Stock Ranking Prediction Using List-Wise Approach and Node Embedding Technique

Traditional stock movement prediction tasks are formulated as either classification or regression task, and the relation between stocks are not considered as an input of prediction. The relative order or ranking of stocks is more important than the price or return of a single stock for making proper investment decisions. Stock ranking performance can be improved by incorporating the stock relation information in the prediction task. We employ a graph-based approach for stock ranking prediction and use the stock relation information as the input of the machine learning model. Investors might be interested in the prediction performance of top- k stocks as they would be more profitable than the others. Thus, the performance measure for stock ranking prediction should be top-weighted and bounded for any value of k. Existing evaluation measures lack these properties, and we propose a new measure named normalized rank biased overlap for top- k ( NRBO@k) stocks for stock ranking prediction. NRBO@k-based investment strategy generates 0.281% to 4.928% higher relative investment gain than the topmost stock-based strategy. We show that the list-wise loss function can improve the stock ranking performance significantly in a graph-based approach. It generates better NRBO@10 than the combination of point-wise and pair-wise loss in three out of four cases. Node embedding techniques such as Node2Vec can reduce the training time of graph-based approaches for stock ranking prediction significantly. Additionally, we improve the prediction performance through hyperparameter tuning of Node2Vec when a sparse stock relation graph is applied.