
Simulation of Human Stock Traders' Chart Analysis Methods Using Deep Learning Models (Apr 8, 2024)
created At: 3/17/2025

Neutral
This analysis was written from a neutral perspective. We advise you to always make careful and well-informed investment decisions.
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Fact
Research Objective
-Simulating human traders’ chart analysis using deep learning.
-Leveraging market inefficiencies to optimize investment strategies.
Methodology
-Chart pattern learning: Using a ResNet-based deep learning model.
-Long-term data analysis: Incorporating 600 days of price data.
-Binarized return prediction: Forecasting whether prices will rise/fall by 10% or move sideways.
-ResNet architecture optimization: Tailoring it for time-series data.
-Softmax probability distribution: Determining the best trade entry points.
Opinion
This study demonstrates that deep learning-based chart analysis can generate meaningful excess returns. The model performed exceptionally well in the Korean market, reinforcing the idea that chart patterns are more useful in less efficient markets.
In contrast, the lower performance in the U.S. market suggests that technical analysis alone may not be sufficient in highly efficient markets. However, fine-tuning Softmax logit values could improve strategy performance.
Ultimately, this study highlights that deep learning-based chart analysis can be a viable investment approach in inefficient markets, and Softmax logit optimization is a key factor for success.
Core Sell Point
Deep learning-based chart analysis can yield high excess returns in the Korean market and, with strategy optimization, can enhance performance in the U.S. market as well.
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