Search Results for capital-asset-pricing-model-capm-
Abstract
Abstract
This study tests the effect of capital costs on the relationship between free cash flow (FCF) and market value. The study selected twenty-six corporations that were listed on the Jordan securities market from 2010 to 2019. The FCF is an independent variable, cost of capital is a mediation variable (proxy of WACC), and market value added (proxy of firm’s value) is a dependent variable. Baron & Kenny's methodology and the Sobel-test were used to analyze the data of the four hypotheses, including the mediation effect of capital costs on FFC & MVA. Based on the Sobel test results, there is a partial mediation effect of the cost of capital between the free cash flow and the market value added of the firm, and the free cash flow is positively related to the market value added. Therefore, FCF has the capability to send positive signals to financial market participants about the firm's performance.
Keywords: - Free Cash Flow (FCF), Weighted Average Cost of Capital (WACC), Market Value Added (MVA), Mmarket Value of Equity (MVE), Capital Asset Pricing Model (CAPM), Beta Coefficient (β).
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.