scholarly journals An Examination of the Benefits of Factor Investing in U.K. Stock Returns

2018 ◽  
Vol 10 (4) ◽  
pp. 154
Author(s):  
Jonathan Fletcher
Keyword(s):  
Stock Returns ◽  
Bayesian Approach ◽  
Short Selling ◽  
Market Frictions ◽  
Trading Costs ◽  
Portfolio Constraints ◽  
Market Index ◽  
Factor Investing ◽  
Significant Performance ◽  
The Bayesian Approach

This study uses the Bayesian approach of Wang (1998) to examine the benefits of factor investing in U.K. stock returns in the presence of market frictions. My study finds that factor investing provides significant performance benefits when the benchmark investment universe is the market index, even in the presence of market frictions such as portfolio constraints and trading costs. However when the benchmark investment universe includes industry portfolios, market frictions, such as no short selling constraints and trading costs, tends to eliminate the benefits of factor investing. Imposing less restrictive portfolio constraints, factor investing can generate significant performance for investors with higher risk aversion levels.

scholarly journals The Bayesian Approach and the Results of the ISAR-REACT 5 Trial

2021 ◽  
Vol 14 (2) ◽  
pp. 231-232
Author(s):  
Adnan Kastrati ◽  
Alexander Hapfelmeier
Keyword(s):  
Bayesian Approach ◽  
The Bayesian Approach

A new Bayesian approach for QTL mapping of family data

2021 ◽  
Author(s):  
Daiane Aparecida Zuanetti ◽  
Luis Aparecido Milan
Keyword(s):  
Qtl Mapping ◽  
Random Effects ◽  
Bayesian Approach ◽  
Variance Component ◽  
Mendelian Inheritance ◽  
Family Data ◽  
Data Sets ◽  
Mendelian Segregation ◽  
Gaw17 Data ◽  
The Bayesian Approach

In this paper, we propose a new Bayesian approach for QTL mapping of family data. The main purpose is to model a phenotype as a function of QTLs’ effects. The model considers the detailed familiar dependence and it does not rely on random effects. It combines the probability for Mendelian inheritance of parents’ genotype and the correlation between flanking markers and QTLs. This is an advance when compared with models which use only Mendelian segregation or only the correlation between markers and QTLs to estimate transmission probabilities. We use the Bayesian approach to estimate the number of QTLs, their location and the additive and dominance effects. We compare the performance of the proposed method with variance component and LASSO models using simulated and GAW17 data sets. Under tested conditions, the proposed method outperforms other methods in aspects such as estimating the number of QTLs, the accuracy of the QTLs’ position and the estimate of their effects. The results of the application of the proposed method to data sets exceeded all of our expectations.

On the ability of New Zealand actively managed funds to generate outperformance in their domestic equity allocations

2018 ◽  
Vol 30 (4) ◽  
pp. 463-481 ◽  
Author(s):  
Bart Frijns ◽  
Ivan Indriawan
Keyword(s):  
New Zealand ◽  
Stock Returns ◽  
Tracking Error ◽  
Tracking Errors ◽  
Content Type ◽  
Portfolio Holdings ◽  
Market Index ◽  
Stock Picking ◽  
Fund Size ◽  
Managed Funds

Purpose This paper aims to assess the ability of New Zealand (NZ) actively managed funds to generate risk-adjusted outperformance using portfolio holdings data. Focusing on domestic equity allocations addresses the benchmark selection issue, particularly for funds with national and international exposures. Design/methodology/approach The authors assess performance using several asset pricing models including the CAPM, three-factor and four-factor models. The authors also assess performance across funds with different characteristics such as fund size, size of local holdings, type of fund provider, past returns and fees. The authors further examine whether funds engage in any stock-picking or market timing by considering the active share and tracking error. Findings The returns on NZ equity holdings of NZ actively managed funds from 2010 to 2017 provide little evidence of risk-adjusted outperformance and stock-picking skill. These exposures yield pre-cost returns that have a nearly perfect correlation with the market index and an insignificant alpha. Funds show little tendency to bet on any of the main characteristics known to predict stock returns, such as size, book-to-market and momentum. In addition, the authors show that the average active shares and tracking errors are low, suggesting that the majority of funds hold NZ equity portfolios that closely mimic the market index. Originality/value Existing studies rely on returns data which aggregate performance across all asset classes with varying exposures. This may lead to benchmark selection issues (particularly for funds with international exposures) which may obscure the fund manager’s true stock-picking skills. Assessment using holdings data would enable suitable performance measurement by researchers and industry analysts.

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