  In the IEEE Investment ranking challenge 2018, participants were asked to
build a model which would identify the best performing stocks based on their
returns over a forward six months window. Anonymized financial predictors and
semi-annual returns were provided for a group of anonymized stocks from 1996 to
2017, which were divided into 42 non-overlapping six months period. The second
half of 2017 was used as an out-of-sample test of the model's performance.
Metrics used were Spearman's Rank Correlation Coefficient and Normalized
Discounted Cumulative Gain (NDCG) of the top 20% of a model's predicted
rankings. The top six participants were invited to describe their approach. The
solutions used were varied and were based on selecting a subset of data to
train, combination of deep and shallow neural networks, different boosting
algorithms, different models with different sets of features, linear support
vector machine, combination of convoltional neural network (CNN) and Long short
term memory (LSTM).
