I’m  Hivi Malu Omar

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.

This study examines the relationship between dividend policies and stock prices among companies listed on the Iraq Stock Exchange from 2010 to 2020. This study utilizes a descriptive analytical approach, drawing on data from annual financial statements and disclosures published by the Iraq Stock Exchange. This study employs a simple linear regression model to analyze the impact of dividends per share and retained earnings per share on the market share price. The findings indicate a negative, statistically insignificant relationship between cash dividends and share prices, whereas retained earnings show a direct positive and significant correlation with stock valuations. Prior research in the Iraqi context has identified cash liquidity as the main determinant of dividend payout decisions, with shareholders favoring regular distributions, regardless of ownership. Studies also find a positive link between profit retention, investment opportunities, and dividends. However, broader challenges facing emerging stock exchanges, such as underperformance, speculation, and a lack of investor knowledge, can negatively impact market performance. This study provides a comprehensive, long-term analysis of dividend payout-stock price dynamics in the Iraqi market, offering valuable insights into how this relationship manifests in an emerging economy setting. The results contribute to the theoretical understanding of the factors driving stock valuation, and have practical implications for investment strategies and corporate dividend policies in Iraq and similar markets. Key recommendations include advertising campaigns to encourage savings, providing investment advisory services, and improving financial disclosure to build investor confidence.