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The main intention of this study is to use the accounting data using CAPM to determine the stock prices/returns for the Nigerian capital market. In this study, the independent variable is the prediction of the stock prices and the dependent variable is stock prices in the market. The proxy that is used in this study to measure the dependent variable is CAPM in Nigerian Market. The most important and interesting phenomenon to investors is the analysis from financial market pertaining to stock returns. The research method employed is quantitative which is unlike qualitative as a way of assessing the stock price. The study mainly aims at assessing the correlation of beta factors and the predictability of stock returns from Nigerian firms listed on the stock exchange. In order to boost the beta estimates and mitigate statistical problems resulted from incorrect measurement, the securities were combined into portfolios. In conclusion the study employs ordinary least squares (OLS) regression technique and obtained beta value which is positive and found conclusive evidence for using CAPM and is thus consistent with Nigerian stock market prices. The CAPM has implications for asset pricing since it shows how to calculate the requisite rate of return to assess the value of the stock prices with any given amount of systematic risk (beta) and since the beta is positive hence the policy makers and investors in the Nigerian stock market would make better informed decisions.

This study investigated the risk-return relationship using the Capital Asset Pricing Model (CAPM), Carhart four-factor Model (C4FM) and Fama and French Multifactor Models (FFMMs): F3FM and F5FM. This study analyzed the JSE ALSI returns of the South African market, in relation to the risk factors constructed by the data of twenty-six emerging markets, over the sample period from October 2000 to October 2021. The methodology employed was a comparison of the conventional regression analysis and novel Bayesian approach. Regression analysis estimated by Ordinary Least Squares (OLS) is one of the foremost methods used in South Africa. However, such an approach does not take into account the properties of the price data, namely, the asymmetric, volatile and random nature. While the Newey-West adjustment is one way to assist in capturing autocorrelation and volatility, it fails to consider asymmetry. The Bayesian approach accounts for all the aforementioned properties, overcoming the fundamental flaws of regression analysis. The additional model diagnostics highlighted in this study improved the Bayesian approach' robustness. The risk factors of the FFMMs estimated by regression analysis, with and without the Newey-West adjustment, were insignificant, whereas the Bayesian test results were significant. This finding clearly highlighted that model choice impacts the significance of parameter estimation and the financial decisions of investors, firms and policymakers. Jensen's alpha revealed CAPM to be optimal but none of the asset pricing models fully captured the risk premia. Thus, returns should be investigated with different risk measures for future research purposes.

The valuation of banks is inherently complicated because of the uncertainties arising from their information opaqueness and inherent risks. Unlike non-banking firms, banks require specialised equity-side valuation approaches. This study addresses a gap in the literature by examining valuation methods used by bank equity researchers. The study used a total of 201 reports on South African banks (2018–2023), 56 reports on Nigerian banks (2018–2023), and 27 reports on Kenyan banks (2018–2023) to investigate the bank equity valuation methods utilised by analysts in the employ of Investec Ltd. and Standard Bank Group Ltd. The study’s findings show that Investec’s South African analysts predominantly used the warranted equity method, based on book value (BV), and return on equity (ROE), for valuing shares throughout the South African, Nigerian, and Kenyan banks surveyed. Furthermore, Standard Bank Group’s analysts employed this method, incorporating tangible net asset value (tNAV) and return on tangible equity (ROTE), for South African and Nigerian banks, but in Kenya their analysts used the residual income model to value the equities of the five Kenyan banks they covered. These findings suggest that the warranted equity method and the residual income model are the mostly used bank equity valuation methods in South Africa, Nigeria, and Kenya. The study concludes with relevant recommendations, offering significant insights for banks, regulators, and investors to make knowledgeable decisions concerning equity valuation.

AbstractThis study expands previous research by adding intellectual capital to the capital asset pricing model and deepening the measurement of intellectual capital using more comprehensive proxies. This study is novel in that it is related to evaluation according to market developments using tests on potential factors in the asset pricing model by adding intellectual capital variables. Intangible assets have the explanatory and predictive power of size and value strategies in predicting returns. This study uses a sample of all industries from all companies listed on the Indonesia Stock Exchange during 2012–2022; the sample totals 5767 firm-years. The results show a significant relationship between the six-factor capital asset pricing model, intellectual capital, and excess stock returns. The robustness test supports these findings, suggesting that intellectual capital performs better financially as an intangible asset. This study contributes to the literature regarding the role of intellectual capital in the capital asset pricing model, which can be helpful when making investment decisions in emerging stock markets.

Understanding why and how asset prices rise is a major concern for industry, policymakers and investors. This has attracted the attention of researchers to the issue of asset pricing, especially high-risk assets such as stocks. The single-factor capital asset pricing model (CAPM) and the Fama and French (1993) three-factor model are some of the well-known models that seek to explain investors' expectations for high-risk asset returns, and various multifactorial extensions of these models have been proposed. One of the latest versions of these models is the seven-factor model, which is expected to have a higher explanatory power than the CAPM model and the Fama and French three-factor model. The purpose of this study is to evaluate the seven-factor model in Tehran Stock Exchange and compare it with CAPM model and Fama and French model in order to predict the expected returns of companies operating in the financial sector of Tehran Stock Exchange during the period 2016: 1 to 2019: 12. In this study, the estimation method is the Auto Regressive Distributed Lag (ARDL). The results of the study show that the seven-factor model gives a better estimate of the expected return than the CAPM and Fama and French models. Also in the seven-factor model, the variables of book value to market value ratio, momentum, liquidity turnover ratio and commodity index have a negative and significant effect on expected returns. But market factor, government bond index and company size factors do not have a significant effect on expected returns.

The blueberry industry is growing significantly in Colombia. For investors, cost analysis plays a key role in project planning for any agribusiness. This paper is an exploratory study, with a descriptive scope to identify components of establishment and production costs of growing blueberries in Colombia. Data were collected and analyzed from a project aimed at establishing a 1 ha blueberry crop in Zipaquirá and 18 blueberry small farms in the Cundiboyacense highlands. Components were applied to estimate the capital investment and to identify the establishment, production, and maintenance costs of this project. Decision investment analysis, including discount rates for the capital asset pricing model (CAPM), were used to compute the net present value (NPV), internal rate of return (IRR), and the return on investment (ROI). Production costs are still expensive due to the Colombian peso (COP) exchange rate compared to the US dollar (USD). Results show advantages for promoting the establishment of blueberry crops because of suitable climatic conditions for continuous production throughout the year in Colombia. Labor costs and utilities are cheaper compared to other countries and represent a competitive advantage in the investment. Intermediation suppliers affect establishment costs for commercial reasons. Blueberry production cost analysis in Colombia may facilitate agribusinesses to promote projects in Colombian agriculture.

The purpose of this study is to examine the moderation effect of the audit committee on the linkages between audit firm size, industry specialization, and the cost of equity capital with firm size and financial leverage as a control variable. This study is one of few that compared the accuracy of Ohlson and Capital Asset Pricing Model in explaining the cost of equity capital and the moderation role of the audit committee. A sample of 123 manufacturing firm-year observations was drawn from Indonesian-listed companies for five years period (2016-2020). The results show that while the audit committees significantly moderate the negative relationship between firm size on the cost of equity capital, there is no significant evidence of the moderation role of the audit committee on the relationship between industry specialization and the cost of equity capital using the Capital Asset Pricing Model. On the contrary, the moderation role of the audit committees on the effect of industry specialization toward the cost of equity capital under Ohlson was supported in the study. Further, it found that the Ohlson has a better performance than the Capital Asset Pricing Model in explaining the cost of equity capital as the Ohlson measurement expressed the role of earnings per share which represent a more real rate of return.

Abstract In this paper, we examine the presence of short-term and long-term momentum returns in Indian stock market. The study also tries to shed light on the power of asset pricing models and select macroeconomic variables in explaining momentum returns. The results confirm the presence of short-term and long-term momentum returns in Indian stock market. It is also found that Carhart four-factor model’s performance is relatively superior to other factor models such as one factor capital asset pricing model and Fama–French three-factor model in terms of capturing momentum returns. Finally, macroeconomic variables which are considered for analysis do not have any power to explain momentum returns.

In doing investment, an investor certainly avoids risk; thus, the investor needs a model in making predictions to forecast the return of shares. There are two models to predict this: Capital Asset Pricing Capital (CAPM) and Arbitrage Pricing Theory (APT). The purpose of this study is to find out which models are more accurate in determining investment options, especially during the Covid-19 pandemic in companies that are included in the LQ 45 Index group. The population in this study is 50 companies listed in LQ 45 from February 2020 - July 2021. The sampling technique used in this study is purposive sampling. The data used in this study will be processed through Ms.Excel and SPSS Version 21. The data analysis techniques used in this study are the Basic Assumption Test consisting of Normality Test and Homogeneity Test, Mean Absolute Deviation (MAD), and hypothesis testing consisting of independent t-test samples. The results in this study show that Model is accurate in predicting stock returns in the Covid-19 pandemic is a CAPM model this is because the value of MAD CAPM is smaller than mad APT. Furthermore, independent t-test samples showed that H0 was rejected which meant that there was a difference in accuracy between CAPM and APT in calculating the return of LQ 45 shares. The implication of this study are expected to provide references to investors and potential investors as a source of information in decision making to make investments in this pandemic period.

Using the capital asset pricing model, this article critically assesses the relative importance of computing ‘realized’ betas from high-frequency returns for Bitcoin and Ethereum—the two major cryptocurrencies—against their classic counterparts using the 1-day and 5-day return-based betas. The sample includes intraday data from 15 May 2018 until 17 January 2023. The microstructure noise is present until 4 min in the BTC and ETH high-frequency data. Therefore, we opt for a conservative choice with a 60 min sampling frequency. Considering 250 trading days as a rolling-window size, we obtain rolling betas < 1 for Bitcoin and Ethereum with respect to the CRIX market index, which could enhance portfolio diversification (at the expense of maximizing returns). We flag the minimal tracking errors at the hourly and daily frequencies. The dispersion of rolling betas is higher for the weekly frequency and is concentrated towards values of β > 0.8 for BTC (β > 0.65 for ETH). The weekly frequency is thus revealed as being less precise for capturing the ‘pure’ systematic risk for Bitcoin and Ethereum. For Ethereum in particular, the availability of high-frequency data tends to produce, on average, a more reliable inference. In the age of financial data feed immediacy, our results strongly suggest to pension fund managers, hedge fund traders, and investment bankers to include ‘realized’ versions of CAPM betas in their dashboard of indicators for portfolio risk estimation. Sensitivity analyses cover jump detection in BTC/ETH high-frequency data (up to 25%). We also include several jump-robust estimators of realized volatility, where realized quadpower volatility prevails.

This study investigates the time-varying integration of stock markets from a global and regional perspective, the consequences of two major global financial crises, i.e., the Asian Financial Crisis and the subprime mortgage, and the Crisis triggered by COVID-19. We contribute to the growing amount of literature on market integration, especially on the role of regional to global market integration. Although regional integration encourages an acceleration of global integration, the effect of a regional factor is not uniform among regions. It is important to understand regional to global market integration and the consequences during the crises. This study employs time-series data from economic territories based on the Morgan Stanley Capital International (MSCI) Asia-Pacific classification. It introduces an alternative measurement of time-varying integration by considering the correlation of regional and global markets using a simple international model, equivalent to the capital asset pricing model (CAPM). The result shows that the market integrations are time-varying both globally and regionally. The domestic markets are affected by the global market and its regional market, as the role of a regional market emerges during the financial crisis period. We find the different responses of stock markets during the Covid-19 period as a dominant factor to exacerbate the market return globally. In the long run, the upward trend for the regional market integration in both developed and emerging markets is inherent to the global market integration. DOI: 10.26905/jkdp.v25i3.5822

This article analyzes the effectiveness of various beta coefficient modifications in forecasting on the Russian stock market. Objective: To test the hypothesis of the superiority of modified beta coefficients in forecast accuracy. Methods: Calculating and comparing the stock returns of ten companies that play a key role in the Russian economy based on three beta coefficient modifications: classic, Monkhouse, and two beta. The criteria were used for verifying sample homogeneity in order to test the hypothesis that modified beta coefficients produce more accurate forecasting. Results: The analysis showed that, in most cases, none of the three models obtained relevant results for the Russian companies. None of the modifications tested can be recognized with high confidence as more accurate than traditional beta coefficients. Conclusions: In the contemporary Russian stock market, the CAPM does not allow one to produce satisfactory forecasts on stock returns.

In recent years, the rapid and significant development of emerging markets has globally led to insight from potential investors and academicians seeking to assess these markets in terms of risk inheritance. Therefore, this study aims to explore the validity and applicability of the capital asset pricing model (henceforth CAPM) and multi-factor models, namely Fama–French models, in Pakistan’s stock market for the period of June 2010–June 2020. This study collects data on 173 non-financial firms listed on the Pakistan stock exchange, namely the KSE-100 index, and follows Fama-MacBeth’s regression methodology for empirical estimation. The empirical findings of this study conclude that small portfolios (small-size companies) earn considerably higher returns than big portfolios (large-size companies). Ultimately, the risk associated with portfolio returns is reported to be higher for small portfolios (small-size companies) than for big portfolios (large-size companies). According to the regression output, the CAPM was found to be valid for explaining the market risk premium above the risk-free rate. Similarly, the FF three-factor model was found to be valid for explaining time-series variation in excess portfolio returns. Later, we added human capital into FF three- and five-factor models. This study found that the human capital base six-factor model outperformed the other competing asset pricing models. The findings of this study indicate that small portfolios (small-size companies) earn more returns than big portfolios (large-size companies) to reward the investor for taking extra risks. Investors may benefit by timing their investments to maximize stock returns. Company investment in human capital adds reliable information, replicates the value of the company and, in the long term, helps investors make rational decisions.

This study examines the role of quality minus junk factor in explaining cross-sectional differences in stock returns for the equity market of Pakistan. A new factor quality minus junk has been added to the existing Fama & French three factor model (1993) as a fourth factor to check whether it is priced as a risk factor. The study also compares the explanatory power of the newly proposed model with the existing single-factor Capital Asset Pricing Model (CAPM) and Fama & French three factor model (1993). A high quality data set of 70 non-financial firms listed on the PSX is employed as a sample for the period 2008- 2016. Results show that quality minus junk factor is priced in the equity market of Pakistan and this newly proposed four factor model has significantly higher explanatory power than the existing three factor model.

This study determines the accuracy level of CAPM and APT in determining the expected return of LQ45 and comparing the expected return from CAPM and APT models. This study uses descriptive and comparative research approaches. The population is all stocks listed in the LQ45 index while the sampling method used is purposive sampling with stock criteria that have complete data for the period November 2015 - November 2019. This study uses an independent sample t-test in testing the expected return differences between the CAPM and APT models. The result showed that the CAPM Model was more accurate in determining the expected return of LQ45 stocks compared to the CAPM method. The result also showed that there was a significant difference in expected return between CAPM and APT models. Based on this result, investors can use the CAPM model in predicting the returns of the stock listed on the LQ45. For further research, can use another index in the capital market as a research object, used a longer period to get a more accurate result, and add some more macro variables.

This study is intended for knowing the Sustainable and Responsible Investment (SRI) stock portfolio performance in the Indonesia Stock Exchange (IDX). SRI is a concept where the investor is not only considering financial performance, but also non-financial aspects in the company such as Environmental, Social, and Governance (ESG). The main objective of this study is to compare the performance of the SRI portfolio with the performance of comparable portfolios. Is the performance of the SRI portfolio in IDX better than conventional portfolio and Islamic portfolio? To answer this question, the SRI portfolio will be formed from SRI-KEHATI Index (SRI), a conventional portfolio will be formed from the LQ45 index, and the Islamic portfolio will be formed from Jakarta Islamic Index (JII). The performances of those three portfolios will be compared based on alpha value, Sharpe Ratio, Treynor Ratio, and Sortino Ratio. This study result shows that the SRI portfolio performance in IDX is better than the conventional portfolio and Islamic portfolio. That performance occurs because the average of SRI portfolio excess return has positive value during the study period.

The Kingdom of Saudi Arabia is strongly committed to stimulating savings culture in the local community by providing financial literacy in financial planning, investments, and budgeting. Inculcating the savings and investment behavior among the people will help materialize one of the elements of Saudi Vision 2030. Tadawul, being the most liquid stock market in the Middle East and North Africa, offers investors the ability to grow their capital with confidence through facilitating trading in different securities such as equities, debt instruments, and Exchange Traded Funds (ETFs). There is a great scope for investors to invest in the companies listed in Tadawul All Share Index (TASI) due to its strong economic fundamentals. The present study aims to apply the CAPM in Tadawul listed stocks, which will help in understanding the systematic and unsystematic risk associated with stocks, understanding their actual and theoretical return on stocks. The methodology adopted is the analysis of secondary data for all listed stocks in Tadawul using the Bloomberg terminal. The financial valuation includes elements like beta, alpha, correlation and standard deviation, expected return and actual return. The practical value obtained from the study will help investors go for undervalued stocks with lower beta, higher expected annual return, and lower systematic risks. Thus, the result shows the predicting power in KSA market and the scope for long-term investments by the investors to boost their savings and investment behavior and materialize one element of Vision 2030. AcknowledgmentThis research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-Track Research Funding Program.

Introduction: Social capital, along with human and economic capital, has drawn considerable attention. It enhances the market participation and mitigates financing costs via promoting mutual trust Method: This research is applied in terms of purpose and is descriptive-correlation regarding the data collection. The aim of this study is to evaluate the relation between social capital and capital cost using the structural equations method. In this study, three methods of O’hanlon and Steele, Capital Asset Pricing Model and adjusted price-to-earnings ratio in an industry were employed to measure capital cost. Nahapiet and Ghoshal’s questionnaire was also adopted to compute social capital. Results: Having ensured the good-fitness of the appraisal and structural models, the research hypotheses were tested and the results reveal that social capital mitigates the capital cost. Conclusion: Policy makers should focus on social capital as the main component of management in organizations, and it is necessary to consider a variety of programs, such as encouraging the creation and strengthening of social institutions.

This paper investigates the validity of the Fama-French Three Factor (FF3F) and the Carhart Four Factor (C4F) models in Morocco. Monthly returns of Casablanca Stock Exchange-listed companies are extracted from Reuters DATASTREAM over a 5 years’ period (2013-2017). Market, size, value and momentum effect-mimicking exogenous variables are formed and regressed against the returns of size and value-sorted portfolios over a total of 8 multivariate linear regressions. While the size and value effects were found to partially hold, the momentum effect was found to be insignificant. Additionally, the C4F Model did not exhibit a better explanatory power compared to the FF3F Model. It appears that both models cannot be fully relied on in order to predict cross-sections of return in the Casablanca Stock Exchange (CSE), as they both only partially hold in the latter. Ultimately, this study brings value to the existing literature by testing two widely explored asset pricing models in an emerging market where equity research-oriented inquiries are relatively scarce or basic. Even if the models do not fully hold in the Moroccan context, this study posits whether the individual anomalistic factors hold in the CSE, which might be useful for future asset pricing models augmenting endeavors.

In this study, the reliability of the Fama–French Three-Factor model (FF3F) and the Carhart Four-Factor model (C4F) is examined thoroughly. In order to determine which of the asset pricing models is the best to explain portfolio returns on the Moroccan share market, these two models are indeed evaluated in the Moroccan market. Additionally, it is worth mentioning that five years of monthly data from the firms that listed on the Casablanca Stock Exchange are used in this research, as well over the period of nine years. The results of this inquiry show that these models barely have a partial hold on the Casablanca Stock Exchange (CSE), which limits their ability to predict the cross-sections of returns. In accordance with this, the C4F model has somewhat greater explanatory power than the FF3F Model. Moreover, our research adds to the body of knowledge by inserting two learned material asset pricing theories to the proof in the market, which is still evolving, and where distinctive anomalistic traits still exist (the CSE).

Investment is a tool for investors to get more profit than what has been invested. Investors must be able to predict the possibilities that occur when investing. Capital Asset Pricing Model is a tool to predict the development of investment in a particular company used to calculate and determine the Expected Return in minimizing risk investments. The authors conducted research using a sample of 5 companies in the automotive industry, namely PT Astra International Tbk, PT Indokordsa Tbk, PT Indomobil Sukses Internasional Tbk, PT Astra Otoparts Tbk, and PT Gajah Tunggal Tbk. This study uses a descriptive quantitative approach with Microsoft Excel 2016 analysis tools. This study aims to determine Portfolio Analysis with the Capital Asset Pricing Model (CAPM) approach which is used as the basis for making stock investment decisions in automotive industry sector companies listed on the Indonesia Stock Exchange. Use from the results of the analysis of the results by comparing the value of E(Ri) has a directly proportional relationship, meaning that the higher the value of, then the stock return (E(Ri)) will be high as well. Of the 5 companies, there are 2 companies that are in the Undervalued category and 3 companies that are in the overvalued category. This means that investors who will invest in companies engaged in the automotive industry can decide to buy shares of the companies PT Indomobil Sukses Internasional Tbk and PT Gajah Tunggal Tbk, because they are classified as undervalued. Meanwhile, investors who want to invest in shares are not advised to buy company shares that are in the overvalued category, but are advised to sell them to investors who already have shares in the company.

Purpose – Most of the major Islamic countries’ stock exchanges have not been able to perform at the same pace with the major emerging countries’ stock exchanges since the mid of 1990s. The purpose of this paper is to examine the implications of stock market liberalization on cost of capital as one of the crucial driver to stock market development and physical investment growth in emerging Islamic countries. Design/methodology/approach – This study employs static panel data techniques on the sample of seven emerging Islamic countries over the years 1989-2008. Findings – The findings of this study suggest that stock market liberalization significantly reduces cost of capital in the stock markets of sample Islamic countries, which carries policy-oriented implications. Reduction in the cost of capital increases the number of exchange-traded companies, profitability of projects and aggregate investment level; therefore, the study findings are highly concerned by the economic policymakers, corporations and investors alike. Research limitations/implications – In the literature, different proxies are employed to measure stock market liberalization and cost of capital as well. Due to data limitations, this study could not employ different proxies for both, especially for stock market liberalization, for robustness purpose. That limitation further restricted the coverage of Islamic stock markets and time period. Therefore, generalization of the study results for overall Islamic stock markets can be slightly drawn. Originality/value – The paper provides further understanding regarding the effects of SML on cost of capital, thereby indirectly on the stock market development, in the context of EIC.

Investors having an understanding of investment statistics are important. Especially quantitative tools related to investment risk measurement. Value-at-Risk Adjusted is one of the investment risk measurement tools, which assumes that returns are not normally distributed.This paper intends to measure investment risk based onValue-at-Risk Adjustedor called Modified Value-at-Risk under the Capital Asset Pricing Model. It is assumed that the return of the market index has a non-constant average and there is a long memory effect. The average of the return of the market index is estimated using ARFIMA models.It is also assumed that the stock risk premium correlates with market risk premiums, and stock risk premiums some time before. The correlation will be analyzed using the ARMAX-GARCH model approach. The Modified Value-at-Risk was then formulated based on the Capital asset Pricing Model with the ARMAX-GARCH model approach.To measure the performance of Modified Value-at-Risk that has been formulated is done with back testing. Back testing is carried out based on the Lopez II method. As a case study, analyzed some data on 10 stocks traded on the capital market in Indonesia.The results of the analysis show that the market index return risk premium significantly follows the ARFIMA model, and the 10 share risk premium significantly follows the ARMAX-GARCH model. Based on the results of back testing calculations indicate that the Value-at-Risk Adjustedor Modified Value-at-Risk is very suitable to be used to measure investment risk in the 10 stocks analyzed.

Behavioral components of Kahneman and Tversky’s (1979) prospect theory (PT) were applied to derive an adjusted Capital Asset Pricing Model (CAPM) in the estimation of merger and acquisition-intensive firms’ expected returns. The premise was that the CAPM – rooted in expected utility theory – is violated by the behavioral biases identified in prospect theory. Kahneman and Tversky’s prospect theory (1979) has demonstrated that weaknesses abound in the viability of classical utility theory predictions. For mergers and acquisitions, firms appear to be isolated from and immune to human error, yet decisions which involve the undertaking of capital-intensive projects are delegated to senior management. These individuals are prone to cognitive biases and personalized risk appetites that may (and often do) compromize attitudes and behavior when it comes to pricing risky ventures. Having established that beta estimates using linear regression are inferior, the CAPM was implemented utilizing beta estimates obtained from the Kalman filter. The results obtained were assessed for their long-term market price predictive accuracy. The authors test the reliability of the CAPM as a predictor of price, observe the rationality of human behavior in capital markets, and attempt to model premiums to adjust CAPM returns to a level that more appropriately accounts for firm specific risk. The researchers show that market participants behave irrationally when assessing M&A firms’ specific risk. Logistic regression coupled with the development of a risk premium was implemented to correct the original Kalman filter returns and was tested for improvements in predictive power.

This study aims to analyze stock performance credibility using the Capital Asset Pricing Model (CAPM) method, the Arbitrage Pricing Theory (APT) method, the Fama-French Three-Factor Model (FFTFM), and the 2013-2017 LQ-45 Stock Performance rating. The technique used for sampling is purposive sampling. The samples in this study are 23 companies. The results show that the CAPM model is more accurate in stock credibility assessment than the APT model and the FFTFM.

A análise de investimentos busca a avaliação e a seleção de oportunidades que darão à empresa um retorno econômico coerente com suas metas. As empresas, de forma a se manterem competitivas no mercado, precisam selecionar projetos relevantes que apresentarão retornos satisfatórios, condizentes com seus investimentos de capital. Para tanto, o aperfeiçoamento das ferramentas gerenciais da empresa torna-se apropriado, em prol de melhorar a escolha de novos empreendimentos. Dessa maneira, o objetivo desse trabalho foi verificar se a utilização da metodologia Valor Econômico Adicionado (EVA) na análise e precificação de projetos agrega informações e conhecimentos que não seriam alcançados por uma empresa de pequeno porte, com as metodologias utilizadas por ela atualmente. Com esse intuito, foi realizado um estudo de caso descritivo em uma empresa que realiza projetos públicos na área de construção civil, chamada Fator Construções. Ao analisar um projeto que foi executado pela empresa em questão, denominado Projeto A, constatou-se que o custo do capital próprio foi relevante, quando calculado pelo atual método de análise utilizado pela empresa. Porém, quando se calcula o valor presente dos EVAs mensais, é observado que esse projeto não agrega valor à empresa. Com a utilização do EVA, o resultado muda inteiramente, demonstrando a importância de se considerar o real custo de capital na precificação e análise de projetos. A análise do presente estudo evidenciou a sensibilidade do resultado do projeto em relação ao custo do capital próprio.

Purpose: This study aimed to analyze the inclusion of corporate governance in the explanation of Fama and French’s (1993, 2015) three and five-factor asset pricing models. Originality/value: This research differs from other works by inserting corporate governance as an explanatory factor in the pricing model of financial assets. Thus, it is intended to contribute to the research area by trying to identify previously unexplored characteristics, as the proposed method, that helps and adds explanation to the pricing models of financial assets, thus helping investors and professionals in the financial area. Design/methodology/approach: The research sample consists of 387 companies listed in B3, in the period between 2012 and 2016. For data analysis, panel data regressions were used according to the methodology of Fama and French studies (1993, 2015) through the Stata software. Findings: The results indicate that corporate governance has a negative impact on the return of the actions of small companies with lower levels of corporate governance and that the reverse is true for large companies with high levels of governance. Other variables were also found to be impacting on stock returns, such as market value, book-to-market, profitability, and investments. / Objetivo: Este estudo teve como objetivo analisar a inclusão da governança corporativa na explicação dos modelos de precificação de ativos de três e cinco fatores de Fama e French (1993, 2015). Originalidade/valor: A presente investigação se diferencia dos demais trabalhos por inserir a governança corporativa como fator explicativo nos modelos de precificação de ativos financeiros. Pretende-se, assim, contribuir para a área de pesquisa ao tentar identificar características antes não exploradas, quanto ao método proposto, que agreguem explicação aos modelos de precificação de ativos financeiros, de modo a ajudar os investidores e profissionais da área financeira. Design/metodologia/abordagem: A amostra de pesquisa é composta por 387 empresas listadas na B3, no período entre 2012 e 2016. Para análise dos dados, utilizaram-se regressões de dados em painel, de acordo com metodologia dos estudos de Fama e French (1993, 2015), por meio do software Stata. Resultados: Os resultados indicam que a governança corporativa impacta negativamente o retorno das ações das pequenas empresas com menores níveis de governança corporativa e que o inverso acontece em relação às grandes empresas, com altos níveis de governança. Outras variáveis também foram encontradas como impactantes no retorno das ações, como valor de mercado, book-to-market, rentabilidade e investimentos.

Using the example of a diversified industrial enterprise, there developed an approach to evaluating the prospects of the implementation of an innovation and investment project on manufacturing a product under conditions of an undeveloped sales market as well as associated risks. It is substantiated that the project can be promising due to its social significance manifested in obtaining environmental, social, and environmental effect from it realization. To evaluate alternative investment projects, there developed a methodological approach that implies analysis according to the following scheme: volume of the market for the product, designed capacity and production technology, competitive environment; characteristics of consumers. This will allow to demonstrate the advantages and disadvantages of each of the alternative projects at the initial stage of the study. The effectiveness and expediency of the project implementation should be evaluated by the following indicators: payback period (PP), accounting rate of return (ARR), net present value (NPV), profitability index (PI), discounted payback period (DPP), internal rate of return (IRR). When making the decision about the implementation of a particular project, its social effect as well as the forecast of future cash flows reflecting changes in their value over time with regard to the discount rate should be taken into account. In developing markets, it is most appropriate to take for the discount rate the weighted average cost of capital, which is calculated in accordance with the CAPM model using the central bank discount rate as risk-free rate, and to calculated the risk premium using the methodology developed by A. Damodaran, based on the data on the country’s credit rating by Moody’s. The identification of the main risks of the project is possible by conducting an expert survey providing for the evaluation of importance and probability of occurrence of individual risk. The subjectivity of an expert survey has to be reduced by identifying the main mental traps. A more in-depth study of an impact of each risk has to be conducted by using the VaR concept and analysing the sensitivity of impact of simultaneous two risks to the net present value of the project.

Typically, the core-required undergraduate business statistics course covers a broad spectrum of topics with applications pertaining to all functional areas of business. The recently updated American Statistical Association's GAISE (Guidelines for Assessment and Instruction in Statistics Education) College Report once again stresses the pedagogical importance of topic and application relevancy in an increasingly data-centered world. To this end, only two introductory textbooks have incorporated some financial investment measures (Sharpe ratio and beta coefficient) in the teaching of numerical descriptive measures and simple linear regression analysis, respectively, while a few others include them as real-data application exercises at the end of their respective chapters. Although this latter coverage is in compliance with GAISE College Report recommendations on the importance of using relevant real data applications in the teaching of introductory business statistics, it forgoes an opportunity to provide more detailed discussion within the text itself and add value to a business student's learning. Given that all business students will have an opportunity to learn about financial investment, regardless of their functional area major, this paper offers a more proactive use of these and other financial investment measures as part of the current, traditional course or as part of a suggested dedicated introductory business statistics course for finance majors.

The capital asset pricing model has been one of the most prevalent models in assessing investors’ expected rate of return. Provided that it is likely that the residuals of the estimated regression of this model resemble conditional heteroscedasticity, this paper aims to test the predictive power of standard CAPM and CAPM based on symmetric and asymmetric conditional heteroscedasticity. For this purpose, the expected returns during the time period of the research have been estimated based on three existing models. The findings were compared with obtained returns and mean squared error index was utilized for measurement of the predictive power of those models. The models were compared using Diebold-Mariano test on mean squared error index. The findings indicated that, with respect to the CAPM model, the consideration of the conditional heteroscedasticity (symmetric and asymmetric) can stimulate predictive power of the obtained return.

Students considering a masters in Finance Engineering or Artificial Intelligence in Finance are usually required to have an undergraduate background in science, technology, engineering, or mathematics (STEM). STEM students have a good capacity in mathematics and science, but they may not have studied financial theory. To facilitate the classroom teaching of the Capital Asset Pricing Model (CAPM) for STEM students, this paper seeks to expound on the essence of the theory starting at a two-asset framework. Adopting the concepts proposed by Merton (1972), this paper accomplishes the derivation by virtue of basic mathematical tools such as linear algebra, geometry, and statistics except for calculus. We show that the major aspects of Merton’s derivation of the CAPM for a universe of N assets may also be obtained in a two-asset world. Through the methods of this article, students will learn the in-depth theory of CAPM and its hands-on empirical tool. For example, students will realize that even if investors specify different threshold rewards, their different CAPMs will yield identical pricing for assets and portfolios.

The current study deals with the validity of Capital Asset Pricing Model in selected industries of India. The CAPM is being tested using Regression analysis with the help of Eviews 9.0 in seven industries for a period of 16 years ranging from January 2000 to April 2016 using monthly stock indices from BSE. The results prove that the model is applicable in many of the industries of Indian Stock Market for the period studied.

La investigación permitió demostrar que las betas calculadas ofrecen diferentes valores dependiendo de la serie histórica de datos seleccionado por el analista, se observan resultados antagónicos con betas del activo más riesgosas que el mercado, pero al ampliar la selección de los datos histórica se convierten en betas menos riesgosas que el mercado en más del 50% de los casos observados. Los resultados confirman lo señalado por algunos investigadores en el pasado, la beta calculada con datos históricos no es una buena aproximación a la beta de la empresa. Se comprobó el impacto de la beta en el Costo del Capital a través del modelo CAPM en Estados Unidos debido a que existen múltiples betas dependiendo de la serie histórica de datos a tomar. Para tal fin, se ha realizado el cálculo de las betas a través de una regresión lineal utilizando algunas empresas cotizadas en los mercados asiáticos a través de la elección del índice Shanghai Composite, el europeo a través de la elección del índice IBEX 35 y el Norteamericano a través del índice Dow Jones, considerando para todos ellos una frecuencia diaria de los rendimientos y seleccionado una serie histórica de 3 años, 6 meses y 3 meses.

The majority of Italian schools (70%) were built in the absence of any legislation related to energy efficiency, and therefore have very low energy performance due to aging or poor quality of construction. An energy retrofit of this building stock is needed to meet the current European goals on greenhouse gas emission reduction. The retrofit is also needed in order to guarantee adequate comfort levels in indoor spaces and good conditions for learning and educational activities, that are often not reached in poor quality constructions. This work presents the results of an interdisciplinary study related to the energy requalification of a school located in Ostia, near Rome in Italy, built in the 1960s with a steel structure and Eternit infill. The scope of the analysis is to verify the economic and environmental effectiveness of four proposed retrofit interventions concerning the replacement of fixtures and the installation of an insulating coat. The current thermal transmittance of the walls was evaluated through thermofluximetric measurements conducted in situ; dynamic simulations were performed to determine the current energy performance and the energy performances following the four proposed retrofit scenarios. Energy and carbon payback times were evaluated (by means of the life cycle analysis (LCA) approach) and the economic value was determined for each of the four proposed retrofits, using a probabilistic approach. The results show that the replacement of windows is the most convenient intervention from all points of view. The study provides evidence that an assessment of schools’ energy retrofits should include both economic and life cycle aspects.

Beta and the capital asset pricing model have traditionally been the preferred measures of risk. However, there is growing literature against the use of the capital asset pricing model to determine the cost of equity in markets, such as emerging markets, where investors display mean-semivariance behaviour and, where share returns are non-normal and asymmetric. Downside risk measures such as semideviation, downside beta and the downside capital asset pricing model have been found to be plausible alternate measures of risk. This study investigates empirically the relationship between risk and return in a downside risk framework and a regular risk framework using returns on companies listed on the JSE Securities Exchange. The empirical evidence from this study indicates that while downside beta and semideviation significantly explain the variation in returns, they do not support them as being more appropriate measures of risk over beta and standard deviation.

Capital Asset Pricing Model is widely used by investors to estimate the return or the moving behavior of the stock and Markowitz Model is employed to achieve portfolio diversification. This study examine whether CAPM is valid to forecast the behaviour of the each individual stock and its return as well as its validity in the portfolio with stocks listed in Malaysia. Second, it evaluates the suitability of Markowitz Model to evaluate the performance of the Malaysia investment portfolio. This is done within the framework of 2010 to 2014 using weekly data of 60 companies. OLS unbiased estimator, autocorrelation and heterodasticity problems are to be conducted to test the validity of the model. It is concluded that CAPM is reasonable to be the indicator of stock prices in Malaysia as well as in portfolio basket. It proves that there is linearity in CAPM but unique risk and systematic do not need to be captured. Managers can use CAPM as a proxy to estimate their stock return and diversify the portfolio to reduce the unsystematic risk to enable them to execute the right policy in their management in order to maximise profit at the same time increase shareholder wealth maximisation. Furthermore, it is suggested to apply Markowitz portfolio diversification to reduce the unsystematic risk. Overall, portfolio diversification could build up the investors’ confidence towards the investment decision and to develop a sound investment financial market in assisting Malaysia to achieve its mission to be a developed country in 2020.

We test the empirical validity of the Capital Asset Pricing Model (CAPM) on the Zimbabwe Stock Exchange (ZSE) using cross-sectional stock returns on 31 stocks listed on the ZSE between March 2009 and February 2014. We conclude that, although the explanatory power of beta tends to fall rapidly for prediction horizons greater than 6 months, beta significantly explains average monthly stock returns on the ZSE. Tests to validate the CAPM reject its validity for the ZSE however, primarily due to liquidity and skewness anomalies. We nevertheless fail to detect any size effects. There is encouraging evidence to suggest that the CAPM performs reasonably well in predicting average monthly returns over prediction horizons of between 3 and 6 months. We recommend that investors and analysts must exercise extreme caution in applying the CAPM. Furthermore, we discourage strategies based on the existence of a size premium on the ZSE. Instead, investors may consider neglected and negatively skewed stocks, albeit over appropriate horizons. Further research on other African stock markets will help verify if the optimal performance range of the CAPM is indeed 3 to 6 months. Development of standard continental proxy market portfolios will also improve the estimation of betas and enhance results of cross-country tests of the CAPM.

Penelitian ini menerapkan secara langsung Capital Asset Pricing Model (CAPM) dan stochastic dominance untuk memecahkan masalah pemilihan portofolio. Adapun tujuan dari penelitian ini adalah untuk mengetahui perbedaan tingkat return dan risiko portofolio antara pemilihan saham menggunakan Capital Asset Pricing Model (CAPM) dan stochastic dominance. Penelitian ini menggunakan data sekunder dan pemilihan sampel secara purposive sampling. Data sampel terdiri dari 20 saham terpilih (dari Indeks Sri-Kehati) yang diperoleh selama 31 bulan, yaitu April 2013 hingga Oktober 2015. Independent sample t-test digunakan untuk menganalisis perbedaaan return dan risiko portofolio antara pemilihan saham menggunakan Capital Asset Pricing Model (CAPM) dengan pemilihan saham menggunakan stochastic dominance. Hasil penelitian mengindikasikan bahwa secara signifi kan terdapat perbedaaan return dan risiko portofolio antara pemilihan saham menggunakan Capital Asset Pricing Model (CAPM) dengan pemilihan saham menggunakan stochastic dominance.

Risk and return in is counted as one of the main tools in Finance literature which at the end will be presented as cost of capital. Cost of capital has a direct and important role in investment and finance decisions and always has been the main challenge for investors and finance decision makers. In this research we have tried to measure the cost of capital based on fundamental model and to compare it with employed cost of capital and Fama&French models. The result of investigation which was taken in 2011 among more than 88 TSE firms indicated that accounting based cost of capital in relation to financial variables has more correlation in compare with Fama&French and CAPM.As to this matter cost of capital can be a better help to users in evaluating and decision makings.

The objective of this study is to investigate the relationship between economic freedom index and stock return in the Kuala Lumpur Stock Exchange, Malaysia for the period, 1995 to 2013. The analysis is conducted within the framework of Capital Asset Pricing Model (CAPM), while using the pooled ordinary least square as the method of estimation. The findings show that economic freedom index does not have significant impact on stock returns in the long run. However, overall economic freedom index has significant impact on stock returns in the short run. We further consider the impact of five components of economic freedom index. It is observed that the components do not have significant long run impact on stock returns. The components-limited government and open markets- have strong short run significant explaining powers. The results are consistent across different levels of inflation and wealth in Malaysia. The results indicate that investors can obtain better mean-variance efficiency when a country exhibit greater economic freedom. This paper should be of interest to both investors and market researchers.

The financial market is characterized by circulating an impressive volume of data to beincorporated into financial decision in a very short time. Gathering and processing this data wouldbe impossible without the level of performance the computer systems have nowadays. Excelmanagement program used for this application, has the necessary functions for using anyeconometric model, data being processed in a current stream due to the possibility of thecorrelating the database with the subsequent stages up to the final stages. For the application that will be described below, namely the management of a portfolio of eightsecurity bonds, a financial analyst should carry more than 10^15 computing operations. Thedemonstration focuses on the analysis of results and on summarizing the concepts used.

This paper presents an improved method of applying entropy as a risk in portfolio optimization. A new family of portfolio optimization problems called the return-entropy portfolio optimization (REPO) is introduced that simplifies the computation of portfolio entropy using a combinatorial approach. REPO addresses five main practical concerns with the mean-variance portfolio optimization (MVPO). Pioneered by Harry Markowitz, MVPO revolutionized the financial industry as the first formal mathematical approach to risk-averse investing. REPO uses a mean-entropy objective function instead of the mean-variance objective function used in MVPO. REPO also simplifies the portfolio entropy calculation by utilizing combinatorial generating functions in the optimization objective function. REPO and MVPO were compared by emulating competing portfolios over historical data and REPO significantly outperformed MVPO in a strong majority of cases.

This paper models hedge fund exposure to risk factors and examines time-varying performance of hedge funds. From existing models such as asset-based style (ABS)-factor model, standard asset class (SAC)-factor model, and four-factor model, we extract the best six factors for each hedge fund portfolio by investment strategy. Then, we find combinations of risk factors that explain most of the variance in performance of each hedge fund portfolio based on investment strategy. The results show instability of coefficients in the performance attribution regression. Incorporating a time-varying factor exposure feature would be the best way to measure hedge fund performance. Furthermore, the optimal models with fewer factors exhibit greater explanatory power than existing models. Using rolling regressions, our customized investment strategy model shows how hedge funds are sensitive to risk factors according to market conditions.

The present study tries to test the validity of the capital asset pricing model (CAPM) in the Bucharest Stock Exchange Market from Romania. The tested period for this study covers 3 years from 2011 to 2013. The sample is composed by 20 companies listed at the Bucharest Stock Exchange. The relationship between risk and expected return is being examined. It was found that the security market line’s slope is downwards sloping and negative and so it was concluded that CAPM does not hold. As a general conclusion there is a weak practical implication of the Capital Asset Pricing Model in Romanian stock market. Thus, other important variables should be considered to find an effective and appropriate pricing mechanism.

This paper is focused on building investment portfolios by using the Markowitz Portfolio Theory (MPT). Derivation based on the Capital Asset Pricing Model (CAPM) is used to calculate the weights of individual securities in portfolios. The calculated portfolios include a portfolio copying the benchmark made using the CAPM model, portfolio with low and high beta coefficients, and a random portfolio. Only stocks were selected for the examined sample from all the asset classes. Stocks in each portfolio are put together according to predefined criteria. All stocks were selected from Dow Jones Industrial Average (DJIA) index which serves as a benchmark, too. Portfolios were compared based on their risk and return profiles. The results of this work will provide general recommendations on the optimal approach to choose securities for an investor’s portfolio.

This article is aimed at proposing of an inovative method for calculating the shares of operational and financial risks. This methodological tool will support managers while monitoring the risk structure. The method is based on the capital asset pricing model (CAPM) for calculation of equity cost, namely on determination of the beta coefficient, which is the only variable, that is dependent on entrepreneurial risk. There are combined both alternative approaches for calculation betas, which means, that there are accounting data used and there is distinguished unlevered beta and levered beta. The novelty of the proposed method is based on including of quantities for measuring operational and financial risks in beta calculation. The volatility of cash flow, as a quantity for measuring of operational risk, is included in the unlevered beta. Return on equity based on the cash flow and the indebtedness are variables used in calculation of the levered beta. This modification makes it possible to calculate the share of operational risk as the proportion of the unlevered/levered beta and the share of financial risk, which is the remainder of levered beta. The modified method is applied on companies from two sectors of the Czech economy. In the data set there are companies from one cyclical sector and from one neutral sector to find out potential differences in the risk structure. The findings show, that in both sectors the share of operational risk is over 50%, however, in the neutral sector is this more dominant.

A number of researchers have sought to test the theoretical prediction of Modern Portfolio Theory that asserts that Socially Responsible Investing (SRI) under-performs conventional investing. In contrast to the majority of literature, which focuses on comparing SRI funds’ performance to conventional funds, this study compares the performance of South Africa’s JSE SRI Index to the performance of local conventional market indices in the period 2004-2012. Using Sharpe ratios, the results of the study indicate that in comparison to conventional indices, the JSE SRI Index generally exhibits an inferior risk-return trade-off in both bull and bear market conditions. Furthermore, spanning tests based on the single-factor Capital Asset Pricing Model provide evidence that the JSE SRI Index is only likely to earn similar risk-adjusted returns to the Synthetic Conventional Index (a self-constructed index tracking non-overlapping conventional stocks). However, if the assumption of a non-restricted investment universe for a non-socially conscious investor is considered, there is a risk-adjusted return penalty for investing in the JSE SRI Index.

One of the most important issues in the capital market is assessing the risk level of companies, especially “systemic risk” that can affect stock returns, and therefore plays a significant role in decision-making. The present study examined the relationship between stock returns and systematic risk based on the Capital Asset Pricing Model (CAPM) in the Tehran Stock Exchange. The data set contains 5 data points per year for the top 50 companies in Tehran Stock Exchange for the five year period of 1387 to 1392 adding up to a total of 1250 data points. Model parameter estimation is done using Panel data techniques and the results of the hypothesis tests show that the statistically significant correlation between systematic risk and stock returns. The results indicate that the a quadratic relationship explains this correlation better than linear relationship is able to, thus rejecting the assumption of linearity on the correlation between systematic risk and stock returns in the Tehran Stock Exchange

This study determines the optimal investment portfolio in Tehran Stock Exchange (TSE) industries. For this purpose, a conditional capital asset pricing model (CAPM) with time-varying covariance, according to a Multivariate GARCH approach has been formulated. According to this conditional CAPM, the conditional variance-covariance matrix and mean of returns are calculated for some industries. By using the Mean-Value at Risk portfolio selection model, the optimum proportion is detected. Results showed that the Pharmaceutical Industry, Financial Group and Cement Industry have the most quotas in portfolio since they maintain the minimum variance and maximum return among all other industries.

This paper contains a comparison of in-sample and out-of-sample performances between the resampled efficiency technique, patented by Richard Michaud and Robert Michaud (1999), and traditional Mean-Variance portfolio selection, presented by Harry Markowitz (1952). Based on the Monte Carlo simulation, data (samples) generation process determines the algorithms by using both, parametric and nonparametric bootstrap techniques. Resampled efficiency provides the solution to use uncertain information without the need for constrains in portfolio optimization. Parametric bootstrap process starts with a parametric model specification, where we apply Capital Asset Pricing Model. After the estimation of specified model, the series of residuals are used for resampling process. On the other hand, nonparametric bootstrap divides series of price returns into the new series of blocks containing previous determined number of consecutive price returns. This procedure enables smooth resampling process and preserves the original structure of data series.