# Absolute Value Constraint: The Reason for Invalid Performance Evaluation Results of Neural Network Models for Stock Price Prediction

arXiv: https://arxiv.org/abs/2101.10942 · HTML: https://mlsanity.com/paper/2101.10942/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 10 Jan 2021.

## Builds on (most cited first, 11 of 11)

- [Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model](https://mlsanity.com/paper/1511.01644/) (2015)
- [Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?](https://mlsanity.com/paper/1606.03556/) (2016)
- [AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling](https://mlsanity.com/paper/1807.00154/) (2018)
- [Distilling Knowledge from Deep Networks with Applications to Healthcare Domain](https://mlsanity.com/paper/1512.03542/) (2015)
- [Predicting the direction of stock market prices using random forest](https://mlsanity.com/paper/1605.00003/) (2016)
- [Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models](https://mlsanity.com/paper/1606.05320/) (2016)
- [Interpreting extracted rules from ensemble of trees: Application to computer-aided diagnosis of breast MRI](https://mlsanity.com/paper/1606.08288/) (2016)
- [Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and Quantification](https://mlsanity.com/paper/1603.08704/) (2016)
- [Learning Interpretable Musical Compositional Rules and Traces](https://mlsanity.com/paper/1606.05572/) (2016)
- [Toward Interpretable Topic Discovery via Anchored Correlation Explanation](https://mlsanity.com/paper/1606.07043/) (2016)
- [Interpretable Machine Learning Models for the Digital Clock Drawing Test](https://mlsanity.com/paper/1606.07163/) (2016)

## Built on by (most cited first, 2 of 2)

- [Dynamical prediction of two meteorological factors using the deep neural network and the long short term memory (1)](https://mlsanity.com/paper/2101.09356/) (2021)
- [Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory (2)](https://mlsanity.com/paper/2104.14406/) (2021)

## Abstract (arXiv)

Neural networks for stock price prediction(NNSPP) have been popular for decades. However, most of its study results remain in the research paper and cannot truly play a role in the securities market. One of the main reasons leading to this situation is that the prediction error(PE) based evaluation results have statistical flaws. Its prediction results cannot represent the most critical financial direction attributes. So it cannot provide investors with convincing, interpretable, and consistent model performance evaluation results for practical applications in the securities market. To illustrate, we have used data selected from 20 stock datasets over six years from the Shanghai and Shenzhen stock market in China, and 20 stock datasets from NASDAQ and NYSE in the USA. We implement six shallow and deep neural networks to predict stock prices and use four prediction error measures for evaluation. The results show that the prediction error value only partially reflects the model accuracy of the stock price prediction, and cannot reflect the change in the direction of the model predicted stock price. This characteristic determines that PE is not suitable as an evaluation indicator of NNSPP. Otherwise, it will bring huge potential risks to investors. Therefore, this paper establishes an experiment platform to confirm that the PE method is not suitable for the NNSPP evaluation, and provides a theoretical basis for the necessity of creating a new NNSPP evaluation method in the future.