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

Abstract

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 (N RBO@k) stocks for stock ranking prediction. N RBO@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 graphbased approach. It generates better N RBO@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.

Quantitative study of random walk parameters in node2vec model

“…In social networks node2vec is used to learn informative node representations for clustering [38] and personalized recommendation [39]. In financial markets, node2vec is used to recommend financial news [40], internet financial fraud detection [41], stock ranking prediction [42], etc. In biological networks, node2vec is used to obtain a dense vector representation of each protein and predict the function of proteins in PPI network [43,44], detect relationships involved in biological pathways [45].…”

Stock Market Predictions through Deep Belief Neural Network

“…The authors conducted a comparative analysis between ML-GAT and established production models that have been developed using publicly available datasets as standard benchmarks, in order to assess the effectiveness of ML-GAT. Suman Saha's [2] graph-based algorithm, optimum loss function, recommended unique performance assessment metric, and network encapsulation approach have facilitated the prediction of top stocks. The encapsulation technique employed by a network is contingent upon the individual components therein.…”