Notes 11/11/23

 I have analyzed regression equation for WSP share price. I am of the opinion that Regression analysis won’t help us in arriving the weights as the co-efficient of independent variables can be negative at times. For example in the following regression equation, we can see co-efficient for financial sentiment is -0.057 and Sector sentiment is -0.019. I have tried different multi-regression equations by slicing and dicing the variables and ended-up with negative co-efficient. I spoke to Patrick on this, we are of the opinion that we can hold-on to multi-regression to save students time and effort.  

 

We can proceed with simple average/sum of sentiment score for the project. Please help me pass-on the message.

 

I have given Python code and excel spreadsheet. Students can run the code using the above spread sheet if they want to take a look into it.  Let me know if you have any thoughts on this.

 

Regression equation for WSP share price

Share Price = 2.2869 + (0.3070) * Macro sentiment + (-0.0576) * Financial sentiment + (-0.0109) * Sector sentiment

 

 

Python Code:

import pandas as pd
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

# Function to load data, perform regression, plot the results, and return the equation
def regress_and_plot(file_path):
    # Load the data
    data = pd.read_excel(file_path, index_col=0).T  # Transpose to have features as columns
    
    # Calculate percentage changes
    data_pct_change = data.pct_change().dropna() * 100  # convert to percentage
    
    # Independent variables (sentiment scores)
    X = data_pct_change.iloc[:, :-1]  # Assuming the last column is the share price
    
    # Dependent variable (share price percentage change)
    y = data_pct_change.iloc[:, -1]  # Assuming the last column is the share price
    
    # Initialize and fit the model
    model = LinearRegression()
    model.fit(X, y)
    
    # Generate predictions
    predictions = model.predict(X)
    
    # Plotting the actual versus predicted values
    plt.figure(figsize=(10, 6))
    plt.scatter(range(len(y)), y, color='blue', label='Actual Percentage Change')
    plt.plot(range(len(y)), predictions, color='red', linestyle='--', label='Predicted Percentage Change')
    
    # Add titles and labels
    plt.title('Regression Line for Share Price Percentage Change')
    plt.xlabel('Time')
    plt.ylabel('Percentage Change in Share Price')
    plt.legend()
    plt.grid(True)
    plt.show()
    
    # Construct the regression equation
    equation = f"Share Price = {model.intercept_:.4f}"
    for coef, name in zip(model.coef_, X.columns):
        equation += f" + ({coef:.4f}) * {name}"
    
    return equation

# Path to the uploaded Excel file
file_path = 'C:/WSP sentimentV2.xlsx' #two months lag in share price from respective quarter to account for earnings call date

# Call the function to perform regression, plot the graph, and get the equation
regression_equation = regress_and_plot(file_path)
print(regression_equation)