Income, Return, Growth Opportunity and Psychological Factors - Mutual Funds
Description
Description of the Data and Visualizations This dataset and the corresponding visualizations explore the relationships between several financial and psychological variables, including Steady Income (SI), Growth Opportunity (GO), Return (R), and Discipline (D). These relationships are defined through a set of equations and coefficients, providing insights into how these factors influence each other. Variables: Steady Income (SI): A numerical value representing the stable income of an individual or entity. Growth Opportunity (GO): A numerical value representing the potential for income growth. Return (R): The outcome or profit derived from investments, calculated based on SI, GO, and other factors. Discipline (D): Represents the investment discipline, influenced by SI, GO, and psychological factors. Psychological Factors (PF): A composite measure based on Return (RT), Consistency (C), and Emotional Stability (ES). Coefficients: alpha, beta, gamma, delta, eta, zeta, theta_1, theta_2, theta_3: These coefficients determine the weight and influence of each variable in the equations.
Files
Steps to reproduce
import numpy as np import matplotlib.pyplot as plt # Given values SI = np.array([20]) GO = np.array([30]) RT = np.array([7]) C = np.array([8]) ES = np.array([6]) # Coefficients alpha = 0.3 beta = 0.4 gamma = 0.2 delta = 0.5 eta = 0.3 zeta = 0.2 theta_1 = 0.4 theta_2 = 0.3 theta_3 = 0.3 # Calculate PF PF = theta_1 * RT + theta_2 * C + theta_3 * ES # Calculate Discipline (D) D = delta * SI + eta * GO + theta_1 * PF + zeta # Calculate Return (R) R = alpha * SI + beta * GO + gamma * (D * PF) + zeta # Values for plotting SI_range = np.linspace(10, 40, 100) GO_range = np.linspace(10, 40, 100) PF_range = theta_1 * 7 + theta_2 * 8 + theta_3 * np.linspace(5, 10, 100) D_range = delta * SI_range + eta * GO + theta_1 * PF_range + zeta R_range = alpha * SI_range + beta * GO + gamma * (D_range * PF_range) + zeta # Plot Steady Income vs. Return plt.figure(figsize=(10, 6)) plt.plot(SI_range, R_range, label='Return vs Steady Income', color='blue') plt.xlabel('Steady Income') plt.ylabel('Return') plt.title('Return vs Steady Income') plt.grid(True) plt.legend() plt.show() # Plot Growth Opportunity vs. Return plt.figure(figsize=(10, 6)) plt.plot(GO_range, R_range, label='Return vs Growth Opportunity', color='green') plt.xlabel('Growth Opportunity') plt.ylabel('Return') plt.title('Return vs Growth Opportunity') plt.grid(True) plt.legend() plt.show() # Plot Psychological Factors vs. Discipline plt.figure(figsize=(10, 6)) plt.plot(PF_range, D_range, label='Discipline vs Psychological Factors', color='red') plt.xlabel('Psychological Factors') plt.ylabel('Discipline') plt.title('Discipline vs Psychological Factors') plt.grid(True) plt.legend() plt.show()