This study developed an investment framework to implement dynamic factor rotation strategies according to changes in economic conditions. I constructed a useful macro indicator that tracked real-time business cycles of the US economy and applied a trend-filtering method to the indicator to...

The study examined high volatile assets, specifically the currency exchange rate of the open financial market. Takes into consideration the five most traded paired currencies of the global financial market. And observed, generally, the data set of the unit currency exchange rate exhibit...

We investigate the cross-sectional predictability of stock returns after controlling for systematic risk factors in Taiwan. We additionally control for GDP growth, industrial production growth and inflation rate because they have a significant and negative pricing premium across stock returns...

Recent research suggests that machine learning models dominate traditional linear models in predicting cross-sectional stock returns. We confirm this finding when predicting one-month forward-looking returns based on a set of common stock characteristics, including predictors such as short-term...

This paper introduces a new out-of-sample forecasting methodology for monthly market returns using the variance risk premium (VRP) that is both statistically and economically significant. This methodology is motivated by the `beta representation,' which implies that the market risk premium is...

This paper considers how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of different vector autoregressive models, the investor is able, each period, to revise past predictive mistakes and learn about...

Long histories of returns are needed but often lacking when estimating the equity premium. This paper studies stock return predictability from the perspective of a Bayesian investor who has access to international data. Learning across countries arises whenever this investor believes that...

The risk premium of stocks due to priced variance risk is summarized to two variables -- the stock-specific price of variance risk (the difference between realized and option-implied variance) and the quantity (i.e., how stock prices respond to their variance shocks) of variance risk....

We construct mean-variance optimized currency portfolios and analyze the time- series variation of the conditional Sharpe ratio. Returns, volatility and skewness are predictable. Market timing – i.e., trading more (less) aggressively when the conditional risk-return trade-off is more (less)...

We document significant persistence in the market timing performance of active individual investors, suggesting that some investors are skilled at timing. Using data on all trades by active Finnish individual investors over almost 15 years, we also show that the net purchases of skilled versus...