Monte Carlo Simulation Prediction of Stock Prices

Xiang, Jeremy Ng Phak; Velu, Shubashini Rathina; Zygiaris, Sotirios

doi:10.1109/dese54285.2021.9719349

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…Madziwa et al (2022) in their research did gold price prediction based on the multivariate stochastic model. Xiang et al (2021) for the gold price prediction used Monte Carlo method. Some authors in their research, beside gold price prediction based on Monte Carlo method, analysed gold price returns (Chai et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

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Gold price prediction based on the Monte Carlo method

Jevtić,

Riznić,

Tomić

2024

XIX International May Conference on Strategic Management – IMCSM24 Proceedings - Zbornik Radova

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Gold is a precious metal that attracts the attention of authors from various fields. Due to its wide application in various industries, it is a very important resource for the business of many companies. In this paper, the price of gold was predicted for a period of one year, based on historical data, for approximately five years. Price forecasting was performed based on the Monte Carlo method, and the simulation itself was performed in the MATLAB software. The aim of this paper is to help the management of companies for which gold is a significant resource in planning and making business and financial decisions. The result of the forecast allows the management to create different scenarios to be ready to react to almost any situation on the market, and thus to maintain the position of the company they lead.

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Section: Literature Reviewmentioning

confidence: 99%

“…Monte Carlo method is widely used for stock price prediction in various industries and used by some groups of authors (Brodd & Djerf, 2018;Xiang et al, 2021;Jevtić et al, 2023;Čečević et al, 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

Gold price prediction based on the Monte Carlo method

Jevtić,

Riznić,

Tomić

2024

XIX International May Conference on Strategic Management – IMCSM24 Proceedings - Zbornik Radova

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“…The value at risk (VaR) and conditional value at risk (CVaR) is used o determine the portfolio's stability under adverse market scenarios. It is expressed in (24) and (25) respectively.…”

Section: Portfolio Stabilitymentioning

confidence: 99%

“…The study explores market volatility statistics using Python, employing geometric brownian motion (GBM) to simulate US and Malaysian stock prices. Conducted over 1,000 cycles, the simulations, driven by mean return and historical return standard deviation, offer valuable insights for retail traders through monte carlo analysis in finance, aiding in understanding market dynamics [24].…”

Section: Introductionmentioning

confidence: 99%

Monte carlo simulation with bilstm for day-ahead stock portfolio management

Shaikh,

Ramadass

2024

IJEECS

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<div>Predicting stock price movement and optimizing day-ahead stock portfolios are challenging tasks due to the inherent complexity and volatility of financial markets. This study proposes a novel approach that combines bidirectional long short-term memory (BiLSTM) neural networks with monte carlo simulation (MCS) to enhance day-ahead stock portfolio management. In the proposed methodology, historical data of the top-performing 10 stocks from different sectors of the National Stock Exchange of India (NSEI) is obtained from 1 January 2004 to 30 June 2023 and utilized to train a BiLSTM model. This model effectively extracts intricate patterns and trends from the time series, leading to more accurate and robust stock price predictions. MCS generates different scenarios, considering various market conditions and uncertainties. These scenarios provide a comprehensive view of the portfolio’s performance under different conditions, thus mitigating the risk of relying solely on a single prediction. The study evaluates the proposed framework and compares its performance against traditional portfolio management strategies. Results demonstrate that the MCS with the BiLSTM approach outperforms traditional methods in terms of risk-adjusted returns and portfolio stability.</div>

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“…Either VaR or ES are predicted loss risk based on historical stock returns. [14] state that these two risk measures have drawbacks. For VaR, it does not meet the subadditive axiom so it is not a coherent risk measure.…”

Section: Introductionmentioning

confidence: 99%

Comparison Between Value at Risk and Adjusted Expected Shortfall: A Numerical Analysis

Trimono,

Maruddani

2023

BAREKENG: J. Math. & App.

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Loss risk is one of the variable that always appears in every kind of investment. On stock asset investments, the characteristics of the risk of loss is uncertain, this means that losses can occur at any time with a value that cannot be determined certainly. From this condition, investors must manage the loss risk appropriately in order to retain investment stability and get optimal profits. One of the important processes in risk management is loss risk forecast. Risk forecast can be done using risk measures. In stock investment, Value at Risk (VaR) is the most widely used risk measure because has a simple model and can be applied to many types of stocks. However, VaR does not satisfy the axiom of subadditivity, thus VaR is not a coherent risk measure. Another risk measure that is coherent and can be used as an alternative to predict loss risk is the Adjusted-Expected Shortfall (Adj-ES). This study aims to compare VaR and Adj-ES through numerical analysis and backtesting test. So we can get reference to conclude the best risk measure for predicting losses on stock investments. The data used in this study are 2022 IDX blue chip i. e EXCL.JK and ICBP.JK from 09/01/21 to 09/09/22. Based on the backtesting test, the violation ratio value for Adj-ES in every violation probability is less than 1 is less than 1. Then, for VaR at 1% violation probability, the violation ratio value is > 1.

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Monte Carlo Simulation Prediction of Stock Prices

Cited by 4 publications

References 13 publications

Gold price prediction based on the Monte Carlo method

Gold price prediction based on the Monte Carlo method

Monte carlo simulation with bilstm for day-ahead stock portfolio management

Comparison Between Value at Risk and Adjusted Expected Shortfall: A Numerical Analysis

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