What is he best known for?
The fields of study he is best known for:
- Statistics
- Finance
- Econometrics
The scientist’s investigation covers issues in Econometrics, Predictability, Cointegration, Macroeconomics and Exchange rate.
In general Econometrics study, his work on Volatility and Out of sample often relates to the realm of Predictive power, thereby connecting several areas of interest.
He has included themes like Predictive regression and Real economy in his Volatility study.
His Predictability study combines topics from a wide range of disciplines, such as Financial economics, Stock return, Model selection and Business statistics.
His work carried out in the field of Cointegration brings together such families of science as Homogeneity, Us dollar and Keynesian economics.
The various areas that David E. Rapach examines in his Exchange rate study include Volatility swap and Volatility smile.
His most cited work include:
- Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy (668 citations)
- Forecasting the Equity Risk Premium: The Role of Technical Indicators (326 citations)
- International Stock Return Predictability: What Is the Role of the United States? (286 citations)
What are the main themes of his work throughout his whole career to date?
David E. Rapach mainly focuses on Econometrics, Predictability, Financial economics, Business cycle and Portfolio.
David E. Rapach connects Econometrics with Predictive power in his study.
His studies in Financial economics integrate themes in fields like Equity, Equity risk, Short interest ratio, Bond and Granger causality.
His Business cycle research includes themes of Dynamic factor, Equity premium puzzle and Recession.
His Equity premium puzzle course of study focuses on Out of sample and Real economy.
His research on Portfolio also deals with topics like
- Artificial intelligence which intersects with area such as Systematic risk,
- Machine learning which intersects with area such as Excess return, Stock return, Exchange rate and Big data.
He most often published in these fields:
- Econometrics (77.23%)
- Predictability (32.67%)
- Financial economics (30.69%)
What were the highlights of his more recent work (between 2017-2021)?
- Econometrics (77.23%)
- Portfolio (23.76%)
- Machine learning (14.85%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Econometrics, Portfolio, Machine learning, Artificial intelligence and Predictability.
David E. Rapach frequently studies issues relating to Relevance and Econometrics.
He works mostly in the field of Portfolio, limiting it down to concerns involving Risk premium and, occasionally, Stochastic volatility, Gibbs sampling, Volatility, Dynamic stochastic general equilibrium and Bayesian probability.
His Machine learning research incorporates themes from Excess return and Big data.
His studies examine the connections between Predictability and genetics, as well as such issues in Inference, with regards to Factor analysis.
His biological study spans a wide range of topics, including Exchange rate, Deep learning and Stock return.
Between 2017 and 2021, his most popular works were:
- Industry Return Predictability: A Machine Learning Approach (14 citations)
- Firm Characteristics and Expected Stock Returns (6 citations)
- Firm Characteristics and Expected Stock Returns (6 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Finance
- Macroeconomics
David E. Rapach spends much of his time researching Econometrics, Machine learning, Artificial intelligence, Statistic and Forecast error.
His Econometrics research includes elements of High dimensional and Relevance.
His Machine learning research integrates issues from Stock return, Excess return and Commodity.
His Artificial intelligence research is multidisciplinary, relying on both Big data, Risk premium and Portfolio.
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