Mohammad Shahfaraz Khan

This study investigates the effectiveness of predictive performance of multiple modeling techniques in forecasting Nigeria’s Food Price Index (FPI) using monthly data from the Food and Agriculture Organization (FAO) Food Price Index spanning January 1990 to January 2025. Five major food subsector indices—Meat, Dairy, Cereals, Oils,and Sugar—serve as predictors. Initial regression on the raw series produced inflated R2 values, prompting diag‑nostic tests that revealed severe non‑stationarity and multicollinearity. These issues were addressed through a two‑step transformation: natural logarithmic conversion to stabilize variance, followed by first‑order differencing to achieve stationarity and eliminate false trends. Post‑transformation diagnostics confirmed full correction, with all variables stationary and multicollinearity reduced to acceptable levels. Using standardized log‑differenced data, five models were evaluated: Multiple Linear Regression, Artificial Neural Network, Random Forest, Support Vector Machine, and Long Short‑Term Memory (LSTM). On the transformed scale, the Support Vector Machine was the strongest performer; however, after back‑transforming predictions to the original scale through recursive exponential reconstruction, Random Forest achieved the highest accuracy (R2 = 0.9342, MAPE = 0.65%). LSTM models performed poorly, indicating a structural mismatch with differenced series lacking persistent trends. The study provides three methodological insights: (1) top performance on transformed data does not guarantee accuracy after inverse conversion; (2) log‑differencing is highly effective for resolving non‑stationarity, heteroscedasticity, and multicollinearity while preserving economic interpretability; and (3) ensemble averaging enhances recursive multi‑step forecasting stability. For practical policy applications requiring multi‑month food price projections, Random Forest is recommended, with Multiple Linear Regression serving as a transparent alternative for coefficient interpretation. These findings support the design of more informed food security interventions and market stabilization strategies in Nigeria.

The use of algorithmic decision systems is being expanded to high-risk areas like credit, recruiting, and distributing government resources. Despite the fact that these systems are usually claimed to be objective and efficient, there have been apprehensions about the likelihood of structural inequalities being perpetuated by the systems. This paper examines the effect of a fairness-aware pre-processing technique called reweighing on the performance of a predictive system in a controlled simulation environment. Using a synthetically created credit approval dataset with structural disadvantage embedded, we compare the performance of a logistic regression classifier with and without reweighing. Fairness is measured using demographic parity disparity (DPD), disparate impact ratio (DIR), and equalized odds difference (EO), along with predictive accuracy. In a single test scenario (seed = 42), reweighing does not improve all fairness metrics uniformly. However, when analyzed for robustness across 50 independent random seeds, we find modest average reductions in demographic parity disparity and equalized odds difference for reweighing, with little change in predictive accuracy. Threshold sensitivity analysis also shows that fairness metrics are sensitive to decision thresholds. These results show that fairness-aware pre-processing can lead to systematic improvements in expectation, although trade-offs across fairness metrics and performance remain context-dependent.

To address the global issue of climate change and create focused mitigation plans, accurate CO2 emissions forecasting is essential. Using CO2 emissions data from 1990 to 2023, this study assesses the predicting performance of five sophisticated models: Random Forest (RF), XGBoost, Support Vector Regression (SVR), Long Short-Term Memory networks (LSTM), and ARIMA To give a thorough evaluation of the models’ performance, measures including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are used. To guarantee dependable model implementation, preprocessing procedures are carried out, such as feature engineering and stationarity tests. Machine learning models outperform ARIMA in identifying complex patterns and long-term associations, but ARIMA does better with data that exhibits strong linear trends. These results provide important information about how well the model fits various forecasting scenarios, which helps develop data-driven carbon reduction programs. Predictive modeling should be incorporated into sustainable climate policy to encourage the adoption of low-carbon technologies and proactive decision-making. Achieving long-term environmental sustainability requires strengthening carbon trading systems, encouraging clean energy investments, and enacting stronger emission laws. In line with international climate goals, suggestions for lowering CO2 emissions include switching to renewable energy, increasing energy efficiency, and putting afforestation initiatives into action.

In the ever-evolving landscape of finance, derivatives, particularly options, have emerged as crucial instruments in stock trades worldwide. As the demand for sophisticated financial products grows, the need for accurate valuation models becomes paramount. Among these models, the Binomial Options Pricing Model (BOPM) stands out as a cornerstone in the evaluation of both call and put options. Through a step-by-step elucidation of the BOPM, this study aims to demystify its mechanics and empower practitioners with the tools to accurately assess option values. By considering various factors such as volatility, interest rates, and dividends, the BOPM offers insights into the intrinsic and extrinsic value of options, facilitating informed decision-making in financial markets.

The valuation of Goodwill (GW) has remained one of the several critical issues in financial analysis. This aspect is particularly important for mergers and acquisitions due to the significance of intangible assets. This study delves into the capitalization method of super profit (CMSP), a prominent technique for GW valuation, enhanced by the integration of statistical tools. Assessing a company’s excess profits over its average return on tangible assets is part of the CMSP. Finding the variables that have a significant impact on GW valuation, such as average profit, capital employed, and rate of return, is the main goal of this research. These issues are thoroughly investigated through statistical analysis to give stakeholders useful information for well-informed decision-making. Additionally, the study seeks to identify the external elements influencing this process as well as the internal aspects influencing GW valuation. Regression analysis, correlation matrices, response analysis and ANOVA are used to improve GW assessment and comprehension of the complex relationships between different factors.

This study provides insightful information about personal finance by examining the complex relationship between income and spending patterns in various occupations. It reveals strong relationships between income levels and expected results through regression analysis, providing insight into how different occupational groups are impacted by financial well-being. Building on this basis, a thorough one-way ANOVA and post-hoc tests show that students, academics, labourers, business owners, and government employees have remarkably different spending preferences. These results highlight the significant impact of occupation-specific variables on financial behaviour, including benefits, income stability, and social expectations. This research provides a path corresponding to specific policy interventions and programs aimed at financial literacy. By promoting economic resilience and well-being across a range of demographic and occupational landscapes, the ultimate objective is to cultivate a future in which responsible spending practices are shared realities rather than just ideals.

This study uses a Design of Experiment (DOE) approach and the Taguchi method to examine how different factors affect the expected return within the framework of the CAPM. The Capital Asset Pricing Model (CAPM) is a financial framework that forecasts an investment’s expected return by taking into account its systematic risk. This model factors elements like the risk-free rate, market risk premium, and beta to ascertain the expected return for a given asset. Typically, historical market data and financial analysis provide the inputs for CAPM. This method, frequently used in manufacturing and engineering, is modified for use in the financial domain to evaluate the importance of variables like beta, market risk premium, and the risk-free rate. The research identifies the factors that influence investment performance and shows how much each factor contributes to the expected returns. It is possible to ascertain each factor's percentage contribution to the expected return through statistical analysis. This work improves knowledge of CAPM and creates opportunities for improving investment decision-making processes by bridging the gap between financial theory and experimental design. Financial practitioners can achieve more reliable and accurate investment return forecasts by incorporating DOE techniques into their existing analytical toolkit.

This study examines customer attitudes towards blockchain technology (BCT)-supported banking applications in Oman and explores the relationship between these attitudes and behavioral intentions, highlighting the influencing factors. Blockchain Technology (BCT) has disrupted the banking sector, but understanding customer attitudes towards BCT-supported banking applications and their impact on behavior remains limited. In pursuit of this objective, a quantitative research design was employed, involving the collection of data from retail bank customers in Oman through a structured questionnaire. The collected data underwent rigorous analysis using a robust Structural Equation Model, confirming the constructs' validity, reliability, and distinctiveness. The study uncovered that BCT has a positive impact on various aspects of the banking sector, including customer service quality, regulatory compliance, secured transactions, and cost reduction. By augmenting transparency, efficiency, and trust in banking transactions, BCT significantly improves customer attitudes towards BCT-supported banks. Furthermore, the research reveals that positive attitudes play a pivotal role in influencing customers' intentions to prefer and continue using these blockchain-backed banks. The study also highlights the importance of trust factors and transaction frequency as moderators in shaping customer attitudes and behavioral intentions in this context. The insights gleaned from this research hold practical significance for banks operating in Oman, offering guidance for tailored marketing campaigns, product design, and aligning their offerings with customer expectations in the realm of blockchain-supported banking applications. Additionally, the study provides practical recommendations for the successful implementation and adoption of blockchain-supported financial applications within the banking sector.