Cross-Section of Expected Returns Based on Equity Duration

2019 ◽  
Vol 27 (3) ◽  
pp. 297-327
Author(s):  
Sungjeh Moon ◽  
Joonhyuk Song
Keyword(s):  
Cross Section ◽  
Risk Premium ◽  
Factor Model ◽  
Explanatory Power ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Expected Returns ◽  
Expected Return ◽  
Cross Sectional ◽  
Risk Premiums

We analyze the cross-sectional expected return of KOSPI stocks using equity duration. From 1991 to 2018, we calculate equity durations for the KOSPI listed stocks (including de-listed stocks) and find that the shorter the equity duration, the higher the risk premium. Using the 4-factor model with equity duration added to the benchmark 3-factor model, the explanatory power of the 4-factor model is superior to that of the existing benchmark model in accounting for risk premiums. This is an unusual finding that is not readily explainable by the traditional CAPM or the Fama-French 3-factor model. This can be interpreted that the equity duration is a separate and significant risk factor dissociated from the HML of the 3-factor model.

The Cross Section of Expected Returns with MIDAS Betas

2011 ◽  
Vol 47 (1) ◽  
pp. 115-135 ◽  
Author(s):  
Mariano González ◽  
Juan Nave ◽  
Gonzalo Rubio
Keyword(s):  
Cross Section ◽  
Risk Premium ◽  
Market Risk ◽  
Ordinary Least Squares ◽  
Mixed Data ◽  
Expected Returns ◽  
Data Sampling ◽  
Cross Sectional ◽  
Market Risk Premium ◽  
The Cross

AbstractThis paper explores the cross-sectional variation of expected returns for a large cross section of industry and size/book-to-market portfolios. We employ mixed data sampling (MIDAS) to estimate a portfolio’s conditional beta with the market and with alternative risk factors and innovations to well-known macroeconomic variables. The market risk premium is positive and significant, and the result is robust to alternative asset pricing specifications and model misspecification. However, the traditional 2-pass ordinary least squares (OLS) cross-sectional regressions produce an estimate of the market risk premium that is negative, and significantly different from 0. Using alternative procedures, we compare both beta estimators. We conclude that beta estimates under MIDAS present lower mean absolute forecasting errors and generate better out-of-sample performance of the optimized portfolios relative to OLS betas.

scholarly journals Previsibilidade de Retorno das Ações na Bovespa: Um Teste Envolvendo o Modelo de Fator de Retorno Esperado

2004 ◽  
Vol 2 (2) ◽  
pp. 183
Author(s):  
Luciano Martin Rostagno ◽  
Gilberto De Oliveira Kloeckner ◽  
João Luiz Becker
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Volume ◽  
Factor Model ◽  
Explanatory Power ◽  
Return Predictability ◽  
Expected Return ◽  
Cross Sectional ◽  
Theoretical Assumptions ◽  
History Of

This paper examines the hypothesis of asst return predictability in the Brazilian Stock Market (Bovespa). Evidence suggests that seven factors explain most of the monthly differential returns of the stocks included in the sample. Within the factors that present statistically significant mean, two are liquidity factors (market capitalization and trading volume trend), three refer to price level of stocks (dividend to price, dividend to price trend, and cash flow to price), and two relate to price history of stocks (3 and 12 months excess return). Contradicting theoretical assumptions, risk factors present no explanatory power on cross-sectional returns. Using an expected return factor model, it is contended that stock returns are quite predictable. An investment simulation shows that the model is able to assemble portfolios with statistically significant higher returns. Additional tests indicate that the winner portfolios are not fundamentally riskier suggesting mispricing of assets in the Brazilian stock Market.

scholarly journals Cross-sectional Volatility and Stock Returns: Evidence for Emerging Markets

2016 ◽  
Vol 41 (3) ◽  
pp. 234-246 ◽  
Author(s):  
Sanjay Sehgal ◽  
Vidisha Garg
Keyword(s):  
South Africa ◽  
Asset Pricing ◽  
South Korea ◽  
Stock Returns ◽  
Risk Premium ◽  
Emerging Market ◽  
Significant Risk ◽  
Residual Variance ◽  
Cross Sectional ◽  
Risk Premiums

Executive Summary Cross-sectional volatility measures dispersion of security returns at a particular point of time. It has received very little focus in research. This article studies the cross-section of volatility in the context of economies of Brazil, Russia, India, Indonesia, China, South Korea, and South Africa (BRIICKS). The analysis is done in two parts. The first part deals with systematic volatility (SV), that is, cross-sectional variation of stock returns owing to their exposure to market volatility measure ( French, Schwert, & Stambaugh, 1987 ). The second part deals with unsystematic volatility (UV), measured by the residual variance of stocks in a given period by using error terms obtained from Fama–French model. The study finds that high SV portfolios exhibit low returns in case of Brazil, South Korea, and Russia. The risk premium is found to be statistically significantly negative for these countries. This finding is consistent with Ang et al. and is indicative of hedging motive of investors in these markets. Results for other sample countries are somewhat puzzling. No significant risk premiums are reported for India and China. Significantly positive risk premiums are observed for South Africa and Indonesia. Further, capital asset pricing model (CAPM) seems to be a poor descriptor of returns on systematic risk loading sorted portfolios while FF is able to explain returns on all portfolios except high SV loading portfolio (i.e., P1) in case of South Africa which seems to be an asset pricing anomaly. It is further observed that high UV portfolios exhibit high returns in all the sample countries except China. In the Chinese market, the estimated risk premium is statistically significantly negative. This negative risk premium is inconsistent with the theory that predicts that investors demand risk compensation for imperfect diversification. The remaining sample countries show significantly positive risk premium. CAPM does not seem to be a suitable descriptor for returns on UV sorted portfolios. The FF model does a better job but still fails to explain the returns on high UV sorted portfolio in case of Brazil and China and low UV sorted portfolio in South Africa. The findings are relevant for global fund managers who plan to develop emerging market strategies for asset allocation. The study contributes to portfolio management as well as market efficiency literature for emerging economies.

scholarly journals A Comparative Analysis of Four-Factor Model and Three-Factor Model in the Nigerian Stock Market

2017 ◽  
Vol 8 (4) ◽  
pp. 38
Author(s):  
Esther Ikavbo Evbayiro-Osagie ◽  
Ifuero Osad Osamwonyi
Keyword(s):  
Factor Model ◽  
Stock Exchange ◽  
Explanatory Power ◽  
Expected Returns ◽  
Momentum Effect ◽  
Cross Sectional ◽  
Operational Strategies ◽  
Portfolio Returns ◽  
Fund Portfolio ◽  
Three Factor

The study investigates if the three-factor model explains variation in expected returns of stocks on the Nigerian Stock Exchange (NSE); and also ascertains if the four-factor model explains the variation in expected returns of stocks on the NSE better than the three-factor model. The study use a sample size of 139 stocks with continuous trading on the NSE for the period January 2007 to December 2014 to construct 10 portfolios on the bases of size, value and returns. By means of multiple OLS regression analysis method with the aid of StataC13 software the analysis was done. The empirical analysis reveals that the three-factor model explains cross sectional variation in expected returns in the NSE. Also, the study shows that the size effect, value effect as well as momentum effect is present in the market. Comparing the four-factor model with three-factor model, shows that the four-factor model have better explanatory power than the three-factor model in explaining returns in the Market. It is recommended that equity investors, fund/portfolio managers and investment advisers should embed in their operational strategies the explanatory power of market beta, size and value as well as momentum on stock/portfolio returns to enable them build up trading strategies that minimize loss and maximize returns. Market regulators and policy makers should ensure appropriate measures are in place to improve market viability and liquidity in order to enhance the depth and breathe of the market.

scholarly journals Creative Destruction and Asset Prices

2016 ◽  
Vol 51 (6) ◽  
pp. 1739-1768 ◽  
Author(s):  
Joachim Grammig ◽  
Stephan Jank
Keyword(s):  
Asset Pricing ◽  
Factor Model ◽  
Creative Destruction ◽  
Expected Returns ◽  
Expected Return ◽  
Cross Sectional ◽  
Large Growth ◽  
Estimated Risk ◽  
Growth Stocks ◽  
Invention Activity

We relate Schumpeter’s notion of creative destruction to asset pricing, thereby offering a novel explanation of size and value premia. We argue that small-value firms must offer higher expected returns to compensate for the risk posed by serendipitous invention activity, whereas large-growth stocks provide protection against creative destruction and receive expected return discounts. A 2-factor model that accounts for creative-destruction risk effectively explains the cross-sectional return variation of size- and book-to-market-sorted portfolios. The estimated risk compensations associated with creative destruction are substantial and statistically significant, indicating their relevance for asset pricing.

scholarly journals Empirical Evidence of CAPM and Fama French 3 factor model at Cement Industry of DSE

2020 ◽  
pp. 1-6
Author(s):  
Avijit Mallik ◽  
Mrs. Syeda Mahrufa Bashar ◽  
Md. Sadid Uddin
Keyword(s):  
Empirical Evidence ◽  
Risk Premium ◽  
Factor Model ◽  
Stock Exchange ◽  
Explanatory Power ◽  
Cement Industry ◽  
Expected Return ◽  
Capm Model ◽  
Cement Manufacturing ◽  
Three Factor

The study discusses empirical evidence on the explanatory power for cement manufacturing industries of the Dhaka Stock Exchange in light of Capital Asset Pricing Model (CAPM) and the Fama French three-factor model. For calculating the market return, both DSEX and DS30 indexes have been used. The study revealed that the Fama French three-factor model has better explanatory power compared to the CAPM model in the Dhaka Stock Exchange. Moreover, the size risk premium has a significant influence in explaining the expected return for cement industries of the Dhaka Stock Exchange for both DSEX and DS30. On the other hand, the value risk premium has significant power in explaining the expected return for cement industries in the Dhaka Stock Exchange.

Tail Risk in the Cross Section of Alternative Risk Premium Strategies

2019 ◽  
Keyword(s):  
Cross Section ◽  
Risk Premium ◽  
Downside Risk ◽  
Empirical Literature ◽  
Direct Result ◽  
Risk Capital ◽  
Cross Sectional ◽  
Risk Premiums ◽  
Out Of Sample ◽  
The Cross

In this article, the authors attempt to get a better understanding of the cross-section of alternative risk premiums using a multi-asset version of the downside risk capital asset pricing model (CAPM). In line with the empirical literature, they find that the cross-section of realized returns is much better explained when using the downside CAPM, rather than relying on the traditional CAPM. However, in contrast to the empirical literature, the authors cannot always recover the required signs in their cross-sectional regressions. In particular, they find that taking on downside risk is not always systematically rewarded. This might be due to the limited availability of time series that essentially overweight the exceptional events of 2008 or a direct result of creating backtests with attractive in-sample features that are impossible to repeat out-of-sample.

scholarly journals An Augmented q-Factor Model with Expected Growth*

2020 ◽  
Author(s):  
Kewei Hou ◽  
Haitao Mo ◽  
Chen Xue ◽  
Lu Zhang
Keyword(s):  
Growth Factor ◽  
Factor Model ◽  
Explanatory Power ◽  
Cash Flows ◽  
Expected Returns ◽  
Q Factor ◽  
Cross Sectional ◽  
Return On Equity ◽  
Investment Theory ◽  
Investment Growth

Abstract In the investment theory, firms with high expected investment growth earn higher expected returns than firms with low expected investment growth, holding investment and expected profitability constant. Building on cross-sectional growth forecasts with Tobin’s q, operating cash flows, and change in return on equity as predictors, an expected growth factor earns an average premium of 0.84% per month (t = 10.27) in the 1967–2018 sample. The q5 model, which augments the Hou–Xue–Zhang (2015, Rev. Finan. Stud., 28, 650–705) q-factor model with the expected growth factor, shows strong explanatory power in the cross-section and outperforms the Fama–French (2018, J. Finan. Econom., 128, 234–252) six-factor model.

Cross-Sectional PEG Ratios, Market Equity Premium, and Macroeconomic Activity

2018 ◽  
Vol 35 (3) ◽  
pp. 471-500
Author(s):  
Xiaoquan Jiang ◽  
Qiang Kang
Keyword(s):  
Cross Section ◽  
Expected Returns ◽  
Future Market ◽  
Cross Sectional ◽  
Risk Premiums ◽  
Econometric Methods ◽  
Out Of Sample ◽  
Macroeconomic Activity ◽  
Equity Premiums ◽  
Valuation Ratios

This article explores the information content of PEG ratios (price/earnings to growth ratios) for future aggregate returns and economic fundamentals. We first establish an analytic link between PEG ratios and time-varying expected returns of stocks. We then combine the link with empirical asset pricing models to extract market-wide information from cross-sectional PEG ratios. The resultant cross-section estimates of the risk premiums on stock betas serve as proxies for market-wide information. The proxies contain salient information about future market equity premiums and macroeconomic activity both in-sample and out-of-sample. Moreover, the proxies outperform aggregate PEG ratios and the cross-section beta-premium estimate based on conventional valuation ratios and retain incremental power in forecasting future market equity premiums. The results are robust to using various econometric methods for standard error adjustments.

scholarly journals Predictability and the Cross-Section of Expected Returns: A Challenge for Asset Pricing Models

2021 ◽  
Author(s):  
Christian Schlag ◽  
Michael Semenischev ◽  
Julian Thimme
Keyword(s):  
Cross Section ◽  
Risk Premium ◽  
Empirical Test ◽  
Expected Returns ◽  
State Variables ◽  
Expected Return ◽  
Long Run ◽  
The Cross ◽  
Long Run Risks ◽  
Macro Finance

Many modern macro finance models imply that excess returns on arbitrary assets are predictable via the price-dividend ratio and the variance risk premium of the aggregate stock market. We propose a simple empirical test for the ability of such a model to explain the cross-section of expected returns by sorting stocks based on the sensitivity of expected returns to these quantities. Models with only one uncertainty-related state variable, like the habit model or the long-run risks model, cannot pass this test. However, even extensions with more state variables mostly fail. We derive conditions under which models would be able to produce expected return patterns in line with the data and discuss various examples. This paper was accepted by David Simchi-Levi, finance.

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