# coding: utf-8 # # Credit Corp's Consumer Lending Segment # # Note: All numbers are in the form of \$'000 unless otherwise stated # # Let's import some libraries first... # In[1]: import pandas from pandas.plotting import scatter_matrix from sklearn import datasets from sklearn import model_selection from sklearn import linear_model # models from sklearn.ensemble import RandomForestRegressor from sklearn.neighbors import KNeighborsRegressor from sklearn.preprocessing import StandardScaler, PolynomialFeatures from sklearn.linear_model import LinearRegression, Ridge from sklearn.tree import DecisionTreeRegressor from sklearn.metrics import mean_squared_error, r2_score import matplotlib.pyplot as plt # Load the past few years of relevant data. # In[2]: dataset = pandas.read_csv("data/ccp-consumer-lending-full-year.csv") print (dataset) # Let us create a linear regression model with the whole dataset. # In[3]: array = dataset.values X = array[:,3:5] # data = avg_gross_loan_book, net_lending Y = array[:,2] # result = NPAT model = LinearRegression() model.fit(X, Y) # train model # the model's linear regression coefficients print("Coefficients: \t%s" % model.coef_) print("Intercept: \t%s" % model.intercept_) print("\nThe equation would look like...") print("p = %sr + %sl + %s" % (model.coef_[0], model.coef_[1], model.intercept_)) # Where # ``` # p = Net profit before tax (npbt) # b = Average gross loan book (gross_book_average) # l = Net lending for the period (net_lending) # ``` # # FY19 Predictions # # ## Based on management's forecasts # # Assumptions: # # * Gross loan book to end the year at \$199.896m (long story on how I got to something so specific) # # * Average gross loan book will be \$191.496m # # * Net lending will be \$50m, on the upper range of the forecast. Quoting a high number here will actually reduce NPBT. # # In[4]: gross_book_average = 191496 net_lending = 50000 npbt = model.predict([[gross_book_average, net_lending]])[0] print("EBIT = \$%sm" % (npbt/1000)) print("NPAT = \$%sm" % (npbt/1000 * 0.7)) # This sits inside the \$17 - 19m range forecast by management, so our model is not crazy bad! # # ## Based on a zero-growth scenario # # The higher the Net Lending completed by the company, the lower the reported Net Profit due to the way the company provisions the expected lossed upfront. So you get a situation where the NPAT is under-reported, unless the company stops growing its loan book. So what happens with NPAT when the loan book stops growing? # # * Assume 17.34% of gross loan book is the required net lending to maintain the loan book. # * Last 5 years (FY14 - FY18) this figure has been: 14.22%, 17.88%, 16.82%, 13.99%, 17.34%. # In[5]: net_lending = gross_book_average * 0.1734 print("\nNet Lending Assumption = %s\n" % net_lending) npbt_zero_growth = model.predict([[gross_book_average, net_lending]])[0] print("EBIT: \$%sm" % (npbt_zero_growth / 1000)) print("NPAT: \$%sm" % (npbt_zero_growth * 0.7 / 1000)) print("\nNPAT buffer: \$%sm" % ((npbt_zero_growth - npbt) / 1000))