- Research Article
18
- 10.1016/j.jeconom.2015.02.040
The long and the short of the risk-return trade-off
- Mar 12, 2015
- Journal of Econometrics
- Marco Bonomo + 3 more +3
The long and the short of the risk-return trade-off
Revisiting Dividend Yield Dynamics and Returns Predictability: Evidence from a Time-Varying ESTR Model
The long and the short of the risk-return trade-off
The long and the short of the risk-return trade-off
Pockets of Predictability
Pockets of Predictability
Predictability of Stock Market Excess Returns With Household’s Obligation Ratio
In this study, I test the predictability of stock market excess returns with households’ obligations ratio. Using U.S stock market data, I show that household’s debt service ratio can predict stock market returns at short horizon and over business cycle frequencies. I show that between 1980 and 2016, mean deviations from debt service ratio is a better forecaster of future returns both in-sample and out-of-sample than dividend-price ratio, dividend yield, earnings-price ratio, investment-capital ratio, and several other popular forecasting variables. The results remain significant using quarterly data and annual data.
Read moreForecasting stock returns in Saudi Arabia and Malaysia
PurposeThis paper aims to investigate the performance of various return forecasting variables and methods in Saudi Arabia and Malaysia. The authors document that market excess returns in Saudi Arabia are predicted by changes in oil prices, the dividend yield and inflation, whereas the equity premium in Malaysia is predicted only by the US market excess returns. In both countries, the authors find that the diffusion index is the best forecasting method and stock return predictability is stronger in expansions than in recessions. To interpret the findings, the authors perform two tests. The empirical results suggest irrational pricing in Malaysia and rationally time-varying expected returns in Saudi Arabia.Design/methodology/approachThe authors apply the state-of-the-art in-sample and out-of-sample forecasting techniques to predict stock returns in Saudi Arabia and Malaysia.FindingsThe Saudi equity premium is predicted by oil prices, dividend yield and inflation. The Malaysian equity premium is predicted by the US market excess returns. In both countries, the authors find that the diffusion index is the best forecasting method. In both countries, predictability is stronger in expansions than in recessions. The tests suggest irrational pricing in Malaysia and rationality in Saudi Arabia.Practical implicationsThe empirical results have some practical implications. The fact that stock returns are predictable in Saudi Arabia makes it possible for policymakers to better evaluate future business conditions, and thus to take appropriate decisions regarding economic and monetary policy. In Malaysia, the results of this study have interesting implications for portfolio management. The fact that the Malaysian market seems to be inefficient suggests the presence of strong opportunities for sophisticated investors, such as hedge and mutual funds.Originality/valueFirst, there are no papers that have studied the return predictability in Saudi Arabia in spite of its importance as an emerging market. Second, the methods that combine all predictive variables such as the diffusion index or the kitchen sink methods have not been implemented in emerging markets. Third, this paper is the first study to deal with time-varying short-horizon predictability in emerging countries.
Read moreEfficient Use of Conditioning Information: A Sharpe Ratio Based Test of Return Predictability
Efficient Use of Conditioning Information: A Sharpe Ratio Based Test of Return Predictability
The Conditional CAPM Explains the Value Premium
The Conditional CAPM Explains the Value Premium
Prediction of time-varying dynamics and chatter stability analysis for surface milling of thin-walled curved CFRP workpiece
Prediction of time-varying dynamics and chatter stability analysis for surface milling of thin-walled curved CFRP workpiece
Read more13-LB: A Prediction Model of Chronic Kidney Disease Progression among Individuals with Type 2 Diabetes in the United States
CKD progression among individuals with T2D is associated with poor outcomes and high costs. The risk factors for CKD progression in T2D have not been fully studied. This study aimed to predict CKD progression among individuals with T2D. Using the ACCORD clinical trial, a time-varying Cox model was developed to predict the risk of CKD progression among patients with CKD. CKD progression was defined as a 50% decline, or 25 mL/min/1.73 m2 decline in eGFR from baseline, doubling of the serum creatinine, or onset of ESKD. A list of candidate variables included demographic characteristics, physical exam, laboratory, medical history, drug use, and healthcare utilization. A stepwise algorithm was used for variable selection. Data was separated into training and validation set. Model performance was evaluated by Brier score (BS) and C statistics. Confidence intervals (CI) were calculated by bootstraping. Decomposition analysis was conducted to assess the predictor contribution. Generalizability was assessed on patient-level data of the HARMONY Outcome clinical trial. A total of 6,982 T2D patients with CKD were used for model development, with a median follow-up of 4 years and 3,346 CKD progression events. The predictors for CKD progression included female sex, age at T2D diagnosis, smoking status, BP, HR, HbA1c, ALT, eGFR, UACR, retinopathy event, hospitalization, interaction of SBP and smoking, and SBP and ALT. The model demonstrated good discrimination (C-statistics 0.745 [95% CI 0.723-0.763]) and calibration (BS 0.0923 [95% CI 0.0873-0.0965]) . The most contributing predictors for CKD progression were eGFR, HbA1c, and SBP. There were 2,954 patients with CKD extracted from HARMONY with 640 CKD progression events and median follow-up of 2 years. The model demonstrated acceptable discrimination (C-statistics: 0.742 [95% CI 0.721-0.7645]) and calibration (BS: 0.0988 [95% CI 0.0952-0.1022]) in the external data. For high-risk patients with both diabetes and CKD, the tool as a dynamic risk prediction of CKD progression may help develop novel strategies to lower the risk of CKD progression. Disclosure Y. Lin: None. H. Shao: Board Member; BRAVO4HEALTH, LLC. A. H. Anderson: None. V. Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. V. Batuman: None. L. Shi: None.
Read morePrices and Returns: What Is the Role of Inflation?
Prices and Returns: What Is the Role of Inflation?
Stock price movements: Evidence from global equity markets
Stock price movements: Evidence from global equity markets
When does investor sentiment predict stock returns?
When does investor sentiment predict stock returns?
Importance of skewness in decision making: Evidence from the Indian stock exchange
Importance of skewness in decision making: Evidence from the Indian stock exchange
Time varying stock return predictability: Evidence from US sectors
Time varying stock return predictability: Evidence from US sectors
Value Timing: Risk and Return Across Asset Classes
Value Timing: Risk and Return Across Asset Classes
Time-varying short-horizon predictability
Time-varying short-horizon predictability