Patent Thickets, Stock Returns, and Conditional CAPM

Patent thickets, a phenomenon of fragmented ownership of overlapping patent rights, hamper firms’ commercialization of patents and thus deliver asset pricing implications. We show that firms with deeper patent thickets are involved in more patent litigations, launch fewer new products, and become less profitable in the future. These firms are also associated with lower subsequent stock returns, which can be explained by a conditional Capital Asset Pricing Model (CAPM) based on a general equilibrium model that features heterogeneous market betas conditional on time-varying aggregate productivity. This explanation is supported by further evidence from factor regressions and stochastic discount factor tests. This paper was accepted by Karl Diether, finance.

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.

Bank Compliance Factors in Implementing Regulation Provisions on Bank Performance in Indonesia

This study aims to examine the effect of legal compliance on the health of commercial banks and Islamic banks in Indonesia, to the extent that compliance with the provisions and standards set by Bank Indonesia has an impact on improving bank performance. This study uses micro banking data listed on the Indonesia Stock Exchange (IDX) for the period 2015- 2019. The data used is panel data that is tested in the relationship between measures of bank health legal compliance with indicators of capital, asset quality, management, earnings, and market risk sensitivity (CAMELS). The results of this study indicate that compliance with earnings and compliance with market risk sensitivity has a negative effect on bank performance, while compliance with liquidity has no effect on bank performance. Furthermore, three control variables used in this study, namely capital, asset quality, and corporate governance, were able to produce results as predicted. Received: 19 August 2021 / Accepted: 6 November 2021 / Published: 3 January 2022

ARBITRAGEUR BEHAVIOR IN SENTIMENT-DRIVEN ASSET-PRICING

This study aims to model arbitrageur behavior in a sentiment-driven capital asset-pricing model under the premise of reflecting a more detailed decomposition of investor types in the equity markets. We explore the behavior and the impact of arbitrageur behavior, particularly, on pricing and on key financial ratios. We observe that the prevalence of the arbitrageur counteracts the effects of unsophisticated investors, resulting in a lower volatility of the price–dividend ratio, lower predictive power of changes in consumption for future price changes and lower equity premium. Thus, the results of our research allow us to conjecture that the extrapolation bias in the prices is lowered.

Analisis Tingkat Kesehatan Laporan Keuangan pada PT Bank Syariah Mandiri Tbk. dengan Metode CAMEL Periode 2016 – 2020

The purpose of this research is to see and find out the level of the soundness of the financial statements at PT Bank Syariah Mandiri Tbk using the CAMEL method (Capital, Asset Quality, The method of data collection used in this research is the documentation of secondary data sources that have been collected and have been documented on the bank's official website in the form of existing financial reports. The results obtained in this study are CAR, ROA, and NI very healthy, NPF, ROE, FDR healthy, and PDN, BOPO quite healthy.

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.

How an idiosyncratic (zero-beta) risk can greatly increase the firm’s cost of capital

The celebrated capital asset pricing model (‘CAPM’) brought numerous appealing insights and spawned a new theory of capital budgeting. One key intuition is that there is ‘no penalty for diversifiable risk’ – that is, any risky payoff that has zero-correlation with the wider economy, and hence zero-beta, is treated as ‘risk-free’. Does that mean that managers can bet the firm on a spin of the roulette wheel without attracting a higher CAPM discount rate? Our re-interpretation of CAPM reveals that potential financial losses which are conventionally regarded as firm-specific ‘unpriced’ risks can bring a large increase in the firm’s beta and CAPM cost of capital, despite having zero-beta and making only negligible difference at the aggregate market level. This mathematical result clashes with textbook expositions but is easily demonstrated and can be traced to authoritative but overlooked parts of the theoretical CAPM literature. JEL Classification: G11, G12

PENENTUAN KEPUTUSAN INVESTASI SAHAM MENGGUNAKAN CAPITAL ASSET PRICING MODEL (CAPM) DENGAN PENAKSIR PARAMETER STOKASTIK

CAPM is a method of determining efficient or inefficient stocks based on the differences between individual returns and expected returns based on the CAPM’s positive value for efficient and negative value for inefficient stocks. The move to share prices in the process can influence investors's decisions in investing funds, so that it can be formulated in stochastic differential equations that form the Geometric Brownian Motion model (GBM). The purpose of the study is to determine return value using the CAPM based on share estimates and historical stock prices. The study uses secondary data that data a monthly closing of stock prices from December 2017 to December 2020. The GBG model's estimated stock price is used to determine the expected value return using the CAPM. In this case, it is called CAPM-Stochastic. Then the results of the CAPM-Stochastic was compared to the results of the CAPM-Historical to define efficient stocks and inefficient stocks. The results of research using CAPM-Stochastic obtained that HMSP, ICBP, KLBF, and WOOD shares are efficient stock while UNVR shares are inefficient. The results of CAPM-Historical obtained that HMSP, ICBP, KLBF, and UNVR shares are inefficient stocks and WOOD is an efficient stocks.