MODERN PORTFOLIO THEORY AND FORECAST IMPLEMENTATION USING MONTE CARLO SIMULATION IN AUTOMOTIVE FINANCING COMPANY

• Main Author: Fajartama, Rian
• Format: Theses
• Language: Indonesia
• Subjects: Manajemen umum
• Online Access: https://digilib.itb.ac.id/gdl/view/55312
• Institution: Institut Teknologi Bandung

Automotive is a product that can support Indonesia’s economic improvement, especially motorcycle and car segments, which can increase the productivity of Indonesian society. The role of multifinance companies is vital for society to own a vehicle. Good efficiency is important for financing companies to remain stable in the new normal Covid-19 pandemic era, with one of the vital aspects is their financing portfolio management. Through the concept of Risk and Return, can be determined whether a risk is acceptable or what action must be taken to determine the trade-off between risk and return. Through Markowitz's Modern Portfolio Theory, optimal portfolio composition is also developed. This research aims to analyze or find optimal portfolio compositions for the Indonesian Automotive Financing Company. The study used annual return data of the 2019-2020 period along with the standard deviation. The probability of optimum yield is simulated using Monte Carlo Simulation with 10,000 trials. The minimum target yield is determined using the existing yield. An analysis is also done to search minimized risk to avoid new normal Covid-19 economic conditions risk. Monte Carlo Simulation and Markowitz’s Methods are reliable for forecasting portfolio composition and determining the best composition. Two simulations are conducted to find the maximized return portfolio and the minimized risk portfolio, with existing yield is 22.00%, standard deviation (StDev) of 0.59%, and coefficient of variance (CoVar) of 2.68%. We found the maximized return portfolio composition resulted in 29.80% yield (35% improvement), 0,50% StDev (15% improvement), with CoVar 1,67% (37% improvement). The minimized risk portfolio is resulting in a 24.26% yield (10% improvement), 0.38% StDev (35% improvement), with CoVar of 1.56% (41% improvement). Both cases provide an illustration in the form of a hypothesis with a statistical approach that is feasible to apply depending on the preferred trade-off between risk and return.