Enhancement of price trend trading strategies via image-induced importance weights

TL;DR

This paper introduces a novel image-based importance weighting method that enhances price trend trading strategies by leveraging deep learning analysis of price chart images, improving portfolio performance.

Contribution

It develops the triple-I importance weights derived from deep learning image analysis to improve trading signals and portfolio strategies in stock markets.

Findings

Significantly improved trading signals in Chinese stock market.

Robustness across different network setups and image structures.

Enhanced long-term and news-based trading strategies.

Abstract

We open up the "black-box" to identify the predictive general price patterns in price chart images via the deep learning image analysis techniques. Our identified price patterns lead to the construction of image-induced importance (triple-I) weights, which are applied to weighted moving average the existing price trend trading signals according to their level of importance in predicting price movements. From an extensive empirical analysis on the Chinese stock market, we show that the triple-I weighting scheme can significantly enhance the price trend trading signals for proposing portfolios, with a thoughtful robustness study in terms of network specifications, image structures, and stock sizes. Moreover, we demonstrate that the triple-I weighting scheme is able to propose long-term portfolios from a time-scale transfer learning, enhance the news-based trading strategies through a non-technical transfer learning, and increase the overall strength of numerous trading rules for portfolio selection.