  Loss reserving generally focuses on identifying a single model that can
generate superior predictive performance. However, different loss reserving
models specialise in capturing different aspects of loss data. This is
recognised in practice in the sense that results from different models are
often considered, and sometimes combined. For instance, actuaries may take a
weighted average of the prediction outcomes from various loss reserving models,
often based on subjective assessments.
  In this paper, we propose a systematic framework to objectively combine (i.e.
ensemble) multiple _stochastic_ loss reserving models such that the strengths
offered by different models can be utilised effectively. Our framework contains
two main innovations compared to existing literature and practice. Firstly, our
criteria model combination considers the full distributional properties of the
ensemble and not just the central estimate - which is of particular importance
in the reserving context. Secondly, our framework is that it is tailored for
the features inherent to reserving data. These include, for instance, accident,
development, calendar, and claim maturity effects. Crucially, the relative
importance and scarcity of data across accident periods renders the problem
distinct from the traditional ensembling techniques in statistical learning.
  Our framework is illustrated with a complex synthetic dataset. In the
results, the optimised ensemble outperforms both (i) traditional model
selection strategies, and (ii) an equally weighted ensemble. In particular, the
improvement occurs not only with central estimates but also relevant quantiles,
such as the 75th percentile of reserves (typically of interest to both insurers
and regulators). The framework developed in this paper can be implemented
thanks to an R package, `ADLP`, which is available from CRAN.
