Analysis Investor Index Indonesia with Capital Asset Pricing Model (CAPM)

This study aimed to compare composition of the optimal portfolio of stocks, the proportion of funds in each of these stocks and calculate risk and return portfolio from Investor33 (INV33) Index and Jakarta Islamic Index (JII) in research period January 2016-December 2018. The method used in this research is a quantitative descriptive method. Sample in this study using purposive sampling were 24 stock from INV33 Index and 17 stock from JII Index. The results of the study were as follows : (1) The optimal portfolio of stocks by using capital asset pricing model from INV33 Index are CPIN (Charoen Pokphand Indonesia Tbk), ITMG (Indo Tambangraya Megah Tbk), BBCA (Bank Central Asia Tbk), UNTR (United Tractor Tbk), (TLKM) Telekomunikasi Indonesia (Persero) Tbk, ICBP (Indofood CBP Sukses Makmur Tbk), BBTN (Bank Tabungan Negara Persero Tbk and from JII Index are ADRO (Adaro Energy Tbk), ICBP (Indofood CBP Sukses Makmur Tbk), INCO (Vale Indonesia Tbk), INDF (Indofood Sukses Makmur Tbk), TLKM (Telekomunikasi Indonesia Persero Tbk), UNTR (United Tractor Tbk). (2) The composition of the proportion of funds in optimal portfolio formed by INV33 Index are BBCA (46,49%), CPIN (20,11%), ICBP (12,78%), ITMG (8,59%), UNTR (6,95%), TLKM (4,11%) and BBTN (0,97%) and from JII Index are ICBP (34,96%), ADRO (19,47%), UNTR (16,26%), INCO (10,88%), TLKM (10,43%) and INDF (8,00%). (3) The optimal portfolio of stocks return from INV33 Index was greater than stock portfolio return from JII Index and the optimal portfolio of stocks risk from INV33 Index was lower than stock portfolio risk from JII Index.

Erklärung von Aktienrenditen am deutschen Aktienmarkt in Jahren der Krise – Validierung des Dreifaktorenmodells von Fama und French

Zusammenfassung In Krisenzeiten steigt die Volatilität an den Aktienmärkten. Es stellt sich die Frage, ob Kapitalmarktmodelle in diesen Perioden verlässliche Ergebnisse erbringen können. In Konkurrenz stehen das Capital Asset Pricing Model (CAPM) sowie Mehrfaktorenmodelle wie das von Fama und French entwickelte Dreifaktorenmodell. Anhand des Deutschen Aktienindex (DAX) wird untersucht, welches Konzept eine bessere Erklärungskraft für Renditen bietet. Es wird geprüft, ob im vorliegenden Untersuchungszeitraum von 2005 bis 2020 ein Size- und ein Value-Effekt vorliegen und welche Erklärungsansätze für diese Anomalien bestehen. Bei Anwendung des Dreifaktorenmodells lag das korrigierte Bestimmtheitsmaß R 2 für die gebildeten Portfolios deutlich höher als beim CAPM. Zudem zeigen die Ergebnisse der T-Tests, die Signifikanzniveaus und der F-Tests, dass das Dreifaktorenmodell dem CAPM für die Erklärung der Portfoliorenditen überlegen ist. Der Analysezeitraum umfasst zwei große Wirtschaftskrisen. Zum einen die Finanzmarktkrise mit Ausstrahlungseffekten auf die Weltbörsen und zum anderem eine weltweite Pandemie mit ebenfalls starken Verwerfungen an den Finanzmärkten. Auch in den Krisenjahren konnte das Dreifaktorenmodells im Vergleich zum CAPM brillieren.

Downside Beta and Downside Gamma: In Search for a Better Capital Asset Pricing Model

In the financial world, the importance of “downside risk” and “higher moments” has been emphasized, predominantly in developing countries such as Pakistan, for a substantial period. Consequently, this study tests four models for a suitable capital asset pricing model. These models are CAPM’s beta, beta replaced by skewness (gamma), CAPM’s beta with gamma, downside beta CAPM (DCAPM), downside beta replaced by downside gamma, and CAPM with downside gamma. The problems of the high correlation between the beta and downside beta models from a regressand point of view is resolved by constructing a double-sorted portfolio of each factor loading. The problem of the high correlation between the beta and gamma, and, similarly, between the downside beta and downside gamma, is resolved by orthogonalizing each risk measure in a two-factor setting. Standard two-pass regression is applied, and the results are reported and analyzed in terms of R2, the significance of the factor loadings, and the risk–return relationship in each model. The risk proxies of the downside beta/gamma are based on Hogan and Warren, Harlow and Rao, and Estrada. The results indicate that the single factor models based on the beta/downside beta or even gamma/downside gamma are not a better choice among all the risk proxies. However, the beta and gamma factors are rejected at a 5% and 1% significance level for different risk proxies. The obvious choice based on the results is an asset pricing model with two risk measures.

Rolling regression technique and cross-sectional regression: A tool to analyze Capital Asset Pricing Model

The Capital Asset Pricing Model (henceforth, CAPM) is considered an extensively used technique to approximate asset pricing in the field of finance. The CAPM holds the power to explicate stock movements by means of its sole factor that is beta co-efficient. This study focuses on the application of rolling regression and cross-sectional regression techniques on Indian BSE 30 stocks. The study examines the risk-return analysis by using this modern technique. The applicability of these techniques is being viewed in changing business environments. These techniques help to find the effect of selected variables on average stock returns. A rolling regression study rolls the data for changing the windows for every 3-month period for three years. The study modifies the model with and without intercept values. This has been applied to the monthly prices of 30 BSE stocks. The study period is from January 2009 to December 2018. The study revealed that beta is a good predictor for analyzing stock returns, but not the intercept values in the developed model. On the other hand, applying cross-section regression accepts the null hypothesis. α, β, β2 ≠ 0. Therefore, a researcher is faced with the task of finding limitations of each methodology and bringing the best output in the model.

The Capital Asset Pricing Model

The capital asset pricing model (CAPM) is an influential paradigm in financial risk management. It formalizes mean-variance optimization of a risky portfolio given the presence of a risk-free investment such as short-term government bonds. The CAPM defines the price of financial assets according to the premium demanded by investors for bearing excess risk.

Investment decision by using Capital Asset Method Pricing Model (CAPM) (Case studies on five automotive companies listed in stock exchange)

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.

An Empirical Research of the Consumption-based Capital Asset Pricing Model: Evidence from China’s Stock Market

Recently, the Capital Asset Pricing Model has been widely used in the stock market. The traditional Capital Asset Pricing Model has been revised and expanded to the Consumption-based Capital Asset Model. This article does the research in the following ways. Firstly, this article summarizes the Capital Asset Pricing Model and empirical method. Secondly, it analyzes and processes the data worked out of the Capital Asset Pricing Model. Finally, it analyzes the empirical results.