  This paper presents a portfolio construction process, including mainly two
parts, Factors Selection and Weight Allocations. For the factors selection
part, We have chosen 20 factors by considering three aspects, the global
market, different assets class, and stock idiosyncratic characteristics. Each
factor is proxied by a corresponding ETF. Then, we would apply several weight
allocation methods to those factors, including two fixed weight allocation
methods, three optimisation methods, and a Black-Litterman model. In addition,
we would also fit a Deep Learning model for generating views periodically and
incorporating views with the prior to achieve dynamically updated weights by
using the Black-Litterman model. In the end, the robustness checking shows how
weights change with respect to time evolving and variance increasing. Results
using shrinkage variance are provided to alleviate the impacts of
representativeness of historical data, but there sadly has little impact.
Overall, the model by using the Deep Learning plus Black-Litterman model
results outperform the portfolio by other weight allocation schemes, even
though further improvement and robustness checking should be performed.
