Search Results for stock-returns
Abstract
The research explores to test the Fama-French five-dimensional model in analyzing the returns of ordinary shares using profitability and investment as a measure of the model. Financial data needed for research. To solve the research problem, some mathematical laws and related statistical methods were adopted to analyze the data of the companies covered by the research. The results of the research indicated that the factors of profitability and investment, respectively, are the most important factors affecting the returns of the shares of the sample companies and their market value, on the one hand, and on the other hand, it requires the use of a five-dimensional Fama-French model to analyze the returns of ordinary shares from companies and the clarity and transparency of their financial information so that The financial analyst can use this model and rely on it in estimating stock returns, in addition to the existence of an efficient market in which the research sample companies operate, in addition to the ability of the F-F-5 model to explain the returns of the shares of the research sample companies by (67%), and this indicates that it contains (67%) ) of the risk factors that accompany its investment.
Keywords: profitability, investment, stock returns, Fama-French five-dimensional model
Abstract
Capital structure is considered a fundamental topic in the field of financial management due to its vital role in supporting corporate financial decisions and its direct impact on financial performance and returns. This study aims to analyze the role of capital structure indicators on abnormal stock returns, with a specific focus on industrial companies listed on the Iraq Stock Exchange, as these returns serve as important indicators of market efficiency and the influence of financial decisions.
The study addresses the relationship between the components of capital structures such as debt and equity—and deviations in stock returns from expected values. These deviations may reflect unexpected opportunities or additional risks borne by investors. The research problem was formulated through inquiries into the impact of the financing mix used by companies on abnormal returns, as well as the extent to which these returns are affected by financial risk and the environmental challenges faced by the Iraqi market.
The significance of this study lies in its attempt to explain how changes in financial leverage influence abnormal returns. It also provides practical indicators that enhance the efficient use of financial resources and help investors gain a better understanding of how to evaluate their returns and expectations based on the components of capital structure. Furthermore, the study seeks to offer insights and recommendations that support financial decision-makers in choosing a balanced capital structure that contributes to growth and risk reduction. The study adopts an analytical approach that integrates theoretical foundations with empirical measurement of financial leverage indicators and abnormal returns, by analyzing data from a sample of listed industrial companies.
Abstract
The research aimed to identify how to build models for selecting the optimal mix of investment portfolios, as well as presenting the stock returns of fifty-four companies listed on the Iraq Stock Exchange to facilitate investors' choice of the best investment alternatives by comparing stock returns with the financial market returns. Using monthly data spanning the period from March 2020 to May 2024, the research examined fifty-four companies listed on the Iraq Stock Exchange, covering all traded sectors. The research also demonstrated the importance of beta analysis (β) in classifying stocks into defensive and offensive, which helps investors build balanced financial portfolios that manage risks more effectively. The research reached several conclusions, the most important of which is that the pricing of capital assets depends on two important factors: the risk premium and the beta value. Consequently, any increase in either of these factors will be directly reflected in the prices of corporate assets.
Abstract
The current research presents the idea of using deep learning tools and employing them in financial aspects due to their significant role and ability to explore unobservable aspects in light of financial models governed by a set of restrictions, conditions and linear relationships. On the other hand, the nature of financial data that tends to be non-linear and suffers from the missing of monthly closing prices, which imposes a state of data loss. All of this provides preference for deep learning models, including the neural network tool. The research aims to estimate financial returns in light of the capital asset pricing model CAPM as a financial model and neural networks as a deep learning tool in addition to the mask & padding tool to address the problem of missing data. The knowledge gap was determined by the inability of the capital asset pricing model to explore hidden and invisible aspects and overcome non-linear relationships. The research sample consisted of 42 organizations listed on the Iraq Stock Exchange for the period from 1/1/2021 to 31/12/2024 with 60 observations. The research concluded that the neural network tool is able to overcome the determinants in light of financial models and provide accurate estimates of returns are close to estimates under the capital asset pricing model.