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In the IEEE Investment ranking challenge 2018, participants were asked tobuild a model which would identify the best performing stocks based on theirreturns over a forward six months window. Anonymized financial predictors andsemi-annual returns were provided for a group of anonymized stocks from 1996 to2017, which were divided into 42 non-overlapping six months period. The secondhalf of 2017 was used as an out-of-sample test of the model's performance.Metrics used were Spearman's Rank Correlation Coefficient and NormalizedDiscounted Cumulative Gain (NDCG) of the top 20% of a model's predictedrankings. The top six participants were invited to describe their approach. Thesolutions used were varied and were based on selecting a subset of data totrain, combination of deep and shallow neural networks, different boostingalgorithms, different models with different sets of features, linear supportvector machine, combination of convoltional neural network (CNN) and Long shortterm memory (LSTM).

Investment Ranking Challenge: Identifying the best performing stocks based on their semi-annual returns