Building on the results of Ludwig (2012), we propose a method to construct robust time-homogeneous Markov chains that capture the risk-neutral transition of state prices from current snapshots of option prices on the S&P 500 index. Using the recovery theorem of Ross (2013), we then derive the market's forecast of the real-world return density and investigate the predictive information content of its moments. We find that changes in the recovered moments can be used to time the index, yielding

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... rategies that not only outperform the market, but are also significantly less volatile. Some moments are nice, some are nicer, some are even worth writing about. -Charles Bukowski Recently, Ross (2013) has shown that the market's risk aversion, in the form of a transition independent pricing kernel, can be recovered from the risk-neutral transition matrix of a Markovian state variable. Knowledge of the pricing kernel allows us to obtain the market's subjective assessment of real-world probabilities from risk-neutral densities, which makes the information embedded in option prices directly accessible to applications such as risk management, portfolio optimization and the design of trading strategies. Ross' recovery theorem is intriguing because, in contrast to previous literature, it does neither rely on historical returns nor restrictions on the shape of * We acknowledge discussions with