We construct a momentum factor that identifies cross-sectional winners and losers based on a weighting scheme that incorporates all the price data, over the entire lookback period, as opposed to only the first and last price points of the window. The weighting scheme is derived from the...

There is limited evidence of intraday predictability both in the cross-section of US stock returns (see Heston et al., 2010) and in the time-series of the aggregate stock market (see Gao et al., 2015). I find that statistical time-series predictability does not imply economic profitability,...

The authors explore the risk-return properties of simple momentum strategies in six major government-bond markets and find that trend-following investment rules generate positive information ratios in the 1987-2011 sample period. They simulate the combination of momentum portfolios with...

This paper examines to what extent the momentum spread ratio (MSR) can predict momentum profits. The momentum spread ratio as a potential proxy of investor underreaction can significantly predict the momentum, industry momentum, and residual momentum, especially after 1994, suggesting that...

This paper challenges the prevailing view that investor sentiment is a contrarian predictor of market returns at nearly all horizons. As an important piece of "out-of-sample" evidence, we document that investor sentiment in China is a reliable momentum signal at monthly frequency. The strong...

The presence of time series momentum effect has been widely documented in the financial markets across asset classes and countries. We find a predictable pattern of the realized semi-variance to the future individual asset return, especially during the stressed states of time series momentum...

We show that conditional skewness and kurtosis of the momentum strategy are highly time-varying and sometimes take extreme values or may even not exist. The high negative skewness and high kurtosis arise since the winners' and losers' skewness moves in opposite directions, whereas the kurtosis...

We study dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. Our results show statistically and economically significant benefits from using deep learning to form optimal portfolios through certainty...

We introduce a flexible utility-based empirical approach to directly determine asset allocation decisions between risky and risk-free assets. This is in contrast to the commonly used two-step approach where least squares optimal statistical equity premium predictions are first constructed to...

Three concepts: stochastic discount factors, multi-beta pricing and mean-variance efficiency, are at the core of modern empirical asset pricing. This chapter reviews these paradigms and the relations among them, concentrating on conditional asset-pricing models where lagged variables serve as...