Low Beta Stock and Excess Return

2013 ◽  
Author(s):  
Raymond Wai Pong Yuen
Keyword(s):  
Excess Return

Magical banking capital: Neo-endogenous money (NEM)

2020 ◽  
Vol 1 (1) ◽  
pp. 27-35
Author(s):  
Sovanbrata Talukdar
Keyword(s):  
Stock Market ◽  
Central Bank ◽  
Financial Market ◽  
Positive Relationship ◽  
Excess Return ◽  
Control Individual ◽  
Financial Constraint ◽  
Imperfect Market ◽  
External Finance ◽  
Endogenous Money

This research emerges with internal financial constraint. How financial constraint may lead to economic recess or back. This financial constraint is different than external finance constraint, and is not due to lack of gold, etc. It explains the positive relationship between excess return in stock market (ERSM) and non-real funding or riskier credit. The matter comes under imperfect market banking. It includes subsequently banking behavior and failure of central bank policy to control individual banks under these circumstances. In addition, it presents measures to get awareness before default comes, as financial default rare and crisis in financial market comes much before that.

scholarly journals Perbandingan 3 Faktor dan 5 Faktor Asset Pricing Model

2019 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Ivan Gumilar Sambas Putra ◽  
Neneng Susanti
Keyword(s):  
Asset Pricing ◽  
Excess Return ◽  
Purposive Sampling ◽  
Asset Pricing Model ◽  
Simultaneous Test ◽  
Paired Samples ◽  
Asset Pricing Models ◽  
Five Factors ◽  
Fama And French ◽  
Research Criteria

AbstractIn the period 1993-2014 Fama and French elicited two models that can be used to calculate asset pricing, the two models are three factors and five factors asset pricing models. The purpose of this study is to compare the best models in predicting excess returns in LQ 45 companies for the period 2012-2016. The population used in this study is the company contained in the LQ 45 category during the period 2012-2016. Based on the sampling method used in this study was purposive sampling with the research criteria set by the researchers, the sample in this study amounted to as many as 20 companies. The research method used in this study is explanatory research methods. Based on tests conducted using statistical data processing applications, namely SPSS Version 24, it is known that five factors are better at explaining excess return compared to three factors when viewed from the simultaneous test and paired samples test that has been done.AbstrakDalam periode 1993-2014, Fama dan French memunculkan dua model yang dapat digunakan untuk menghitung harga aset, dua model tersebut adalah tiga faktor dan lima faktor model penetapan harga aset. Tujuan dari penelitian ini adalah untuk membandingkan model terbaik dalam memprediksi kelebihan pengembalian di perusahaan LQ 45 untuk periode 2012-2016. Populasi yang digunakan dalam penelitian ini adalah perusahaan yang terdapat dalam kategori LQ 45 selama periode 2012-2016. Berdasarkan metode pengambilan sampel yang digunakan dalam penelitian ini adalah purposive sampling dengan kriteria penelitian yang ditetapkan oleh peneliti, sampel dalam penelitian ini berjumlah sebanyak 20 perusahaan. Metode penelitian yang digunakan dalam penelitian ini adalah metode penelitian eksplanatori. Berdasarkan tes yang dilakukan menggunakan aplikasi pengolahan data statistik, yaitu SPSS Versi 24, diketahui bahwa lima faktor lebih baik dalam menjelaskan kelebihan pengembalian dibandingkan dengan tiga faktor jika dilihat dari uji simultan dan uji sampel berpasangan yang telah dilakukanKeywords: excess return; five factors; three factors. 

Sources of Excess Return and Implications for Active Fixed-Income Portfolio Construction

2019 ◽  
Vol 46 (2) ◽  
pp. 106-120 ◽  
Author(s):  
Stephen Laipply ◽  
Ananth Madhavan ◽  
Aleksander Sobczyk ◽  
Matthew Tucker
Keyword(s):  
Excess Return ◽  
Portfolio Construction ◽  
Fixed Income ◽  
Fixed Income Portfolio

Dynamic conditional betas and equity returns

2020 ◽  
Vol 07 (04) ◽  
pp. 2050035
Author(s):  
Salvatore Joseph Terregrossa ◽  
Veysel Eraslan
Keyword(s):  
Cross Product ◽  
Equity Returns ◽  
Excess Return ◽  
Cross Sectional ◽  
New Approach ◽  
Product Term ◽  
Conditional Beta ◽  
Conditional Betas ◽  
Empirical Methodology ◽  
Cross Product Term

Our study makes use of a new approach to estimate time-varyingbetas with an application of the corrected Dynamic Conditional Correlation (cDCC) model. Our empirical methodology encompasses an examination of predictive relations between equity return and different specifications of dynamic conditional beta, using cross-sectional regression analysis at both the portfolio and firm levels. Our main finding is a significant, positive relation between equity excess return and an interactive cross product term of dynamic conditional beta and market excess return ([Formula: see text]); suggesting that equity return is largely determined by an interaction effect between dynamic beta and market return.

scholarly journals KiwiSaver fund performance and asset allocation policy

2019 ◽  
Vol 31 (2) ◽  
pp. 232-257
Author(s):  
Huong Dieu Dang
Keyword(s):  
Asset Allocation ◽  
Tracking Error ◽  
Excess Return ◽  
Superior Performance ◽  
Data Set ◽  
Content Type ◽  
Allocation Policy ◽  
Holding Period ◽  
Management Fees ◽  
The Impact

Purpose This paper aims to examine the performance and benchmark asset allocation policy of 70 KiwiSaver funds catergorised as growth, balanced or conservative over the period October 2007-June 2016. The study focuses on the sources for returns variability across time and returns variation among funds. Design/methodology/approach Each fund is benchmarked against a portfolio of eight indices representing eight invested asset classes. Three measures were used to examine the after-fee benchmark-adjusted performance of each fund: excess return, cumulative abnormal return and holding period returns difference. Tracking error and active share were used to capture manager’s benchmark deviation. Findings On average, funds underperform their respective benchmarks, with the mean quarterly excess return (after management fees) of −0.15 per cent (growth), −0.63 per cent (balanced) and −0.83 per cent (conservative). Benchmark returns variability, on average, explains 43-78 per cent of fund’s across-time returns variability, and this is primarily driven by fund’s exposures to global capital markets. Differences in benchmark policies, on average, account for 18.8-39.3 per cent of among-fund returns variation, while differences in fees and security selection may explain the rest. About 61 per cent of balanced and 47 per cent of Growth funds’ managers make selection bets against their benchmarks. There is no consistent evidence that more actively managed funds deliver higher after-fee risk-adjusted performance. Superior performance is often due to randomness. Originality/value This study makes use of a unique data set gathered directly from KiwiSaver managers and captures the long-term strategic asset allocation target which underlines the investment management process in reality. The study represents the first attempt to examine the impact of benchmark asset allocation policy on KiwiSaver fund’s returns variability across time and returns variation among funds.

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