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Budget Forecasting and Integrated Strategic Planning for Leaders

Budget Forecasting and Integrated Strategic Planning for Leaders ArXiv ID: 2510.26035 “View on arXiv” Authors: Matt Salehi Abstract This study explored how advanced budgeting techniques and economic indicators influence funding levels and strategic alignment in California Community Colleges (CCCs). Despite widespread implementation of budgeting reforms, many CCCs continue to face challenges aligning financial planning with institutional missions, particularly in supporting diversity, equity, and inclusion (DEI) initiatives. The study used a quantitative correlational design, analyzing 30 years of publicly available economic data, including unemployment rates, GDP growth, and CPI, in relation to CCC funding trends. Results revealed a strong positive correlation between GDP growth and CCC funding levels, as well as between CPI and funding levels, underscoring the predictive value of macroeconomic indicators in budget planning. These findings emphasize the need for educational leaders to integrate economic forecasting into budget planning processes to safeguard institutional effectiveness and sustain programs serving underrepresented student populations. ...

October 30, 2025 · 2 min · Research Team

Comparative Study of Long Short-Term Memory (LSTM) and Quantum Long Short-Term Memory (QLSTM): Prediction of Stock Market Movement

Comparative Study of Long Short-Term Memory (LSTM) and Quantum Long Short-Term Memory (QLSTM): Prediction of Stock Market Movement ArXiv ID: 2409.08297 “View on arXiv” Authors: Unknown Abstract In recent years, financial analysts have been trying to develop models to predict the movement of a stock price index. The task becomes challenging in vague economic, social, and political situations like in Pakistan. In this study, we employed efficient models of machine learning such as long short-term memory (LSTM) and quantum long short-term memory (QLSTM) to predict the Karachi Stock Exchange (KSE) 100 index by taking monthly data of twenty-six economic, social, political, and administrative indicators from February 2004 to December 2020. The comparative results of LSTM and QLSTM predicted values of the KSE 100 index with the actual values suggested QLSTM a potential technique to predict stock market trends. ...

September 4, 2024 · 2 min · Research Team