Page 61 - IMDR Journal 2025

Research Article
Agents Define agent types, objectives, and how they MARL Agents train themselves by simulating millions of
interact. market conditions based on past elections, learning optimal
trading strategies through trial-and-error, adapting instantly
2. Choosing a Programming Language
to new exit poll data using real-time learning and
Python: Preferred for MARL due to its extensive libraries continuously improving trading decisions without human
and community support.
intervention.
Key Libraries The final output will an AI-powered predictive model that
▪ Tensor Flow Deep learning model training. accurately forecasts how Lok Sabha elections impact
financial markets before results are officially announced.
▪ Py Torch Flexible for research applications.
▪ Ray RLlib Scalable reinforcement learning framework.
3. MARL Frameworks and Libraries CONCLUSION
Petting Zoo Provides diverse environments for MARL This study highlights the significant influence of Lok Sabha
research. elections on Indian financial markets, demonstrating how
political stability and uncertainty drive market trends.
MAgent Simulates large-scale multi-agent interactions Traditional forecasting models, such as time series analysis
efficiently.
and sentiment analysis, struggle to adapt to real-time
MARL lib A unified interface for MARL algorithms. election-induced volatility, limiting their predictive
4. Implementing Learning Algorithms accuracy. In contrast, Multi-Agent Reinforcement Learning
(MARL) offers a more dynamic and precise approach by
Algorithm Selection Choose appropriate models such as
MADDPG (Multi-Agent Deep Deterministic Policy simulating investor behavior and market interactions. The
Gradient) or QMIX. findings suggest that clear election mandates typically lead
to market rallies, while uncertainty results in corrections.
Training Process Ensure agents learn effectively through Investors can leverage AI-driven insights to anticipate
interaction. market reactions, while policymakers can use these models
5. Leveraging Educational Resources to implement stability measures.
Research Papers: Review foundational and recent studies on Future research can focus on refining MARL with real-time
MARL. data integration, extending its applicability to global
GitHub & Online Courses: Access hands-on learning markets, and enhancing AI explainability for broader
materials. institutional adoption. By incorporating high-frequency
trading data and alternative market indicators, MARL can
6. Experimentation and Optimization Simulation Test
further improve its predictive accuracy. Additionally,
agent behaviours in dynamic environments.
expanding its application to other emerging economies can
Performance Evaluation Assess metrics like cumulative validate its effectiveness across different political systems.
rewards and strategy efficiency. Overall, MARL presents a transformative framework for
Optimization Fine-tune parameters for better decision- election-based market forecasting, offering an adaptive and
making and cooperation. data-driven approach to financial decision-making.
Contrasting MARL with Traditional Models
● Time Series Models (ARIMA, LSTM) Predictive REFERENCES
accuracy~50-54%, struggling with sudden political
Balaji C., K. G. (2018). Impact of General Elections on
volatility.
Stocks Markets in India. Open Journal of Economics and
● Sentiment Analysis Models Accuracy ~60-70%, but lacks Commerce, 1-7.
market interaction dynamics.
Kapoor, A. (2013). Effect of Political Decision Making on
● MARL Superiority By incorporating multi-agent Indian Capital Markets . International Journal of Research in
learning, MARL dynamically adjusts to real-time data, Management , 3(1).
leading to higher predictive potential in election-driven
Kavita Chavali, A. M. (2020). Stock Market Response to
markets.
Elections: An Event Study Method . The Journal of Asian
Unlike these traditional models, MARL dynamically learns Finance, Economics, and Business, 9-18.
from real-world interactions among multiple investor types, Loomba, J. (2014). 16th Lok Sabha Elections and Contagion
making it more adaptive and effective in volatile election- Effects to Indian Stock Market . Asia Pacific Journal of
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Additionally, while standard Reinforcement Learning (RL) Prof, G. A. (2020). The Impact of the General Election and
models focus on a single agent learning framework, the Parties Leading the Governement on the Stock Market
MARL’s multi-agent system enhances its predictive power Index in India. Nevilla Wadia Institute of Mangement
by simulating the competitive and cooperative nature of real Studies & Research, 88-96.
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