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.

Measuring Islamic Stock Performance in Indonesia with A Modified Sharpe Ratio

Since the late 1960s, one of the stock performance analysis tools commonly used is Sharpe Ratio. The Sharpe Ratio consists of three components, namely stock return, risk-free returns, and stock risk. Many studies approach risk-free returns with interest rates, including when measuring the performance of Islamic stocks, while interest rates are prohibited in the concept of Islamic finance. Moreover, the stock risk is measured by a standard deviation which assumes returns are normally distributed, while many stock returns are non-normally distributed. This paper intends to measure the performance of Islamic stocks listed on the Indonesian Stock Exchange (IDX) for the period of January 2011 to July 2018 using a modified Sharpe Ratio. The ratio is modified by replacing the interest rate with four approaches: eliminating the interest rate, changing with zakah rates, changing with inflation, changing with the nominal gross domestic product, and replacing the risk measurement from Standard Deviation to Value at Risk (VaR). The findings provide almost the same results as the original measurement and thus, show very high suitability for using these models in other circumstances. Therefore, on the concept of Islamic finance, risk-free returns can be measured using these four approaches, especially inflation and GDP. This study also recommends inflation and GDP to measure risk-free returns in the Sharia's Compliant Asset Pricing Model (SCAPM) or Islamic Capital Asset Pricing Model (ICAPM).====================================================================================================ABSTRAK – Pengukuran Kinerja Saham Syariah di Indonesia menggunakan Sharpe Ratio Modifikasi. Sejak akhir 1960-an, salah satu alat mengukur kinerja saham yang biasa digunakan adalah Sharpe Ratio. Model Sharpe Ratio terdiri atas tiga komponen, yaitu return saham, return bebas risiko, dan risiko saham. Return bebas risiko diukur mengunakan variabel suku bunga yang digolongkan riba dan dilarang dalam konsep keuangan islam. Sedangkan risiko saham diukur dengan standar deviasi yang mengasumsikan data berdistribusi normal. Paper ini bertujuan untuk mengukur kinerja saham syariah yang terdaftar pada Bursa Efek Indonesia (BEI) untuk periode Januari 2011 sampai Juli 2018 dengan menggunakan Sharpe Ratio modifikasi. Kajian akan memodifikasi model Sharpe Ratio dengan mencari variabel alternatif penganti suku bunga dengan empat pendekatan, yaitu: menghilangkan variabel suku bunga tersebut, mengganti dengan zakat rate, mengganti dengan inflasi, dan mengganti dengan produk domestik bruto, serta mengganti standar deviasi dengan Value at Risk (VaR) sebagai pengukur risiko saham yang selanjutnya diimplementasikan pada pasar modal syariah di Indonesia periode Januari 2011 - Juli 2018. Hasil kajian menunjukkan kesesuaian yang sangat tinggi untuk hasil pengukuran kelima model tersebut. Dilihat dari kedekatan hasil pengukuran kinerja, kelima model tersebut dapat dikelompokkan menjadi dua, yaitu model dengan tingkat suku bunga, inflasi, dan PDB sebagai kelompok pertama, sedangkan model tanpa suku bunga dan tingkat zakat sebagai kelompok kedua

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.

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.

THE SEARCH FOR TIME-SERIES PREDICTABILITY-BASED ANOMALIES

This paper introduces a new algorithm for exploiting time-series predictability-based patterns to obtain an abnormal return, or alpha, with respect to a given benchmark asset pricing model. The algorithm proposes a deterministic daily market timing strategy that decides between being fully invested in a risky asset or in a risk-free asset, with the trading rule represented by a parametric perceptron. The optimal parameters are sought in-sample via differential evolution to directly maximize the alpha. Successively using two modern asset pricing models and two different portfolio weighting schemes, the algorithm was able to discover an undocumented anomaly in the United States stock market cross-section, both out-of-sample and using small transaction costs. The new algorithm represents a simple and flexible alternative to technical analysis and forecast-based trading rules, neither of which necessarily maximizes the alpha. This new algorithm was inspired by recent insights into representing reinforcement learning as evolutionary computation.