ETF Portfolio Construction via Neural Network trained on Financial Statement Data

TL;DR

This paper introduces a neural network-based method for constructing ETF portfolios using financial statement data, addressing data scarcity issues by training on individual stocks to improve ETF management, especially for new ETFs.

Contribution

The paper presents a novel neural network approach that leverages stock data to construct ETF portfolios, overcoming limited ETF historical data for machine learning applications.

Findings

Proposed method outperforms baseline models in experiments.

Effective for managing newly listed ETFs with limited historical data.

Neural networks trained on stock data can predict ETF performance successfully.

Abstract

Recently, the application of advanced machine learning methods for asset management has become one of the most intriguing topics. Unfortunately, the application of these methods, such as deep neural networks, is difficult due to the data shortage problem. To address this issue, we propose a novel approach using neural networks to construct a portfolio of exchange traded funds (ETFs) based on the financial statement data of their components. Although a number of ETFs and ETF-managed portfolios have emerged in the past few decades, the ability to apply neural networks to manage ETF portfolios is limited since the number and historical existence of ETFs are relatively smaller and shorter, respectively, than those of individual stocks. Therefore, we use the data of individual stocks to train our neural networks to predict the future performance of individual stocks and use these predictions and the portfolio deposit file (PDF) to construct a portfolio of ETFs. Multiple experiments have been performed, and we have found that our proposed method outperforms the baselines. We believe that our approach can be more beneficial when managing recently listed ETFs, such as thematic ETFs, of which there is relatively limited historical data for training advanced machine learning methods.