# Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities

arXiv: https://arxiv.org/abs/2107.03926 · HTML: https://mlsanity.com/paper/2107.03926/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 45 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Deep Learning for Financial Applications : A Survey](https://mlsanity.com/paper/2002.05786/) (2020)
- [Explainable Text-Driven Neural Network for Stock Prediction](https://mlsanity.com/paper/1902.04994/) (2019)

## Built on by (most cited first, 3 of 3)

- [Stock Embeddings: Learning Distributed Representations for Financial Assets](https://mlsanity.com/paper/2202.08968/) (2022)
- [Industry Classification Using a Novel Financial Time-Series Case Representation](https://mlsanity.com/paper/2305.00245/) (2023)
- [Contrastive Learning of Asset Embeddings from Financial Time Series](https://mlsanity.com/paper/2407.18645/) (2024)

## Abstract (arXiv)

Forecasting stock returns is a challenging problem due to the highly stochastic nature of the market and the vast array of factors and events that can influence trading volume and prices. Nevertheless it has proven to be an attractive target for machine learning research because of the potential for even modest levels of prediction accuracy to deliver significant benefits. In this paper, we describe a case-based reasoning approach to predicting stock market returns using only historical pricing data. We argue that one of the impediments for case-based stock prediction has been the lack of a suitable similarity metric when it comes to identifying similar pricing histories as the basis for a future prediction -- traditional Euclidean and correlation based approaches are not effective for a variety of reasons -- and in this regard, a key contribution of this work is the development of a novel similarity metric for comparing historical pricing data. We demonstrate the benefits of this metric and the case-based approach in a real-world application in comparison to a variety of conventional benchmarks.