Geometric Brownian Motion with Ito's lemma approach to evaluate market fluctuations: A case study on Colombo Stock Exchange

Rathnayaka, R. M. K. T.; Jiang, Wei; Seneviratna, D.M.K.N.

doi:10.1109/besc.2014.7059517

Cited by 18 publications

(8 citation statements)

References 10 publications

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“…Furthermore, our results are similar to the conclusions in Merh et al (2010) and Lee et al (2007) which stated that ARIMA models outperform ANN models for stock price predictions. On the other hand, Rathnayaka et al (2014) found that the stochastic model prediction is more significant than the traditional ARIMA model. In fact, on the basis of our results, the ARIMA model and the stochastic model produce almost the same results.…”

Section: Discussionmentioning

confidence: 95%

“…They found a higher accuracy for prediction with a mean absolute percentage error (MAPE) less than 20%. Rathnayaka et al (2014) developed a forecasting model using the geometric Brownian motion model and compared the predictions with the results from the traditional time series model ARIMA. They used the Colombo Stock Exchange (CSE), Sri Lanka data to build their models and found that the stochastic model prediction is more significant than the traditional model.…”

Section: Literature Reviewmentioning

confidence: 99%

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Comparison of Financial Models for Stock Price Prediction

Islam

Nguyen

2020

JRFM

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Time series analysis of daily stock data and building predictive models are complicated. This paper presents a comparative study for stock price prediction using three different methods, namely autoregressive integrated moving average, artificial neural network, and stochastic process-geometric Brownian motion. Each of the methods is used to build predictive models using historical stock data collected from Yahoo Finance. Finally, output from each of the models is compared to the actual stock price. Empirical results show that the conventional statistical model and the stochastic model provide better approximation for next-day stock price prediction compared to the neural network model.

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Section: Discussionmentioning

confidence: 95%

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Financial Models for Stock Price Prediction

Islam

Nguyen

2020

JRFM

View full text Add to dashboard Cite

show abstract

“…These include the bilinear models, the threshold autoregressive, autoregressive conditional heteroscedastic, etc. (Jayathileke and Rathnayake, 2013; Rathnayaka and Seneviratna, 2014; Rathnayaka et al , 2014). However, most of the forecasting mechanisms suggest that linear and nonlinear separate methods are not sufficient to forecast modern financial indices under high volatility.…”

Section: Discussionmentioning

confidence: 99%

An unbiased GM(1,1)-based new hybrid approach for time series forecasting

Rathnayaka

Seneviratna

Jiang

et al. 2016

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Purpose The time series forecasting is an essential methodology which can be used for analysing time series data in order to extract meaningful statistics based on the information obtained from past and present. These modelling approaches are particularly complicated when the available resources are limited as well as anomalous. The purpose of this paper is to propose a new hybrid forecasting approach based on unbiased GM(1,1) and artificial neural network (UBGM_BPNN) to forecast time series patterns to predict future behaviours. The empirical investigation was conducted by using daily share prices in Colombo Stock Exchange, Sri Lanka. Design/methodology/approach The methodology of this study is running under three main phases as follows. In the first phase, traditional grey operational mechanisms, namely, GM(1,1), unbiased GM(1,1) and nonlinear grey Bernoulli model, are used. In the second phase, the new proposed hybrid approach, namely, UBGM_BPNN was implemented successfully for forecasting short-term predictions under high volatility. In the last stage, to pick out the most suitable model for forecasting with a limited number of observations, three model-accuracy standards were employed. They are mean absolute deviation, mean absolute percentage error and root-mean-square error. Findings The empirical results disclosed that the UNBG_BPNN model gives the minimum error accuracies in both training and testing stages. Furthermore, results indicated that UNBG_BPNN affords the best simulation result than other selected models. Practical implications The authors strongly believe that this study will provide significant contributions to domestic and international policy makers as well as government to open up a new direction to develop investments in the future. Originality/value The new proposed UBGM_BPNN hybrid forecasting methodology is better to handle incomplete, noisy, and uncertain data in both model building and ex post testing stages.

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“…Using historical volatility and GBM, Urama and Ezepue (2018) forecast and model stock prices of a Nigerian bank. Rathnayaka et al (2014) estimate share price indices in short-term investment on the Colombo Stock Exchange in Sri Lanka using a forecasting model based on GBM. Using historical returns and standard deviations as proxies for the drift and diffusion terms, Abidin and Jaffar (2014) apply the GBM approach to forecast the closing price of small-sized companies listed on Bursa Malaysia.…”

Section: Literature Reviewmentioning

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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Geometric Brownian Motion with Ito's lemma approach to evaluate market fluctuations: A case study on Colombo Stock Exchange

Cited by 18 publications

References 10 publications

Comparison of Financial Models for Stock Price Prediction

Comparison of Financial Models for Stock Price Prediction

An unbiased GM(1,1)-based new hybrid approach for time series forecasting

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

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