economy, Business Model Innovation, Just Transition, Circular Justice

JEL classification: O, O3, Q01, Q56

Open Access Article SciPap-2457

by Tran Hoang Vu

Abstract: This study examines the determinants of total factor productivity (TFP) and real GDP growth in 90 middle-income countries over 1990–2020, and contributes by separating the drivers of productivity from the drivers of growth within the same empirical framework to clarify the role of FDI. Using annual panel data, we estimate a TFP equation and a growth equation with country fixed effects and panel EGLS with cross-section weights, reporting White cross-section robust standard errors; this specification is appropriate for large cross-country panels because it controls for time-invariant national characteristics and addresses heteroskedasticity across countries. The results indicate that, conditional on country effects, TFP is linked mainly to domestic structure and macro-fiscal conditions: larger sectoral shares in agriculture and manufacturing and a higher import share are associated with lower TFP, while government consumption is positively related to TFP, and FDI and investment are not robust predictors of productivity. In contrast, GDP growth is positively associated with TFP, FDI, and investment, while inflation and government consumption are negatively related to growth and imports are growth-enhancing. The policy implication is that middle-income countries should prioritise productivity-raising structural upgrading, improve the efficiency and composition of public spending towards skills and infrastructure, and strengthen absorptive capacity so that FDI and trade translate into sustained productivity gains and higher long-run growth.

Keywords: GDP, Fdi, Middle-Income Countries, Macroeconomic, Total Factor Productivity.

JEL classification: B22, C58