This document summarizes a study that analyzed the relevance of using the Capital Asset Pricing Model (CAPM) to measure stock returns and risk. The study compared linear CAPM models to non-linear models using weekly stock return data from Indonesian companies from September to November 2014. The results showed that (1) different CAPM models produced similar beta values but different alpha values; (2) non-linear models had a better fit than linear models; and (3) both linear and non-linear CAPM models were still relevant for measuring stock beta and returns, though the CAPM concept continues to be developed and improved over time as financial markets become more complex.
The article re-institutes the investor faith in CAPM model and talks about how CAPM is very closely coupled to the actual investment practices at the ground level. The general criticism of CAPM model that it does not fit empirical asset pricing well cast doubt on the validity of model have been explained.
International Journal of Business and Management Invention (IJBMI)inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
This presentation demonstrates that how economic concepts and/or econometric techniques can be useful in financial decision making (i.e. trading) and that how EViews can effectively handle the whole process.
AN ANALYSIS OF THE FINANCIAL PERFORMANCE EFFECT OF SHARIA COMPANIES ON STOCK ...Saputra Ayudi
This study entitled " An Analysis of the Financial Performance Effect of Sharia Companies on Stock Price Changes ( a Case Study of Companies Listed on the Sharia Stock Listing) ". This study uses eight (8) the financial ratios are debt to equity ratio, current ratio, inventory turnover ratio, total asset turnover, net profit margin, return on equity, price earning ratio, and dividend yield. The above financial ratios have been considered representative of the five types of financial ratios, which are 5 types of financial ratios in question are leverage ratios, liquidity ratios, efficiency ratios, profitability ratios, and market value ratios.
The article re-institutes the investor faith in CAPM model and talks about how CAPM is very closely coupled to the actual investment practices at the ground level. The general criticism of CAPM model that it does not fit empirical asset pricing well cast doubt on the validity of model have been explained.
International Journal of Business and Management Invention (IJBMI)inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
This presentation demonstrates that how economic concepts and/or econometric techniques can be useful in financial decision making (i.e. trading) and that how EViews can effectively handle the whole process.
AN ANALYSIS OF THE FINANCIAL PERFORMANCE EFFECT OF SHARIA COMPANIES ON STOCK ...Saputra Ayudi
This study entitled " An Analysis of the Financial Performance Effect of Sharia Companies on Stock Price Changes ( a Case Study of Companies Listed on the Sharia Stock Listing) ". This study uses eight (8) the financial ratios are debt to equity ratio, current ratio, inventory turnover ratio, total asset turnover, net profit margin, return on equity, price earning ratio, and dividend yield. The above financial ratios have been considered representative of the five types of financial ratios, which are 5 types of financial ratios in question are leverage ratios, liquidity ratios, efficiency ratios, profitability ratios, and market value ratios.
MODELING THE AUTOREGRESSIVE CAPITAL ASSET PRICING MODEL FOR TOP 10 SELECTED...IAEME Publication
Systematic risk is the uncertainty inherent to the entire market or entire market segment and Unsystematic risk is the type of uncertainty that comes with the company or industry we invest. It can be reduced through diversification. The study generalized for selecting of non -linear capital asset pricing model for top securities in BSE and made an attempt to identify the marketable and non-marketable risk of investors of top companies. The analysis was conducted at different stages. They are Vector auto regression of systematic and unsystematic risk.
Hybrid Methods of Some Evolutionary Computations AndKalman Filter on Option P...IJMERJOURNAL
ABSTRACT: The search for a better option price continues within the financial institution. In pricing a put option, holders of the underlying stock always want to make the best decision by maximizing profit. We present an optimal hybrid model among the following combinations: Kalman Filter-Genetic Programming(KF-GP), Kalman Filter-Evolutionary Strategy(KF-ES) and Evolutionary Strategy -Genetic Programming(ES- GP). Our results indicate that the hybrid method involving Kalman Filter-Evolutionary Strategy(KF-ES) is the best model for any investor. Sensitivity analysis was conducted on the model parameters to ascertain the rigidity of the model.
Security Analysts’ Views of the Financial Ratios of Manufacturers and Retailers Raju Basnet Chhetri
Keishiro Matsumoto (Associate Professor of Finance, University of Virgin Islands),
Melkote Shivaswamy (Associate Professor of Accounting, Ball State University)
James P. Hoban, Jr. (Professor of Finance, Ball State University)
This paper conducts some experiments with forex trading data. The data being used is from kaggle.com, a website that provides datasets for machine learning and data scientists. The goal of the experiments is to know how to design many parameters in a forex trading robot. Some questions that want to be investigated are: How far the robot must set the stop loss or target profit level from the open position? When is the best time to apply for a forex robot that works only in a trending market? Which one is better: a forex trading robot that waits for a trending market or a robot that works during a sideways market? To answer these questions, some data visualizations are plotted in many types of graphs. The data representations are built using Weka, an open-source machine learning software. The data visualization helps the trader to design the strategy to trade the forex market.
Assignment template pg research metodology (502)Abhilash Kharvi
In this Assignment paper to help for the students to learn research methodology to find out what are the methods and Technics are used in Journals
In this Paper to take particular topic to find out the Particular Problems
MODELING THE AUTOREGRESSIVE CAPITAL ASSET PRICING MODEL FOR TOP 10 SELECTED...IAEME Publication
Systematic risk is the uncertainty inherent to the entire market or entire market segment and Unsystematic risk is the type of uncertainty that comes with the company or industry we invest. It can be reduced through diversification. The study generalized for selecting of non -linear capital asset pricing model for top securities in BSE and made an attempt to identify the marketable and non-marketable risk of investors of top companies. The analysis was conducted at different stages. They are Vector auto regression of systematic and unsystematic risk.
Hybrid Methods of Some Evolutionary Computations AndKalman Filter on Option P...IJMERJOURNAL
ABSTRACT: The search for a better option price continues within the financial institution. In pricing a put option, holders of the underlying stock always want to make the best decision by maximizing profit. We present an optimal hybrid model among the following combinations: Kalman Filter-Genetic Programming(KF-GP), Kalman Filter-Evolutionary Strategy(KF-ES) and Evolutionary Strategy -Genetic Programming(ES- GP). Our results indicate that the hybrid method involving Kalman Filter-Evolutionary Strategy(KF-ES) is the best model for any investor. Sensitivity analysis was conducted on the model parameters to ascertain the rigidity of the model.
Security Analysts’ Views of the Financial Ratios of Manufacturers and Retailers Raju Basnet Chhetri
Keishiro Matsumoto (Associate Professor of Finance, University of Virgin Islands),
Melkote Shivaswamy (Associate Professor of Accounting, Ball State University)
James P. Hoban, Jr. (Professor of Finance, Ball State University)
This paper conducts some experiments with forex trading data. The data being used is from kaggle.com, a website that provides datasets for machine learning and data scientists. The goal of the experiments is to know how to design many parameters in a forex trading robot. Some questions that want to be investigated are: How far the robot must set the stop loss or target profit level from the open position? When is the best time to apply for a forex robot that works only in a trending market? Which one is better: a forex trading robot that waits for a trending market or a robot that works during a sideways market? To answer these questions, some data visualizations are plotted in many types of graphs. The data representations are built using Weka, an open-source machine learning software. The data visualization helps the trader to design the strategy to trade the forex market.
Assignment template pg research metodology (502)Abhilash Kharvi
In this Assignment paper to help for the students to learn research methodology to find out what are the methods and Technics are used in Journals
In this Paper to take particular topic to find out the Particular Problems
In this paper, we used financial statements as the main information to calculate the enterprise
value by discounted cash flow model. For the prediction of future cash flows in DCF model, a new method
based on the Markov chain is proposed to get the growth rates of future cash flows, instead of the fixed growth
rate method. The superior performance of it can be illustrated in empirical analysis. And the result shows that
we can improve the accuracy of the enterprise value evaluation with partial information by using the Markov
chain
Determinants of the implied equity risk premium in BrazilFGV Brazil
This paper proposes and tests market determinants of the equity risk premium (ERP) in Brazil. We use implied ERP, based on the Elton (1999) critique. The ultimate goal of this exercise is to demonstrate that the calculation of implied, as opposed to historical ERP makes sense, because it varies, in the expected direction, with changes in fundamental market indicators. The ERP for Brazil is calculated as a mean of large samples of individual stock prices in each month in the January, 1995 to September, 2015 period, using the “implied risk premium” approach. As determinants of changes in the ERP we obtain, as significant, and in the expected direction: changes in the CDI rate, in the country debt risk spread, in the US market liquidity premium and in the level of the S&P500. The influence of the proposed determining factors is tested with the use of time series regression analysis. The possibility of a change in that relationship with the 2008 crisis was also tested, and the results indicate that the global financial crisis had no significant impact on the nature of the relationship between the ERP and its determining factors. For comparison purposes, we also consider the same variables as determinants of the ERP calculated with average historical returns, as is common in professional practice. First, the constructed series does not exhibit any relationship to known market-events. Second, the variables found to be significantly associated with historical ERP do not exhibit any intuitive relationship with compensation for market risk.
Authors:
Sanvicente, Antonio Zoratto
Carvalho, Mauricio Rocha de
FGV's Sao Paulo School of Economics (EESP)
Predicting future sales is intended to control the number of existing stock, so the lack or excess stock can be minimized. When the number of sales can be accurately predicted, then the fulfillment of consumer demand can be prepared in a timely and cooperation with the supplier company can be maintained properly so that the company can avoid losing sales and customers. This study aims to propose a model to predict the sales quantity (multi-products) by adopting the Recency-Frequency-Monetary (RFM) concept and Fuzzy Analytic Hierarchy Process (FAHP) method. The measurement of sales prediction accuracy in this study using a standard measurement of Mean Absolute Percentage Error (MAPE), which is the most important criteria in analyzing the accuracy of the prediction. The results indicate that the average MAPE value of the model was high (3.22%), so this model can be referred to as a sales prediction model.
Testing and extending the capital asset pricing modelGabriel Koh
This paper attempts to prove whether the conventional Capital Asset Pricing Model (CAPM) holds with respect to a set of asset returns. Starting with the Fama-Macbeth cross-sectional regression, we prove through the significance of pricing errors that the CAPM does not hold. Hence, we expand the original CAPM by including risk factors and factor-mimicking portfolios built on firm-specific characteristics and test for their significance in the model. Ultimately, by adding significant factors, we find that the model helps to better explain asset returns, but does still not entirely capture pricing errors.
Modelling: What’s next for Financial Services in Europe?GRATeam
This paper outlines a practical roadmap to realising cost savings, delivering a material reduction in the volume and complexity of models by outlining five key principles of model optimisation: develop a comprehensive review of models, harmonise methodologies, re-design model validation/monitoring process, re-think its modelling team’s organisation & governance and build new expertise and recruit talent.
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Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
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A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
1. International Journal of Business and Management; Vol. 10, No. 10; 2015
ISSN 1833-3850 E-ISSN 1833-8119
Published by Canadian Center of Science and Education
194
Analysis of Relevance Concept of Measurement Capm Return and Risk
of Shares
Aminullah Assagaf1
1
Post Graduate Program University Dr. Soetomo, Surabaya, Indonesia
Correspondence: Priyono, Post Graduate Program University Dr. Soetomo, Surabaya, Indonesia. E-mail:
priyono.unu_sidoarjo@yahoo.com
Received: August 6, 2015 Accepted: September 10, 2015 Online Published: September 18, 2015
doi:10.5539/ijbm.v10n10p194 URL: http://dx.doi.org/10.5539/ijbm.v10n10p194
Abstract
This study aims to ensure that the concept of CAPM still have strong relevance for use by financial experts,
decision makers or management of companies and investors in the capital market to determine or quantify the risk
or beta (β) shares.
The method used is the econometric approach and try to compare earlier research model that has been used by
researchers and financial experts in several different places and periods. The selected sample is the most actively
traded shares on the Indonesian Stock Exchange (BEI) in the period September to November 2014. The data used
is data weekly or every Wednesday, to avoid anomalies or abnormalities price on Monday and Friday.
The results obtained from this study are: (A) model that has been used or developed but the result is the same in the
valuation of the beta (β), but differ in the value of alpha (α) in each of these models, because the risk free return (Rf)
constant during the observation period, or there will be differences in the value of beta (β) and alpha (α) when Rf is
varied during the observation period. (B) According to research data, the ratio of non-linear or quadratic models is
more relevant to use in assessing beta stocks, as comparison determinant coefficient (R2
) of linear and non-linear
models for the entire company shares were observed. (C) Results of measurement estimate stock return (Ri) both
with linear and non-linear models, shows that the concept is still relevant or CAPM model used in the
measurement of beta (β) shares. (D) non-linear model based Ln can not be used when there are negative returns or
loss, as in this study, the data to for transformation will an error and SPSS simulation process can not be done. (E)
In addition to the concept of CAPM still there are many important things that must be considered in relation to the
risk and return of investment.
Keywords: capital asset pricing model (CAPM), arbitrage pricing theory (APT), capital investment
1. Introduction
The concept of capital asset pricing model or CAPM is the traditional concepts that are often found in the books of
financial management, and is widely used by financial experts, management or enterprise decision makers and
investors in the analysis of investment in the capital market. The concept is relatively simple CAPM application
because the data is relatively easy to obtain through various information media. Data availability and relatively
easy to obtain in the form of time series and cross section so facilitate researchers.
to access from a variety of sources available either manually or online system. Time series data are available
various alternatives in the form of an annual period, quarterly, monthly, weekly and daily. Departing from the ease
of data acquisition and understanding of CAPM concepts, so widely used in research and investment decisions in
the stock market. On the other hand, the concept of CAPM is also heavily criticized, as in the study of Farma and
Frence (1992) in Brigman and Houston (2010) who found that there was no historical relation between stock
returns (Ri) and beta (β) market (Rm). Furthermore, studies Farma and France (2001) in Harjito and Martono
(2012) suggested that there are two variables that are consistently associated return stock that is (a) the size of the
company and (b) the ratio of market to book value. The study also found that smaller companies provide higher
yields, and higher return on stocks with market-to-book ratio is low. Instead after controlling for size company and
market-to-book ratio, they found no association between beta stocks with the rate of return or return these shares.
The weakness of the CAPM concept feared by researchers and analysis, then they do innovation in a way modify
or develop independent variables were considered related and not covered by the concept of CAPM. They
2. www.ccsenet.org/ijbm International Journal of Business and Management Vol. 10, No. 10; 2015
195
developed a model with variables that are considered more relevant, known as multivariable models. in
multivariable model is assumed that the risk is caused by several factors, while the concept of CAPM measure of
risk is confined to the return market (Rm). This multivariable models as a positive step that is considered important
in the theory of financial science, but still has weakness when applied in decision making. This is why the concept
of CAPM remains the most widely used method of estimating the stock return. CAPM concept is still quite
relevant and has the advantage, but in the development of the future is still possible to do modifications and
enhanced as was done in this study. Previous models that have been developed to improve the CAPM concept as
mentioned above, which adds several independent variables, but among the variables used in econometric turned
out to contain weakness in the form of violation of classical assumptions in shape multicollinearity or there is a
relationship between independent variables. Consequences for violations of the assumption of multicollinearity
both statistically and in theory, it is not valid models used to estimate the dependent variable. Variable use inflation,
interest rates and foreign exchange rates, in theory all three interrelated or occur multicollinearity among these
variables, so it is not valid to use in assessing stock returns (Ri). In addition, the three variables included in the
variable mentioned already market return (Rm) composite stock price index or CSPI used in CAPM model.
Empirical evidence shows that multicollienarity between variable inflation, interest and foreign exchange rates, for
example (a) originated from changes in the dollar exchange rate will push up the price of the product in the country
that uses the currency component of foreign and other related products in the market mechanism. (B) the price rise
could potentially bring wisdom monetary authorities to curb inflation through changes in prevailing interest rates.
Raise interest rates by the central bank is due to reasons of macro economic theory, namely the general interest
must be greater than inflation in order to avoid rush or withdrawal society massively because the return on savings
is less than the value inflation lowering the intrinsic value of the money saved in the bank. Another way people are
in a rush to move their funds in accounts dollars or other foreign currencies, which have an impact on the value of
the rupiah is getting worse, inflation is increasing, then the interest rises, and so on. Where the benefits of the
concept of CAPM because only the market return (Rm) as the only independent variable used in measuring the
impact on stock returns (Ri) was observed. The views of experts and practitioners who wish to modify and enhance
the CAPM concept as found in the approach Arbitrage Pricing Theory (APT), but the result is not optimal, because
of the weakness of the model innovation that is causing the invalid multicollinearity used in the valuation of stock
return (Ri). Based on the description above, in this study will be assessed the level of relevance of CAPM concept
that uses linear models, while the socio-economic conditions increasingly complex and takes place nonlinear. that
become the central issue in this study is whether the concept of CAPM is still relevant and reasonably fit for use in
measuring the value of beta (β) stock and stock return prediction (Ri) was observed. The objective was to examine
the relevance and appropriateness of the use of the concept of CAPM in an era of increasingly advanced
technological developments and increasingly complex business environment, by comparing the CAPM concept
with a model that will be developed.
Then the results will be tested or proved by using time series data development market return ( Rm ) to changes in
stock return ( Ri ) seed company or the most active stocks traded on the Indonesia Stock Exchange .
2. Literature Review and Reserach Hypothesis
The model capital asset pricing model , or CAPM not only as an abstract theory which is much discussed in various
literature sumer but this model has contributed and appeal of many parties to implement it in decision-making ,
notably by financial experts , management companies and investors .
CAPM model uses a variable stock return (Ri) , return free risk (Rf) and the market return (Rm), which is described
in equation form :
where
Ri = return of shares required by securities i;
Rf = risk-free return or return free risk;
Rm = market return or return market;
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a. Short sales allowed, that it can not sell the securities which are owned and use the cash generated to purchase
other securities.
b. There is a risk freelending and borrowing rate.
c. Not considering the tax borne by the investor or tax free.
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Ln (Rit - RFT) = Ln Ln αi + βi (RMT + RFT) + eit
2). The non-linear model with the transformation of data into a quadratic, as a research model development Balck,
Jensen, and Scholes (1972) referred to above.
(Rit - RFT) 2 = αi + βi (RMT + RFT) 2 + eit
3. Research and Methodolody
3.1 Model Analysis
Based on the model that has been used earlier as noted above, this study used a more relevant model to estimate
beta stocks. And as a comparison, the study also used simulated by using the model as in the previous studies and
literature review reserach hyphothesis mentioned above.
To prove the relevance of the model, then the selected samples in this study are the stocks of companies included in
the category of seed stock or the most actively traded on the Indonesia Stock Exchange. The data used are time
series period September to November 2014 using weekly data on every Wednesday. Wednesday consideration is
to obtain a rational price levels or avoid contention anomalies or abnormalities presumption stock market prices on
Monday and Friday.
From these data dioleh appropriate measurement method that has been determined, the calculation is then
processed through SPSS software version 22. In the simulation process, use the model as mentioned above, then
the results were compared between one model with other models. Results of the comparison and analysis
conducted in the end can be described estimation models are more relevant in assessing stock beta (β) and stock
return (Ri).
The model used in this study consisted of a model of linear and non-linear models as follows:
a. Model-1
Lintner study that was published by Douglas (1968) as in Husnan (2009).
Rit = αi + βi Rmt + eit
b. Model-2
Study Miller and Scholes (1972) as in Husnan (2009).
Rit = αt + βi (Rmt- RFT) + eit
c. Model-3
Black studies, Jensen, and Scholes (1972) as in Husnan (2009).
Rit = RFT + βi (Rmt- RFT) + eit
For the econometric estimation for which data are available and the process of regression calculations with SPSS
version 22, the simplified model used in the form of the following equation.
Rit - RFT = αi + βi (Rmt- RFT) + eit
d. Model-4
Non-linear models were developed in the form of quadratic.
(Rit - RFT) 2 = αi + βi (Rmt - RFT) 2 + eit
e. Model-5
Non-linear models developed by Husnan (2009).
Rit Ln = Ln Ln αi + βi Rmt + eit
f. Model-6
Non-linear models were developed from studies Black, Jensen, and Scholes (1972).
Ln (Rit - RFT) = Ln Ln αi + βi (Rmt- RFT) + eit
To prove the ability of the model to explain the phenomenon in terms of the return shares with a market return, will
be proven by the statistics and their relevance to actual conditions. The statistics will be compared mainly to the
beta coefficient stock, constants, the significance of the relationship between these variables and accuracy of
models used or the appropriate amount of determinand coefficient (R2
).
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3.2 Determination of the Sample
To illustrate the relevance of CAPM kosep then in the study carried out observations of some leading stocks or the
most actively traded on the Indonesia Stock Exchange during the period September to November 2014 using a
sample weekly period as mentioned above, namely the period of 2 September 2014 until 12 November 2014 .
Shares of the company were chosen as samples are (a) International Astra or ASII, industrial or automotive
business, (b) Bank Central Asia or BBCA, banking business, (c) Bank Negara Indonesia 1946 or BBNI, banking
business, (d) Warehouse salt or GGRM, the tobacco industry, (e) Hero Super Market or HERO, retail business, (f)
Indofood Super Market or INDF, business retal and foodstuffs, (g) Kimia Farma or KAEF, binis pharmacy, (h)
Gas perusahaa or PGAS, gas fuel business, (i) Cement Cibinong or SMCB, cement industry, (j) Wijaya Karya
Beton or WTON, konstrusi service businesses, and (k) Unilever Indonesia or UNVR, distributor business.
3.3 The Definition and Measurement of Variables
The type of data collected from these companies are stock returns or Ri, return market or Rm and return free risk or
Rf.
(A) Return of shares or Ri, is the rate of return or income derived from each of the company's shares as a result of
changes in prices between the time or in every week as measured by calculation,
Ri = (Pit - Pit-1) / Pit-1
If the price happens (Pit) is greater than the price of the previous week (Pit-1), then there is a return or income, the
opposite occurs when the price of (Pit) lower than the previous week (Pit-1), then the negative returns or loss , On
the price difference divided by the price of the previous week (Pit-1), so that the quantity of stock returns (Ri).
(B) Return Rm market or obtained from the difference between the composite stock price index that occurred at
that time (IHSGt) with the composite share price index of the previous week (IHSGt-1). Positive difference shows
the income or return, otherwise the negative difference between the period showed a negative return or loss. Of the
difference is then divided by the composite stock price index the previous week (IHSGt-1), in order to obtain the
magnitude of the market return (Rm) which is depicted in the following formulation:
Rm = (IHSGit – IHSGit-1) / IHSGit-1
(C) Return Rf as free of risk or risk-free rate of return , in this case used the average deposit interest rate between
banks . The deposit rate is expressed in annual percentage , so as to obtain a weekly risk-free return , the percentage
of the split 52 or 52 weeks a year . Return free of risk or Rf formulated namely.
Rf = Return of risk free annual / 52
In the measurement of a relatively short period of time , then return free risk or risk-free rate of return tend to be
constant , but in the long term are likely to change .
4. Research and Discussion
4.1 Data Research
Based on the results of research on secondary data obtained from the Indonesia Stock Exchange through
www.invessmentworld / bei / prices / stock can be obtained and calculated the amount of stock return ( Ri ) , the
market return ( Rm ) and the risk free return ( Rf ) required for calculates coefficients beta stock ( Rm - Rf ) and
alpha (α) will be done using SPSS software version 22. research data were calculated using the method of
measurement as set forth in reseach and methodology referred to above with the following results
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Source : Indonesia stock exchange (http://wwww.duniainvestasi.com/bei/prices/stock).
1). Estimation Model-1
The estimation results with SPSS 22: using Lintner models are loaded by Douglas ( 1968) as in Husnan (2009 ) ,
Rit = αi + βi Rmt + eit
Stock beta coefficient (β) and alpha (α) of the equation Model -1 above , can be seen on each company observed as
set forth in the following table:
Source : Indonesia stock exchange (http://wwww.duniainvestasi.com/bei/prices/stock).
-Lintner model implementation as table calculation results SPSS version 22 mentioned above, showing beta (β)
shares and alpha (α) as follows,
-Beta (β) shares the company's overall result was observed consistently positive, which means that the higher the
risk of the stock, the greater the possibility of obtaining a stock return (Ri).
-Beta (β) stock shows the magnitude of the changes that will occur in the stock return (Ri) when there is a change of
one unit in the variable (Rm - Rf).
-Value or quantity of Beta (β) shares, vary from one company to another company. This is mainly due to the
performance of the company and the company's operating conditions in the face of changes in the macroeconomic
environment or market that is reflected by the stock price index (CSPI). This can be seen in companies that have a
relatively high beta stocks than other companies such as Unilever (UNVR) distribsi sector, following Bank Negara
Indonesia 1946 (BBNI) banking sector, Kimia Farma (KAEF) pharmaceutical sector and Indonesia Astra
Year Market R Risk Free
2014 ASII BBCA BBNI GGRM HERO INDF KAEF PGAS SMCB WTON UNVR (Rm) (Rf)
02-Sep
10-Sep 0,00280 0,00296 0,00046 0,00079 0,00218 0,00014 0,00671 0,00025 0,00295 0,01688 0,00077 (0,01127) 0,00115
17-Sep 0,00006 0,00009 0,00003 0,00248 0,00067 0,00028 0,00005 0,00000 0,00004 0,00009 0,00053 0,00881 0,00115
24-Sep 0,00013 0,00030 0,00151 0,00032 0,00074 0,00002 0,00068 0,00005 0,00031 0,00062 0,00037 (0,00274) 0,00115
01-Okt 0,00084 0,00184 0,00232 0,00004 0,00000 0,00014 0,00071 0,00032 0,00191 0,00268 0,00020 (0,00640) 0,00115
08-Okt 0,00194 0,00172 0,00302 0,00002 0,00000 0,00137 0,01032 0,00095 0,00012 0,01069 0,00157 (0,03548) 0,00115
15-Okt 0,00211 0,00017 0,00214 0,00097 0,00145 0,00158 0,00585 0,00014 0,00051 0,00958 0,00026 0,00089 0,00115
22-Okt 0,00050 0,00070 0,00236 0,00040 0,00229 0,00111 0,00067 0,00020 0,00043 0,00383 0,00082 0,02245 0,00115
29-Okt 0,00235 0,00066 0,00020 0,00004 0,00152 0,00094 0,00101 0,00146 0,00290 0,00009 0,00090 (0,00004) 0,00115
05-Nov 0,00010 0,00047 0,00003 0,00684 0,00108 0,00004 0,02063 0,00003 0,00423 0,00125 0,00068 (0,00144) 0,00115
12-Nov 0,00042 0,00034 0,00087 0,00059 0,00050 0,00016 0,00118 0,00013 0,00001 0,00150 0,00004 (0,00355) 0,00115
Stock Return (Ri)
Stock Return (Ri), Market Return (Rm) dan Risk Free (Rf)
No. Code Company α β Rm R
2
1 ASII Astra Internasional -0,0043 1,240 0,287
2 BBCA BCA 0,012 0,724 0,121
3 BBNI BNI 0,0113 1,570 0,380
4 GGRM Gudang Garam 0,0094 0,198 0,006
5 HERO Hero Super Market 0,0037 0,969 0,176
6 INDF Indofood Super Market -0,0056 0,400 0,061
7 KAEF Kimia Farma 0,0108 2,674 0,292
8 PGAS Perusahaan Gas 0,0037 0,360 0,072
9 SMCB Semen Cibin (Holcim Indo) -0,0230 0,555 0,088
10 WTON Wijaya Karya Beton 0,0217 2,148 0,199
11 UNVR Unilever Indonesia -0,0008 1,231 0,507
Ri = α + βRm + ei
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International (ASII) sector otomotive. Otherwise some companies are not sensitive to changes in its return when
there is a change in Rom as the company Gudang Garam (GGRM) and Business Process Gas cigarette sector
(PGAS) fuel sector. Beta stocks showed a relatively very small stock returns have changed very little when there is
a change in Rm. Such conditions can be stated that the company has an operational system performance and stable
business that are less affected to market changes, such as tobacco consumption and relatively stable gas on stable
economic conditions or crises. The risk is relatively small shares of the investor but it is less interesting, because
they expect return derived from changes in stock prices
- I- Alpha (α) shows the influence of other factors not accounted for in the study, so that in case the condition Rm
= 0, then the stock return (Ri) of alpha (α). In the econometric analysis model assumed that other factors constant
alpha (α) is expressed as a constant.
- The results of the above study showed the amount of alpha (α) is not consistent between one company with
another company. There are 4 companies that have a magnitude of alpha (α) which is negative as the company (a)
International Astra Indonesia (ASII) automotive sector, (b) Super Market Indofood (INDF) sector of foodstuffs, (c)
Cement Cibinong (SMCB) cement production, and (d) Unilever Indonesia (UNVR) distribution sector. While
other companies have a value of alpha (α) is positive.
- Alpha (α) value negative indicates that there are other factors that are not accounted for in market variables that
negatively affect stock return is concerned, and vice versa in case the value of alpha (α) is positive. Other variables
that are not accounted for in these models but have an influence on stock returns (Ri) among other fundamental
corporate performance appropriate operational condition, so although the market return (Rm) does not change,
then returns appropriate shares will receive the amount of alpha (α), and suffered losses when the value of alpha (α)
is negative.
- This model does not use the CAPM model as a whole because it ignores the return free risk factor (Rf) or just use
factor Rm, so the beta is generated possibility of a different result when compared to that using the CAPM model
(Rm - Rf) as independent variables
- 1. Estimation Model - 2
- The estimation results with SPSS 22: using the model Miller and Scholes (1972 ) in Husnan (2009 ) ,
- Rit = αt + βi ( Rmt- RFT ) + eit
- Stock beta coefficient (β) and alpha (α) from the equation model - 2 above , can be seen on each company
observed as set forth in the following table.
Source : Indonesia stock exchange (http://wwww.duniainvestasi.com/bei/prices/stock).
- Beta (β) observed overall stock company, the result was consistently positive, which means the higher the risk
more and more stock likely acquire stock returns. This is consistent with the Model - 1 above, which shares the
beta coefficient or positive and is equal between the Model-1 with Model-2. This happens because the risk free
return (Rf) value constant during the observation period. When changes or variations to the risk free return (Rf)
during the observation period coefficient or beta stocks will differ in value.
No. Code Company α β(Rm - Rf) R
2
1 ASII Astra Internasional -0,0029 1,240 0,287
2 BBCA BCA 0,013 0,724 0,121
3 BBNI BNI 0,0131 1,570 0,380
4 GGRM Gudang Garam 0,0097 0,198 0,006
5 HERO Hero Super Market 0,0048 0,969 0,176
6 INDF Indofood Super Market -0,0051 0,400 0,061
7 KAEF Kimia Farma 0,0139 2,674 0,292
8 PGAS Perusahaan Gas 0,0042 0,360 0,072
9 SMCB Semen Cibin (Holcim Indo) -0,0223 0,555 0,088
10 WTON Wijaya Karya Beton 0,0241 2,148 0,199
11 UNVR Unilever Indonesia 0,0006 1,231 0,507
Ri = α + β(Rm - Rf) + ei
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- Alpha (α) obtained the same tendency with the model-1 above that the 3 companies that together have an alpha (α)
is negative, while the company Unilever Indonesia (UNVR) and other companies have value of alpha (α) is
positive. The value of alpha (α) is different than the Model-1 mainly because it takes into account the Model-2
return free risk factor (Rf) in measuring return market that expressed in (Rm-Rf).
- Model - 2 is not use Rf as a constant variable in the equation as the CAPM model:
Ri = Rf + βi (RMT + RFT) + eit
but using a model or equation:
Ri = α + βi (RMT + RFT) + eit
Supposedly relevant to the CAPM model are:
Ri - Rf = α + βi (RMT + RFT) + eit
Analyzes relevant to the CAPM model used on the Model - 3 below.
1. Estimation Model - 3
The estimation results with SPSS 22: using the model developed in the study Balck, Jensen, and Scholes (1972) as
in Husnan (2009),
Rit = RFT + βi (Rmt - RFT) + eit
For the econometric estimation for which data are available and the process of regression calculation, the
simplified models in the form of the following equation,
Rit - RFT = αi + βi (Rmt - RFT) + eit
Stock beta coefficient (β) and alpha (α) from the equation model - 3 above, can be seen on each company observed
as set forth in the following table.
Source : Indonesia stock exchange (http://wwww.duniainvestasi.com/bei/prices/stock).
- Beta (β) observed overall stock company, the result was consistently positive, which means the higher the risk the
greater the possibility of obtaining shares of stock return. This is consistent with the Models 1 and -2 above, the
beta coefficient, or positive stock and the value is the same, this is mainly due to the risk free return (Rf) of its
constant value during the observation period. When changes or variations to the risk free return (Rf) during the
observation period, the beta coefficient stock (β) will differ in value.
- The results of the above study showed the amount of alpha (α) is not consistent from one company to the other
company. There are 4 companies have a magnitude of alpha (α) is negative or equal to -1 Model, namely the
company (a) International Astra Indonesai (ASII) automotive sector, (b) Super Market Indofood (INDF) sector of
foodstuffs, (c) Cement cibinong (SMCB) and cement production (d) Unilever Indonesia (UNVR) distribution
sector. While other companies have a value of alpha (α) is positive.
No. Code Company α β(Rm - Rf) R
2
1 ASII Astra Internasional -0,0040 1,240 0,287
2 BBCA BCA 0,012 0,724 0,121
3 BBNI BNI 0,0120 1,570 0,380
4 GGRM Gudang Garam 0,0085 0,198 0,006
5 HERO Hero Super Market 0,0037 0,969 0,176
6 INDF Indofood Super Market -0,0063 0,400 0,061
7 KAEF Kimia Farma 0,0127 2,674 0,292
8 PGAS Perusahaan Gas 0,0030 0,360 0,072
9 SMCB Semen Cibin (Holcim Indo) -0,0235 0,555 0,088
10 WTON Wijaya Karya Beton 0,0230 2,148 0,199
11 UNVR Unilever Indonesia -0,0005 1,231 0,507
Ri - Rf = α + β(Rm - Rf) + ei
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- Conclusion The comparison between the 3 models, it turns out there is no difference if it is used to estimate the
stock beta (β), so it can be stated that the 3 models have the same ability to measure the level of risk and stock
return (Ri).
- To see the excellence and relevance of measurements of beta (β) stock, then in this study developed a model
non-linear through the transformation of data into a quadratic Ln and as the Model- 4, Models - 5 and Models - 6
below.
1. Estimation Model - 4
The estimation results with SPSS 22: Non Linear models in quadratic form , as follows ,
(Rit - RFT) 2 = αi + βi ( Rmt- RFT ) 2 + eit
Stock beta coefficient (β) and alpha (α) from the equation model - 4 above , can be seen on each company observed
as set forth in the following table.
Source : Indonesia stock exchange (http://wwww.duniainvestasi.com/bei/prices/stock).
Model-4 non-linear or quadratic shape, which is the development of Model-3 above. Non-linear model estimation
results indicate as follows,
- Coefficient, or beta (β) and constant or alpha (α) different from the results of the linear model simulation or
model-3.
- The value of beta (β) shares are not consistently positive as Model-3. There are 3 companies which had a beta
value of shares (β) is negative, the company (a) Gudang Garam (GGRM) with the tobacco industry or the beta
coefficient stock: -1.317, (b) Hero Supermarket (HERO) retail sector with a beta coefficient or stock: - 0.473, (c)
Cement Cibinong (SMCB) cement industry with a beta coefficient or stock: -1.178. While other companies have
a share beta coefficient (β) is positive as it did in Model-3, but different quantity or value.
- The value of beta (β) shares were negative indicates that the addition of a variable (Rm- Rf) would lead to a
decrease in stock returns (Ri) of beta (β) shares. This means that the stock returns negatively or inversely
proportional to the risk of the stock. Which means that the greater the risks faced, the smaller the stock return.
This is particularly relevant when compared with the company's business operations,
(A) Gudang Garam (GGRM) the tobacco industry, in the event of an increase in JCI is marked by the influence of
macroeconomic variables, the interest of investors to buy shares in the company is reduced, because the raw
materials are still the dominant cigarette industry uses imported components such as tobacco and others.
(B) Hero Super Market (HERO) retail business, JCI is not so encouraging increase in stock return (Ri) of this
company, because the components of the product resulting from a large number of goods sold still rely on imported
raw materials. As a result, the stock return (Ri) will tend to weaken if there is an increase or a composite stock price
index in Indonesia Stock Exchange Composite Index.
(C) Cement Cibinong (SMCB) cement industry, the increase in JCI weakening the stock return (Ri) is the cement
industry. This is mainly due to the consumption of cement in the country is much related to the progress of
No. Code Company α β(Rm - Rf) R
2
1 ASII Astra Internasional 0,0010 0,598 0,056
2 BBCA BCA 0,001 0,790 0,125
3 BBNI BNI 0,0009 1,659 0,391
4 GGRM Gudang Garam 0,0015 -1,317 0,070
5 HERO Hero Super Market 0,0011 -0,473 0,060
6 INDF Indofood Super Market 0,0004 0,736 0,262
7 KAEF Kimia Farma 0,0041 3,337 0,046
8 PGAS Perusahaan Gas 0,0003 0,428 0,145
9 SMCB Semen Cibin (Holcim Indo) 0,0016 -1,178 0,104
10 WTON Wijaya Karya Beton 0,0035 5,608 0,170
11 UNVR Unilever Indonesia 0,0004 0,856 0,657
(Ri - Rf)
2
= α + β(Rm - Rf)
2
+ ei
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infrastructure construction. When turmoil macroeconomic variables that cause the JCI rises, then the impact will
affect the price of raw materials required by the construction, and it also means that demand for cement will be
affected. In the prediction of stock return (Ri) conducted by investors in the shares of the cement industry is going
to decline that caused the stock price does not go up and result in a stock return (Ri) decreases
The accuracy of the linear model (Model-3) and non- linear models (Model-4) can be determined by comparing the
R2
deteminand coefficients obtained between the two models mentioned above. Determinant coefficient shows
ability to explain the change in the dependent variable stock return (Ri) as a result of changes in market returns
independent variables and risk free return (Rm - Rf). The greater the value of the determinant coefficient (R2
)
shows the better the model. Based on this view and pay attention determinant coefficient (R2
) of each company of
the 2 models in question, it can be concluded that the non- linear models or model - 4 is more relevant to use in
measuring the beta value of the company.
1). Estimation Model -5
Assessment of beta stocks as non linear models developed by Husnan (2009),
Rit Ln = Ln Ln αi + βi Rmt + eit
This model can not be used because the data available there is a negative return or loss, so the process was not
carried out due to an error preformance transformation into value Ln, so that can not be processed through
SPSS seftware. Ln non-linear models can be used when a data field is available change or are non-negative or
positive returns or losses.
2). Estimation Model - 6
Beta assessment of other stocks, as the development of studies Balck, Jensen, and Scholes (1972) as in Husnan
(2009) but using a non-linear models as follows,
Ln (Rit - RFT) = Ln Ln αi + βi (Rmt - RFT) + eit
Model-6 is the same as the Model-5, the available data can not be processed by Ln because negative returns or
loss will occur error when transformed into value Ln. Ln non-linear models can be used when a data field is
available change or are non-negative or positive returns or do not experience a loss.
5. Conclussion and Limitation Study
5.1 Conclussion
Based on the description above, can be summarized as follows:
1). Assessment of beta (β) stock produce the same figures for the use of Model 1, Model 2 and Model-3 because
the risk free return (Rf) constant during the observation period. If there is variation in the risk free return (Rf)
during the observation period, the stock beta (β) will differ among the 3models mentioned.
2). The constant or alpha (α) differ between Model 1, Model 2 and Model-3 because there is a different treatment to
retrun free risk indicators (Rf), namely (a) Model-1 does not use return free risk (Rf), (b) Model-2 using a risk
Code Model -3 Model -4 Diff
ASII 0,287 0,598 0,310
BBCA 0,121 0,790 0,669
BBNI 0,380 1,659 1,279
GGRM 0,006 -1,317 -1,324
HERO 0,176 -0,473 -0,648
INDF 0,061 0,736 0,675
KAEF 0,292 3,337 3,046
PGAS 0,072 0,428 0,356
SMCB 0,088 -1,178 -1,266
WTON 0,199 5,608 5,409
UNVR 0,507 0,856 0,349
COEFFICIENT DETERMINAND (R
2
)
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free return (Rf) only independent variable (Rm - Rf), (c) Model-3 using a risk free return (Rf) on stock return
(Ri - Rf) and the market return (Rm-Rf)
3). Comparison of linear and non-linear models showed that non-linear or quadratic superior and more relevant use
in estimating beta stocks. This is evidenced in the determinant coefficient (R2
) which obtained the larger
number of non linear model for the entire company that was observed during the period.
4). The results of the estimation of measurement stock return (Ri) both with linear and non-linear models, shows
that the concept or CAPM model is still relevant in measuring the beta (β) shares. Selection of the best models
and relevant in measuring the beta (β) stock, is dependent on the distribution of the data and suitability in the
selection of a good model of linear and non-linear.
5). The non-linear model with base Ln can not be used in this study because there are negative returns or loss,
resulting in an error when transformed into value Ln, and can not be done with SPSS simulation process.
Non-linear models Ln base on Model-5 and Model-6 can be used in other studies when the data in the form of
a positive return or no loss.
5.2 Limitation Study
In the implementation of the capital asset pricing model, or CAPM, experts in finance, management or decision
makers in the company and investors, should be aware of the limitations of the predictive ability of the risk and the
return generated by the CAPM model. There are several other important matters related to risk and stock return.
This is relevant with a view Husnan (2009) related to matters associated with risk and stock return. The important
thing in question is (a) there is a relationship between risk with return or return on investment, (b) diversification is
very important in reducing the level of risk without reducing the level of return, (c) return or refund is real
important things in investments, and contrast with the return nominal, (d) the risk of investment is usually
influenced by the duration period of an investment, which in the short term the possibility of such shares in the
long-term risk but the risk is relatively low, and (e) there is no guarantee that the past will be repeated in the future,
especially when associated with risk and investment returns of the past.
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