Abstract
Investors are exposed to risk and uncertainty because of changes in financial markets’ prices. Investors perceive the risks associated with changes in the market prices as higher due to inaccuracy in predicting future returns because of fluctuations in prices. For this reason, they adopt different risk management methods that reduce or eliminate these risks. This research relies on Monte Carlo Simulation technique in predicting forthcoming yield rates from three companies operating under Turkish automotive segment namely, Dogus Automotive (DOAS), Tofas (TOASO) and Ford Otosan (FROTO). The simulation, which runs from January 1, 2023, to December 31, 2023, gives investors research-based insights that help them make strategic investment choices in times of high volatility in the market. According to the results, by modeling prospective future scenarios, MCS can be employed as a viable means of predicting stock prices in financial markets which subsequently helps people make rational investments thereby securing profitable ventures. Furthermore, this study offers practical suggestions in the form of MCS-generated volatility ranges. Investors can determine when it is advisable to buy or sell stocks in order to reduce potential losses and increase profits by setting realistic price objectives and allocating the portfolio differently in accordance with these calls.
Keywords
Risk Management Financial Markets Automotive Industry Stock Forecasting Monte Carlo Simulation
References
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Abstract
References
- [1] Edjossan-Sossou A. M., Sustainable risk management strategy selection using a fuzzy multi-criteria decision approach, Int. J. Disaster Risk Reduct., 45 (2019) 101474.
- [2] M. Broadie, Operations Research jUffl rms Risk Estimation via Regression, 63(5) (2024) 1077–1097.
- [3] David Iyanuoluwa A., Rhoda Adura A., Onyeka Franca A., Oluwaseyi Rita O., Binaebi Gloria ., Ndubuisi Leonard N., Review of Ai Techniques in Financial Forecasting: Applications in Stock Market Analysis, Financ. Account. Res. J., 6(2) (2024) 125–145.
- [4] Prakash A., Mohanty R.P., DEA and Monte Carlo simulation approach towards green car selection, Benchmarking, 24(5) (2017) 1234–1252.
- [5] Sivabalan S., Minu R.I., Statistical Sales Forecasting Using Machine Learning Forecasting Methods for Automotive Industry, vol. 686 LNNS. Springer Nature Singapore, (2023).
- [6] Čečević B. N., Antić L., Jevtić A., Stock Price Prediction of the Largest Automotive Competitors Based on the Monte Carlo Method, Econ. Themes, 61(3) (2023) 419–441.
- [7] Borchert P., Zellmer-Bruhn D. M., Reproduced with permission of the copyright owner . Further reproduction prohibited without, J. Allergy Clin. Immunol., 130(2) (2010) 556.
- [8] Zhao X., Liu H., Zhang B., Liu F., Luo J., Bai J., Fast photon-boundary intersection computation for Monte Carlo simulation of photon migration, Opt. Eng., 52(1) (2013) 019001.
- [9] Twumasi C., Twumasi J., Machine learning algorithms for forecasting and backcasting blood demand data with missing values and outliers: A study of Tema General Hospital of Ghana, Int. J. Forecast., 38(3) (2022) 1258–1277.
- [10] Twumasi C., Cable J., Pepelyshev A., Mathematical Modelling of Parasite Dynamics: A Stochastic Simulation-Based Approach and Parameter Estimation via Modified Sequential-Type Approximate Bayesian Computation, vol. 86, no. 5. Springer US, 2024.
Details
| Primary Language | English |
|---|---|
| Subjects | Statistical Analysis, Statistical Theory, Statistical Data Science |
| Journal Section | Natural Sciences |
| Authors | |
| Publication Date | December 30, 2024 |
| Submission Date | May 1, 2024 |
| Acceptance Date | December 16, 2024 |
| Published in Issue | Year 2024Volume: 45 Issue: 4 |
