FNT502

Articles

This article explores the impact of alternative data on the accuracy of financial forecasts, particularly highlighting how the availability of short-term-oriented data, such as social media or transaction data, influences forecasters’ focus on short-term versus long-term predictions. Through a combination of theoretical modeling and empirical analysis, the authors demonstrate that while alternative data can enhance the precision of short-term forecasts, it may simultaneously reduce the informativeness of long-term forecasts. This effect, termed the "Horizon Effect," reveals a trade-off in forecasting accuracy across time horizons, driven by cognitive constraints and the costs associated with processing data. By examining this shift through the lens of equity analysts' use of alternative data and the evolution of forecast informativeness over time, the study provides essential insights for investors and analysts navigating the growing landscape of data sources.

This article provides an in-depth examination of time series forecasting techniques, tracing the evolution from traditional methods like ARIMA and exponential smoothing to advanced machine learning and deep learning models, including LSTM and RNN. It categorizes various forecasting approaches, analyzing their strengths, limitations, and suitability across diverse fields, including finance, healthcare, and energy management. The paper also addresses significant challenges in time series forecasting, such as handling seasonality, trends, and data irregularities, as well as model-specific issues like interpretability, overfitting, and adapting to evolving data patterns. The authors further discuss emerging trends and propose future directions, emphasizing the integration of explainable AI, probabilistic forecasting, and scalable methods to improve prediction accuracy and adaptability in real-world applications.

This study explores the use of machine learning to analyze financial accounting data, providing critical insights for investment and business decisions. The researchers employed an exploratory data analysis (EDA) on financial statements, like balance sheets and income statements, to measure parameters such as debt-to-equity ratio, net profit margin, and inventory turnover. These indicators serve as the foundation for evaluating company profitability, supporting data-driven decision-making. Subsequently, four machine learning models (Linear Regression, K-Nearest Neighbor, Support Vector Regressor, and Decision Tree) were applied to predict total revenue. By using financial analytics in this manner, the study demonstrates how machine learning can support more accurate forecasting and risk management in corporate finance, with potential implications for real-time financial monitoring and expanded applications in diverse industries. 

This review explores the ethical considerations of AI in financial services, addressing issues such as bias, transparency, accountability, privacy, and algorithmic fairness. As AI is integrated into financial services, benefits like enhanced efficiency and personalized services emerge, yet ethical challenges also arise. AI algorithms may inadvertently reinforce biases found in historical data, leading to unfair outcomes in decisions such as credit scoring and loan approvals. Additionally, opaque AI decision-making challenges transparency, while the extensive data collection required for AI raises privacy concerns. The authors propose solutions including algorithmic audits, inclusive data practices, and regulatory frameworks to ensure fairness and accountability. The paper calls for further research in bias detection, explainable AI, comprehensive ethical frameworks, and consumer education to build a trustworthy AI ecosystem in financial services.