The Evolution of Neural Network-Based Chart Patterns: A Preliminary Study
This addresses a specific problem in financial analysis for stock market prediction, but it is incremental as it builds on existing deep neural network techniques.
The paper tackled the problem of searching for neural network-based chart patterns, which had been unexplored, by formulating a general search problem and proposing a HyperNEAT framework that successfully found attractive patterns on the Korean stock market, showing potential compared to other schemes.
A neural network-based chart pattern represents adaptive parametric features, including non-linear transformations, and a template that can be applied in the feature space. The search of neural network-based chart patterns has been unexplored despite its potential expressiveness. In this paper, we formulate a general chart pattern search problem to enable cross-representational quantitative comparison of various search schemes. We suggest a HyperNEAT framework applying state-of-the-art deep neural network techniques to find attractive neural network-based chart patterns; These techniques enable a fast evaluation and search of robust patterns, as well as bringing a performance gain. The proposed framework successfully found attractive patterns on the Korean stock market. We compared newly found patterns with those found by different search schemes, showing the proposed approach has potential.