Low-risk investment strategy: sector bets or stock bets?

PurposeThe portfolio of low-risk stocks outperforms the portfolio of high-risk stocks and market portfolios on a risk-adjusted basis. This phenomenon is called the low-risk effect. There are several economic and behavioral explanations for the existence and persistence of such an effect. However, it is still unclear whether specific sector orientation drives the low-risk effect. The study seeks to answer the following important questions in Indian equity markets: (a) Whether sector bets or stock bets mainly drive the low-risk effect? (b) Is it a mere proxy for the well-known value effect? (c) Does the low-risk effect prevail in long-only portfolios?Design/methodology/approachThe study is based on all the listed stocks on the National Stock Exchange (NSE) of India from December 1994 to September 2018. It classifies them into 11 Global Industry Classification Standard (GICS) sectors to construct stock-level and sector-level BAB (Betting Against Beta) and long-only low-risk portfolios. It follows the study of Asness et al. (2014) to construct various BAB portfolios. It applies Fama–French (FF) three-factor and Fama–French–Carhart (FFC) four-factor asset pricing models in addition to Capital Asset Pricing Model (CAPM) to examine the strength of BAB, sector-level BAB, stock-level BAB and long-only low-beta portfolios.FindingsBoth sector- and stock-level bets contribute to the return of the low-risk investing strategy, but the stock-level effect is dominant. Only betting on safe sectors or industries will not earn economically significant alpha. The low-risk effect is unique and not a value effect in disguise. Both long-short and long-only portfolios within sectors and industry groups deliver positive excess returns. Consumer staples, financial, materials and healthcare sectors mainly contribute to the returns of the low-risk effect in India. This study offers empirical evidence against the Samuelson (1998) micro-efficient market given the strong performance of the stock-level low-risk effect.Practical implicationsThe superior performance of the low-risk investment strategies at both stock and sector levels offers investors an opportunity to strategically invest in stocks from the right sectors and earn high risk-adjusted returns with lower drawdowns over an entire market cycle. Besides, it paves the way for stock exchanges and index manufacturers to launch sector-specific low-volatility indices for relevant sectors. Passive funds can launch index funds and exchange-traded funds by tracking these indices. Active fund managers can espouse sector-specific low-risk investment strategies based on the results of this and similar other studies.Originality/valueThe study is the first of its kind. It offers insights into the portfolio characteristics and performance of the long-short and the long-only variant of low-risk portfolios within sectors and industry groups. It decomposes the low-risk effect into sector-level and stock-level effects.

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.

Hierarchical Time-Varying Estimation of Asset Pricing Models

This paper presents a new hierarchical methodology for estimating multi factor dynamic asset pricing models. The approach is loosely based on the sequential Fama–MacBeth approach and developed in a kernel regression framework. However, the methodology uses a very flexible bandwidth selection method which is able to emphasize recent data and information to derive the most appropriate estimates of risk premia and factor loadings at each point in time. The choice of bandwidths and weighting schemes are achieved by a cross-validation procedure; this leads to consistent estimators of the risk premia and factor loadings. Additionally, an out-of-sample forecasting exercise indicates that the hierarchical method leads to a statistically significant improvement in forecast loss function measures, independently of the type of factor considered.

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