Stock price prediction using geometric Brownian motion

Agustini, W Farida; Affianti, Ika Restu; Putri, Endah R. M.

doi:10.1088/1742-6596/974/1/012047

Cited by 31 publications

(20 citation statements)

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“…In a ROA, price uncertainty is often modeled as a Geometric Brownian Motion (GBM) (Mun 2006, Guthrie 2009, Younis et al 2016, Farida Agustini et al 2018. A GBM is a stochastic simulation process in which a price follows a random walk.…”

Section: Price Uncertaintymentioning

confidence: 99%

Infrastructure maintenance and replacement optimization under multiple uncertainties and managerial flexibility

Boomen

Spaan

Shang

et al. 2019

Construction Management and Economics

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Infrastructure maintenance and replacement decisions are subject to uncertainties such as regular asset degradation, structural failure, and price uncertainty. In the engineering domain, Markov Decision Processes (MDPs) typically focus on uncertainties regarding asset degradation and structural failure. While the literature in the engineering domain stresses the importance of addressing price uncertainties, it does not substantiate the observations of such uncertainties through optimization modeling. By contrast, real option analyses (ROAs) that originate from the financial domain address price uncertainties but generally disregard asset degradation and structural failure. Accordingly, this piece of current research brings both domains closer together and proposes an optimization approach that incorporates the flexibility to choose between multiple successive intervention strategies, regular asset degradation, structural failure and multiple price uncertainties. A practical result of the current research is a realistic approach to optimization modeling in which state space reduction is achieved by combining prices into portfolios. The current research obtains transition probabilities from existing price data. This approach is demonstrated using a case study of a water authority in the Netherlands and confirms the premise that price fluctuations may influence short-term maintenance and replacement decisions. ARTICLE HISTORY

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Section: Price Uncertaintymentioning

confidence: 99%

Infrastructure maintenance and replacement optimization under multiple uncertainties and managerial flexibility

Boomen

Spaan

Shang

et al. 2019

Construction Management and Economics

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“…With the advent of AI in different fields, its rippling effect may also be seen in finance and price forecasting. As stock prices are updated every second, there is always a possibility of a drift in the data distribution and rendering [ 16 ]. Continual advances in computational science and data innovation are essential to the globalization of the economy [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction

Padhi

Padhy

Bhoi

et al. 2022

Computational Intelligence and Neuroscience

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Developing reliable equity market models allows investors to make more informed decisions. A trading model can reduce the risks associated with investment and allow traders to choose the best-paying stocks. However, stock market analysis is complicated with batch processing techniques since stock prices are highly correlated. In recent years, advances in machine learning have given us a lot of chances to use forecasting theory and risk optimization together. The study postulates a unique two-stage framework. First, the mean-variance approach is utilized to select probable stocks (portfolio construction), thereby minimizing investment risk. Second, we present an online machine learning technique, a combination of “perceptron” and “passive-aggressive algorithm,” to predict future stock price movements for the upcoming period. We have calculated the classification reports, AUC score, accuracy, and Hamming loss for the proposed framework in the real-world datasets of 20 health sector indices for four different geographical reasons for the performance evaluation. Lastly, we conduct a numerical comparison of our method’s outcomes to those generated via conventional solutions by previous studies. Our aftermath reveals that learning-based ensemble strategies with portfolio selection are effective in comparison.

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“…The core issue confronting researchers who have used Monte Carlo simulation methods is whether an accurate prediction of future stock prices is possible if the base data belong to different periods (Parungrojrat & Kidsom 2019). 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).…”

Section: Introductionmentioning

confidence: 99%

“…Although the GBM method recognizes random walk in the movement of stock prices, it also points toward specific components, an assumption inherently present in technical and fundamental analysis theories (Liu et al 2020). Therefore, prior research has advocated the theoretical soundness of the GBM method for predicting stock prices even as the research on providing empirical validity to the use of GBM in building stock prediction models is also growing (Agustini et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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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Stock price prediction using geometric Brownian motion

Cited by 31 publications

References 1 publication

Infrastructure maintenance and replacement optimization under multiple uncertainties and managerial flexibility

Infrastructure maintenance and replacement optimization under multiple uncertainties and managerial flexibility

An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction

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

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