Financial accounting and corporate finance are complementary mechanisms for value creation and risk optimization because both domains influence how firms measure performance, allocate capital, manage financing choices, and control financial risk. The article argues that accounting information is not merely a reporting output but a strategic input that supports investment, financing, dividends, liquidity, and risk-management decisions. Using a quantitative explanatory design, the proposed framework examines the effects of financial accounting information quality and corporate finance decisions on firm value, financial performance, and risk outcomes. Accounting information quality is conceptualized through accrual quality, earnings persistence, disclosure quality, audit quality, and reporting timeliness, while corporate finance decisions are represented by investment efficiency, leverage, dividend policy, and working capital management. Value creation is measured through indicators such as return on assets, return on equity, Tobin’s Q, and market-to-book ratio, whereas risk optimization is assessed through financial stability, liquidity, leverage, earnings volatility, and distress-risk indicators. The results support the view that high-quality accounting information improves corporate finance decision-making, enhances firm value, and strengthens risk optimization. Corporate finance decisions also partially mediate the relationship between accounting quality and value creation. Sustainable firm value, therefore, depends on the effective integration of reliable accounting information with strategic financial decision-making and risk governance.
A. Bhargava, Ankita Jain, A. Baral et al.· The Journal of Theoretical A...· 0 citations
Predictive decision analytics is gaining significance for better planning, resource allocation, and optimization of operations in business & industry. The issue of forecasting electricity prices is a relevant one as the volatility of prices directly affects procurement, production scheduling and operating costs. This study developed an artificial intelligence and machine learning framework for predictive decision analytics using electricity price forecasting as the application domain. A publicly available dataset comprising 23,304 observations and 11 variables was analysed. Historical electricity load, lagged electricity prices, day, and season were used as predictors of the current electricity price. Random Forest, XGBoost, and Gradient Boosting models were developed using an 80:20 train–test split. Model performance was evaluated using MAE, RMSE, MAPE, R², five-fold time-series cross-validation, and SHAP-based interpretation. All three models achieved comparable predictive performance. XGBoost produced the highest R² (0.8870) and lowest RMSE (927.43), while Gradient Boosting achieved the lowest MAE (575.34) and MAPE (10.44%). Time-series cross-validation reported a mean R² of 0.7823, confirming stable predictive capability under changing temporal conditions. SHAP analysis identified recent historical electricity prices, particularly P(T−1) and P(T−24), as the most influential predictors. The results prove that ensemble machine learning is a credible and explainable approach to electricity price prediction, which can be utilized for procurement planning, budgeting, production scheduling, and decision-making for business and industry. .
A. Bhargava· Journal of Intelligent Decis...· 0 citations
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