The question of whether stock market returns contain a predictable component has attracted much attention from both academics and market participants. As a result, numerous financial and macroeconomic variables have been employed to address the issue (see, inter alia, Rozeff, 1984; Campbell and Shiller, 1988; Fama and French, 1988; Hodrick, 1992; Lamont, 1998; Pontiff and Schall, 1998; Lettau and Ludvigson, 2001; Boudoukh et al., 2007; Kellard et al., 2010; Andriosopoulos et al., 2014; Jordan et al., 2014) while a plethora of approaches and testing procedures have emerged in the literature making the overall assessment an extremely difficult task (e.g., Goetzmann and Jorion, 1993; Nelson and Kim, 1993; Ferson et al., 2003; Inoue and Kilian, 2004; Amihud and Hurvich, 2004; Lewellen, 2004).1 However, although the empirical evidence on return predictability is mixed when we consider stock returns in the levels, there is a smaller body of literature which suggests that it is more likely to predict the signs of stock returns instead (see Breen et al., 1989; Pesaran and Timmermann, 2000, 1995; Christoffersen et al., 2007; Nyberg, 2011; Chevapatrakul, 2013). An important development in this area is the recent theoretical work by Christoffersen and Diebold (2006) which suggests that the sign of stock returns is predictable given that their volatilities are.2 The intuition behind this relationship is that any changes in volatility will alter the probability of observing negative or positive returns. More specifically, a rise in volatility increases the probability of a negative return, given that the expected returns are positive. Christoffersen and Diebold (2006) point out that sign predictability may exist even if there is no mean predictability, while it is also independent of the shape of the return distribution. Such sign predictability can be particularly useful for creating profitable investment strategies. Building on the work of Christoffersen and Diebold (2006), which provides a more concrete framework for sign predictability, we empirically investigate whether improved volatility forecasts, via a measure for information demand, can indeed lead to better sign forecasts of stock returns. Given that stock return predictability in the levels has been proven difficult to detect in the US context (see, Bossaerts and Hillion, 1999; Goyal and Welch, 2003, 2008; Kostakis et al., 2015), it is of particular interest to investigate whether the sign of US stock returns can be predicted instead. Additionally, we investigate whether such sign return predictions can be exploited to form profitable trading strategies from the perspective of investors. This is important for two reasons. First, statistical significance does not necessarily translate into economic gains for investors (Leitch and Tanner, 1991; Andriosopoulos et al., 2014) and second, apart from the theoretical contribution of Christoffersen and Diebold (2006), there is no empirical evidence regarding the economic value of sign predictability based on their model.