AI-powered stock market analysis with a neural network overlay

Can AI Predict the Stock Market? How Deep Learning is Changing Trading

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Samir D’Costa in Tech & Innovation December 2025 • 5 min read.

"Explore how a new deep-learning model simulates human stock traders, potentially unlocking more profitable strategies"


The stock market, often perceived as a complex and unpredictable arena, has long presented challenges to investors seeking consistent profits. While the efficient market hypothesis suggests that it's impossible to outperform the market, numerous studies indicate the presence of inefficiencies that can be exploited. This has led to the development of various techniques aimed at achieving above-market returns, commonly known as alpha.

In recent decades, systematic trading has experienced significant advancements, with deep learning schemes emerging as powerful tools for analyzing and predicting market behavior. These schemes leverage vast amounts of historical data to identify patterns and relationships that might be invisible to human analysts. This article delves into how these models work and their potential impact on the future of trading.

Inspired by the methods of professional technical analysts, a novel deep learning model has been developed to simulate human stock traders' chart analysis. By examining stock prices over extended periods and predicting future price movements, this model offers a fresh perspective on leveraging AI for investment strategies. This article will explore the inner workings of this model, its performance, and the implications for investors looking to gain an edge in the market.

Decoding the Deep Learning Model: Simulating Human Trading Intuition

AI-powered stock market analysis with a neural network overlay

The core innovation lies in a deep learning model designed to mimic how professional technical analysts make trading decisions. Unlike traditional quantitative methods, this approach focuses on learning directly from stock price charts, much like a human trader would. The model analyzes historical stock prices over a 600-day period to predict whether the price will rise or fall by 10% or 20% within a specified timeframe. This timeframe, denoted as 'D' days, is a variable that is tested with different durations to optimize the model's performance.

The model utilizes a modified ResNet architecture, a type of deep neural network known for its ability to handle complex patterns and dependencies in data. ResNet's skip connections enable the network to retain crucial information from earlier layers, facilitating the training of deeper and more sophisticated models. This is particularly important in stock market analysis, where both short-term and long-term trends influence price movements.

Here's a breakdown of the model's key components:
  • ResNet Architecture: The ResNet model's skip connections capture both major and minor trends in stock prices.
  • Long Window Size: A 600-day window provides ample historical data for identifying patterns and trends.
  • Discretized Return Rate Labels: Assigning labels based on whether the price rises or falls by 10% or 20% simplifies the learning process and avoids overfitting.
  • Softmax Logits and Thresholding: Trading decisions are based on softmax logit values, which indicate the model's confidence in its predictions. A threshold is applied to filter out less confident predictions and focus on higher-probability trades.
To increase the probability of success, the model incorporates a thresholding technique based on softmax logits. Softmax logits represent the model's confidence level in its predictions. By only trading stocks where the largest softmax logit exceeds a certain threshold, the model focuses on higher-probability opportunities. This approach also helps reduce the number of trades, making it more practical for real-world applications where transaction costs can significantly impact profitability.

The Future of Trading: AI-Powered Insights

The research presented in this paper marks a significant step forward in the application of AI to stock market analysis. By simulating human trading intuition through deep learning, the model offers a unique approach to predicting market trends and improving investment outcomes. While challenges such as market volatility and data limitations remain, the potential for AI to transform the trading landscape is undeniable. As AI technology continues to evolve, we can expect even more sophisticated models and strategies to emerge, further blurring the lines between human expertise and machine intelligence in the world of finance.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2304.1487,

Title: Using A Deep Learning Model To Simulate Human Stock Trader'S Methods Of Chart Analysis

Subject: q-fin.st cs.lg

Authors: Sungwoo Kang, Jong-Kook Kim

Published: 28-04-2023

Everything You Need To Know

1

What is the primary goal of the deep learning model described for stock market analysis?

The primary goal of the deep learning model is to simulate the decision-making process of professional technical analysts. It aims to analyze historical stock prices and predict future price movements, offering a fresh perspective on investment strategies. This approach, unlike traditional quantitative methods, focuses on learning directly from stock price charts, similar to how a human trader would operate.

2

How does the ResNet architecture contribute to the deep learning model's effectiveness in stock market analysis?

The ResNet architecture is crucial because of its ability to handle complex patterns and dependencies in stock market data. Its skip connections allow the network to retain crucial information from earlier layers, which is particularly important for analyzing both short-term and long-term trends that influence price movements. This feature enables the model to process and learn from a 600-day window of historical stock prices, facilitating a deeper understanding of market behavior.

3

Can you explain the role of 'Discretized Return Rate Labels' in the model?

The 'Discretized Return Rate Labels' simplify the learning process and help avoid overfitting by assigning labels based on whether a stock price rises or falls by 10% or 20% within a specified timeframe. This simplifies the model's task by categorizing price movements into predefined outcomes rather than trying to predict exact price changes. This approach makes the model more robust and easier to train, as it focuses on broader trends rather than minute fluctuations.

4

How does the model use softmax logits and thresholding to improve trading decisions?

The model uses softmax logits, which represent its confidence level in its predictions, and applies a thresholding technique to filter out less confident predictions. By only trading stocks where the largest softmax logit exceeds a certain threshold, the model focuses on higher-probability opportunities. This approach reduces the number of trades, which is particularly beneficial in real-world applications where transaction costs can impact profitability. The thresholding helps in making more informed and strategic trading decisions.

5

What are the potential future implications of AI in stock market analysis, according to the text?

The text suggests that AI has the potential to transform the trading landscape. As AI technology continues to evolve, we can expect more sophisticated models and strategies to emerge. The deep learning model discussed marks a significant step forward in applying AI to stock market analysis, by simulating human trading intuition. The research highlights the potential for AI to improve investment outcomes and blur the lines between human expertise and machine intelligence in finance, despite challenges like market volatility and data limitations.

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