TL;DR
This paper develops a neural network model using a large dataset of financial features to predict stocks that are good investments, aiming to assist investors in decision-making.
Contribution
It introduces a novel application of Self-Organizing Maps trained on over a million data points for stock investment prediction.
Findings
High accuracy in identifying good investments
Effective use of diverse financial features
Scalability to large datasets
Abstract
Given financial data from popular sites like Yahoo and the London Exchange, the presented paper attempts to model and predict stocks that can be considered "good investments". Stocks are characterized by 125 features ranging from gross domestic product to EDIBTA, and are labeled by discrepancies between stock and market price returns. An artificial neural network (Self-Organizing Map) is fitted to train on more than a million data points to predict "good investments" given testing stocks from 2013 and after.
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Taxonomy
TopicsStock Market Forecasting Methods