Stochastic linear programming is a suitable numerical approach for solving practical asset-liability management problems. In this paper, we consider a multi-stage setting under time-varying investment opportunities and propose a decomposition of the benefits in dynamic re-allocation and predictability effects. We use a first-order unrestricted vector autoregressive process to model asset returns and state variables and include, in addition to equity returns and dividend-price ratios, Nelson/Siegel parameters to account for the evolution of the yield curve. The objective is to minimize the Conditional Value at Risk of shareholder value, i.e., the difference between the mark-to-market value of (financial) assets and the present value of future liabilities.

We study the implications of predictability on the optimal asset allocation of ambiguity-averse long-term investors and analyze the term structure of the multivariate risk–return trade-off considering parameter uncertainty. We calibrate the model to real returns of U.S. stocks, long-term bonds, cash, real estate, and gold using the term spread and the dividend–price ratio as additional predictive variables, and we show that over long horizons, the optimal asset allocation is significantly influenced by the covariance structure induced by estimation errors. The ambiguity-averse long-term investor optimally tilts his or her portfolio toward a seemingly inefficient portfolio, which shows maximum robustness against estimation errors.

Betting quotes provide valuable information on market-implied probabilities for outcomes of events such as elections or referendums, which may have an impact on exchange rates. We generate exchange-rate forecasts around such events based on a model that combines risk-neutral event probabilities implied from betting quotes with risk-neutral exchange-rate densities extracted from currency option prices. Its application to predict exchange rates around the Brexit referendum and the U.S. presidential elections shows that these forecasts, conditional on the respective outcomes, were accurate, and markets were able to separate their views on the likelihood and the impact of these events.

We use data from betting markets to analyze the sensitivity of stock returns to potential outcomes of political events such as elections. By classifying stocks into expected conditional winners and losers prior to such an event, we form portfolios that generate large positive returns after the event date, conditional on correctly anticipating the outcome. The approach is illustrated using data from the 2016 US presidential election and the 2016 Brexit referendum. We show that these sensitivities contain information about event-related returns beyond that of firm characteristics whose predictive power has been documented in the literature.