Exploring Sentiment-Driven Stock Price Paths: Simulated Variations

Published: 22 December 2023| Version 1 | DOI: 10.17632/whdfr8rgyc.1
Sunil Maria Benedict


This study delves into the dynamic nature of financial markets by simulating sentiment-driven stock price paths. A series of graphs, each representing a unique simulation, were generated through a Python-based model. These simulations aimed to capture the influence of market sentiment on stock prices and showcase the inherent unpredictability and complexity of market dynamics. The simulations were conducted over a set number of trading days, utilizing sentiment fluctuations derived from stochastic processes. Each graph exhibits a distinct stock price trajectory, emphasizing the impact of varying sentiment levels on market behavior. Through these visualizations, it becomes evident that sentiment-driven factors contribute significantly to the volatility and direction of stock prices. The simulations illustrate the inherent uncertainty and randomness inherent in market sentiment, displaying diverse price movements across different scenarios. Key takeaways from the simulations include the non-linear nature of sentiment effects on stock prices, reflecting the intricate interplay between investor perceptions and market outcomes. These graphs elucidate the notion that sentiment-driven dynamics often lead to divergent and unpredictable stock price paths. This study provides valuable insights into the significance of sentiment fluctuations in shaping market behaviours and underscores the challenges associated with predicting stock price movements solely based on sentiment analysis. The diversity among the simulated graphs highlights the need for robust risk management strategies in financial decision-making, acknowledging the ever-changing landscape influenced by sentiments in the stock market. Overall, these visual representations offer a compelling narrative about the role of sentiment in financial markets, portraying the intricate relationship between market expectations and stock price trajectories.


Steps to reproduce

import pandas as pd import numpy as np import matplotlib.pyplot as plt # Read the CSV file into a DataFrame df = pd.read_csv('/Users/sunilbenedict/Documents/HINDUNILVR.NS.csv') # Extract the 'Close' prices for analysis closing_prices = df['Close'] # Calculate daily returns returns = np.log(closing_prices / closing_prices.shift(1)) # Calculate drift and volatility of returns drift = returns.mean() volatility = returns.std() # Set the initial stock price and parameters for the simulation stock_price = closing_prices.iloc[-1] # Initial stock price time_intervals = 252 # Number of trading days iterations = 5 # Number of simulations sentiments = np.random.normal(0, 0.01, (iterations, time_intervals)) # Random sentiment fluctuations # Simulate sentiment-driven stock price paths with varying sentiment randomness for i in range(iterations): price_paths = np.zeros(time_intervals) for t in range(1, time_intervals): sentiment_change = drift + sentiments[i, t] + volatility * np.random.normal(0, 1) price_paths[t] = stock_price * np.exp(sentiment_change) # Plot the simulations plt.figure(figsize=(10, 6)) plt.plot(price_paths, label=f'Simulation {i + 1}') plt.title(f'Sentiment-Driven Stock Price Simulation {i + 1}') plt.xlabel('Trading Days') plt.ylabel('Stock Price') plt.legend() plt.show()


CMR Group of institutions


Mathematics, Computer Simulation, Stock Exchange, Stock Assessment