Page 60 - IMDR Journal 2025

Research Article
Correlation of Election with Indian Financial Markets bank stocks reacted when polls suggested an uncertain or
split verdict.
The outcome of the Lok Sabha election has a profound
impact on Indian financial markets, reflecting the 4. Making Better Predictions
interconnectedness between political stability and economic As these agents keep learning from more data and simulated
confidence. Elections play a big role in shaping how the experiences, the system becomes better at predicting how
country is run, and the markets react quickly based on who markets might move in response to future political
might come to power and what their plans are. During scenarios.
election time, stock markets often become jumpier, as Trial-and-Error Learning: Agents test multiple trading
investors try to guess what changes might be coming.
strategies and optimize based on outcomes.
If one party wins clearly and forms a stable government,
Real-Time Adaptation: The model updates predictions
investors usually feel more confident. It means economic dynamically using exit poll results, news sentiment, and
policies are more likely to continue smoothly. But if the institutional investor activity.
results are unclear or no party has a strong majority, markets
may become nervous. People worry that important decisions Example Training Scenario
might get delayed or stuck. ● If an agent buys banking stocks before a stable govern-
The outcome of elections doesn’t just affect stocks-it also ment is confirmed and they rise the agent gets a reward.
impacts the value of the Indian rupee, interest rates on ● If an agent holds stocks during political uncertainty and
government bonds, and how much foreign money comes they crash agent gets a penalty.
into the country. Governments that focus on reforms and
This process repeats millions of times across different
growth tend to attract more foreign investment, which historical elections until the AI finds the most profitable
strengthens the economy and makes India more attractive to patterns.
global investors.
Training on Historical Lok Sabha Election Data
The new government’s choices around spending,
borrowing, and inflation control also affect things like The AI is fed past election results and stock market reactions
prices, interest rates, and how fast the economy grows. So, to learn how different scenarios impacted the market:
elections aren’t just about politics—they have a big
influence on the economy and the mood of investors, both in
India and abroad.

How MARL Agents model works and Train Themselves
for Predicting Election-Based Market Movements
Understanding How AI (MARL) Models Stock Market
Reactions to Exit Polls: Multi-Agent Reinforcement
Learning (MARL) is a smart way to use AI for predicting
how the stock market might react to events like Lok Sabha
exit polls. Unlike older methods, MARL doesn’t just rely on
fixed formulas it learns by simulating how different types of
investors behave in real-world situations.
The MARL model learns from these historical reactions and
Steps
builds strategies that adapt dynamically to new elections.
1. Creating a Virtual Market Real-Time Learning from 2024 Elections (Live Market
First, a virtual stock market is set up. In this environment, Data)
different AI agents represent real-life market participants Once trained, the MARL system can process real-time exit
like regular traders, big institutional investors, and high- poll results and adjust its trading strategies dynamically.
speed trading firms. These agents “live” in the system and
respond to changing political or economic news, such as exit Example
poll results. ● AI detects that exit polls are showing unexpected seat
losses for the ruling party.
2. Learning Through Practice
● The AI analyzes social media, news sentiment, and FII
Each AI agent makes decisions like buying, selling, or
holding stocks. Based on the outcome (profit or loss), the outflows to predict a potential market correction.
agent gets a reward or penalty. Over time, they learn what ● The system executes automatic trades before human
strategies work best through trial and error—just like traders react, minimizing risks and maximizing profits.
humans do. Building a MARL Model Key Components and Resources
3. Using Past Election Data 1. Defining the Environment and Agents
To train these agents, real historical data from Indian Environment: Create a simulated world where agents
elections (1999 to 2019) is fed into the system. This includes operate, including state space, action space, and reward
exit poll results and how the stock market moved around mechanisms.
those events. For example, the model might learn how PSU

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