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EEG-Based Emotion Classification in Financial Trading Using Deep Learning: Effects of Risk Control Measures.

Bhaskar Tripathi¹, Rakesh Kumar Sharma¹

¹School of Humanities and Social Sciences, Thapar Institute of Engineering and Technology, Patiala 147004, India.

Sensors (Basel, Switzerland)

|April 13, 2023

Summary

This summary is machine-generated.

Risk management strategies like stop loss and limit orders significantly impact day traders' emotions. Using these tools promotes hope, while trading without them increases fear and worry, aiding decision-making.

Keywords:

behavioral finance decision-making deep learning electroencephalography (EEG)emotion classification neuro-finance

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Area of Science:

Neuroscience and Computational Finance
Application of deep learning in financial market analysis
EEG-based emotion recognition

Background:

Day traders face high pressure, influencing decisions and leading to losses.
Emotional states significantly affect trading outcomes, especially in volatile markets.
The impact of risk control measures on trader emotions remains unclear.

Purpose of the Study:

To assess the impact of stop loss and limit orders on day trader emotions.
To develop a deep learning framework for emotion classification in financial trading using EEG data.
To compare emotional states during trading with and without risk control measures.

Main Methods:

Two experiments were conducted: one with stop loss/limit orders, one without.
A novel hybrid neural network (CNN-BiLSTM-CRF) with Bayesian Optimization was developed.
Electroencephalography (EEG) data was used for valence-arousal emotion classification.

Main Results:

The proposed model achieved high classification accuracies (85.65% and 85.05%).
Trading with stop loss/limit orders correlated with High Valence/High Arousal emotions (hope).
Trading without these measures increased Low Valence/High Arousal emotions (fear, worry).
Calmness (High Valence/Low Arousal) was highest in a non-trading control group.

Conclusions:

The developed framework effectively classifies emotions in financial trading.
Stop loss and limit orders positively influence trader emotional states.
Findings can enhance risk management and decision-making for day traders.