Contribution Indonesian Composite Index in PT Telekomunikasi Indonesia stock price model using 2-dimensional Geometric Brownian Motion

Hoyyi, Abdul; Tarno,; Maruddani, Di Asih I; Rahmawati, Rita

doi:10.1088/1742-6596/1217/1/012091

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…However, this trend cannot be made based on a definitive trend in the movement of share prices in the future because whatever predictions one might make can only be described as probabilistic and speculative (Chen et al 2018). Any sure prediction requires a standard order and an explainable pattern over a long period (Hoyyi et al 2019). If it is impossible to do this, the resultant implication is that the movement of share prices is only random (Liu et al 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Prior research has demonstrated that the shorter the duration of the base period, the higher the prediction accuracy in the short term (Agustini et al 2018). For instance, researchers have found the prediction accuracy higher for one week's prediction (Hoyyi et al 2019), as shown by lower mean absolute percentage error (MAPE). Monte Carlo simulation considers the stock prices' randomness, drift, and volatility in its predictions (Lux 2018).…”

Section: Introductionmentioning

confidence: 99%

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Effectiveness of Geometric Brownian Motion Method in Predicting Stock Prices: Evidence From India

2022

AJAG

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This research examines whether stock prices in the Indian stock markets follow a Geometric Brownian Motion (GBM).This study is keen on knowing if one can predict the simulated stock prices accurately against the actual stock prices. One-year, three-year, and five-year data of the historical stock prices of 50 stocks listed on the S&P BSE (Bombay Stock Exchange) Sensex 50 Index were employed as the base data to predict stock prices using the Monte Carlo simulation's GBM method. This study investigates whether there are statistically significant differences between the actual stock prices for three months and the simulated prices of the same period. This research has found that the GBM Monte Carlo simulation effectively predicts future stock prices for three months based on the historical data of stock prices of the past year. This study did not find significant differences between the actual and predicted stock prices when the simulation used the past one year's data. This research is original in the Indian context, as it situates the GBM method of Monte Carlo simulation in the premise of bounded rationality and efficient market hypothesis theories. There is thus the empirical evidence for bounded rationality and that the stock markets are not efficient.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effectiveness of Geometric Brownian Motion Method in Predicting Stock Prices: Evidence From India

2022

AJAG

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“…They find the GBM approach to be “a safe model” for forecasts of daily closes for up to 2 weeks of investments. Hoyyi et al (2019) use a two‐dimensional GBM to model stock prices of PT Telekomunikasi Indonesia. Ladde and Wu (2009) and Wu (2010) use historical values and least squares regression to estimate drift and diffusion parameters for their modified GBM framework.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Traditionally, the drift and diffusion terms are estimated using historically returns and standard deviation (Benninga, 2014;Estember & Maraña, 2016;Sengupta, 2004;Urama & Ezepue, 2018), although some papers have attempted to use forecasted drift and diffusion terms (Abidin & Jaffar, 2014;Hoyyi et al, 2019;Ladde & Wu, 2009;Reddy & Clinton, 2016;Shafii et al, 2019;Surapaitoolkorn, 2009;Wu, 2010). Reddy and Clinton (2016) use expected drift estimated by using the capital asset pricing model (CAPM), but they nevertheless use historic diffusion term.…”

Section: Introductionmentioning

confidence: 99%

The reliability of geometric Brownian motion forecasts of S&P500 index values

Sinha

2021

Journal of Forecasting

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This manuscript extends the literature on the application of geometric Brownian motion. Forecasted drift and diffusion terms estimated separately and recursively are plugged into the framework to forecast S&P500 index values. Expected index values are estimated from 100,000 simulated index values and probabilities. The results of comparing expected index values with actual values indicate that although reliable predictions of S&P500 index values can be obtained at monthly, quarterly, and annual frequencies, the reliability may decrease in that order.

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“…The former has its advantage of describing the decision-making for all points in the time horizon, while the later seeks one or limited several (multiple optimal stopping times) timings for some specific action and it handles both discrete and continuous environment. In the operation modelling of this paper, we mainly focus on the time to suspend or halt production under pressure of profitability and the decision is under consideration of time-continuous price dynamic, hence optimal stopping problem is established after modeling the price process with geometric Brownian motion, which is commonly used to simulate the variable on continuous time horizon, as applied in [10] for price forecasting, [11], [12] for stock trading, [13] for risk analysis and hedging.…”

Section: Introductionmentioning

confidence: 99%

Option to survive or surrender: carbon asset management and optimization in thermal power enterprises from China

Liu

Xie

et al. 2021

Preprint

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Carbon emission right allowance is a double-edged sword, one edge is to reduce emission as its original design intention, another edge has in practice slain many less developed coal-consuming enterprises, especially for those in thermal power industry. Partially governed on the hilt in hands of the authority, body of this sword is the prices of carbon emission right. How should the thermal power plants dance on the blade motivates this research. Considering the impact of price fluctuations of carbon emission right allowance, we investigate the operation of Chinese thermal power plant by modeling the decision-making with optimal stopping problem, which is established on the stochastic environment with carbon emission allowance price process simulated by geometric Brownian motion. Under the overall goal of maximizing the ultimate profitability, the optimal stopping indicates the timing of suspend or halt of production, hence the optimal stopping boundary curve implies the edge of life and death with regard to this enterprise. Applying this methodology, real cases of failure and survival of several Chinese representative thermal power plants were analyzed to explore the industry ecotope, which leads to the findings that: 1) The survival environment of existed thermal power plants becomes severer when facing more pressure from the newborn carbon-finance market. 2) Boundaries of survival environment is mainly drawn by the technical improvements for rising the utilization rate of carbon emission. Based on the same optimal stopping model, outlook of this industry is drawn with a demarcation surface defining the vivosphere of thermal power plants with different levels of profitability. This finding provides benchmarks for those enterprises struggling for survival and policy makers scheming better supervision and necessary intervene.

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Contribution Indonesian Composite Index in PT Telekomunikasi Indonesia stock price model using 2-dimensional Geometric Brownian Motion

Cited by 5 publications

References 5 publications

Effectiveness of Geometric Brownian Motion Method in Predicting Stock Prices: Evidence From India

Effectiveness of Geometric Brownian Motion Method in Predicting Stock Prices: Evidence From India

The reliability of geometric Brownian motion forecasts of S&P500 index values

Option to survive or surrender: carbon asset management and optimization in thermal power enterprises from China

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