Financial markets are characterized by rapid volatility and dynamic fluctuations, which necessitate robust approaches to financial risk management. Traditional statistical techniques often fall short in capturing the nonlinear and complex interactions that influence stock price behavior. This study aims to develop and evaluate predictive models for Apple Inc.’s stock prices by integrating statistical analysis with machine learning approaches. Using 17 years of historical stock market data, we examine price dynamics, trading volume patterns, and volatility trends. Descriptive statistical analysis reveals a significant negative correlation between trading volume and stock price (r = –0.523), while daily return volatility is measured at 11.5369, underscoring inherent financial risks. Two predictive models—Linear Regression and Random Forest—are employed, utilizing features such as opening price, daily high and low prices, and trading volume. Model performance is assessed through mean absolute percentage error (MAPE), yielding error rates of 0.0143 and 0.0161, respectively, with Random Forest demonstrating slightly superior accuracy. The findings highlight the potential of data-driven approaches for enhancing stock price forecasting and financial decision-making. By combining traditional statistical methods with machine learning techniques, this study contributes to the literature on financial risk management and offers practical insights into how advanced predictive analytics can improve strategic responses to market uncertainty.

Knowing what influences researchers on conducting applied research in developing countries is of interest to policymakers in higher education institutions. This study utilized a quantitative approach to investigate factors that drive academics in the higher education institutions in the Kurdistan Region of Iraq on conducting applied research. For this purpose, data were collected using a survey from researchers at different universities and fields and then were analyzed using the probit model. Based on the main findings of this study, the probability of conducting applied research increases as the years of experience increase when a researcher has external funding, while the probability decreases as the years of experience increase when a researcher has only the knowledge of applying to external funding without actually having any funding available. This study recommends some actions that help in promoting the research field in the higher education institutions in the Kurdistan Region of Iraq.