Study of Effectiveness of Time Series Modeling (Arima) in Forecasting Stock Prices

Mondal, Prapanna; Shit, Labani; Goswami, Saptarsi

doi:10.5121/ijcsea.2014.4202

Cited by 177 publications

(137 citation statements)

References 16 publications

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“…Finally, the ARIMA(p,d,q) which is a generalization of ARMA(p,q) is mathematically defined as in (14)  …”

Section: Formulation Of Autoregressive Integrated Moving Average mentioning

confidence: 99%

“…The existing literature contains numerous forecast models such as Exponential Smoothing (ETS) [11], TBATS model [12] and Autoregressive and Moving Average (ARMA) model [13]. ARMA model is a well-known method to investigate the time series data [14]. Based on ARMA model, [15] formulated Autoregressive and Integrated Moving Average (ARIMA) model for predicting the linear time series data by transforming the non-stationary data into stationary data before forecasting.…”

Section: Introductionmentioning

confidence: 99%

“…ARIMA based forecasting models are widely used to forecast weather patterns [16], stock market [14] and electricity loads [17,18]. Also, ARIMA models were used in anomalies detection for specific applications in industrial sectors like refinery [19] and network security [20].…”

Section: Introductionmentioning

confidence: 99%

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Predictive analytics for detecting sensor failure using autoregressive integrated moving average model

Thiyagarajan

Kodagoda

Nguyen

2017

2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)

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Abstract-Sensors play a vital role in monitoring the important parameters of critical infrastructure. Failure of such sensors causes destabilization to the entire system. In this regard, this paper proposes a predictive analytics solution for detecting the failure of a sensor that measures surface temperature from an urban sewer. The proposed approach incorporates a forecasting technique based on the past time series of sparse data using an ARIMA model. Based on the 95% forecast interval and continuity of faulty data, a criterion was set to detect anomalies and to issue a warning for sensor failure. Gaussian distribution was implemented on the forecasted and faulty data. By using the probability density of the distribution, the mean and variance were computed for faulty data to examine the abnormality in the variance value of each day to detect the sensor failure.

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“…Finally, the ARIMA(p,d,q) which is a generalization of ARMA(p,q) is mathematically defined as in (14)  …”

Section: Formulation Of Autoregressive Integrated Moving Average mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predictive analytics for detecting sensor failure using autoregressive integrated moving average model

Thiyagarajan

Kodagoda

Nguyen

2017

2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)

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“…It is used to quantify the goodness of fit of the model. When comparing two or more models, the one with the lowest AIC is generally considered to be closer with real data (Mondal et al, 2014;p.15).…”

Section: Graph 2 Time Series Plots Of the Variablesmentioning

confidence: 99%

Mevsimsel Değişkenliklerin Borsa İstanbul Üzerindeki Oynaklık Etkisi: Ramazan Ayı Örneği

Yılmaz

Ergün

2017

Adnan Menderes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi

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This study aims to investigate the volatility effect of seasonality during Muslim's holy month of Ramadan in Borsa Istanbul (BIST) at the basis of four main return indices of technology, service, industry and financial sector for the period 2000-2017. In the literature, it is emphasized that those moving calendar anomalies such as Ramadan may affect the volatility of stock returns due to Islamic beliefs of investors such as fasting, increase in religious rituals or increase in negative perception on speculative trading. In this study, Ramadan effect on stock returns and volatility are analysed by using dummy variables in regression and GARCH model respectively. An examination of trading data shows that average rates of return are unaffected during the month of Ramadan, and although it seems there is an increase in volatility, except in service sector it is not statistically significant. ÖZETBu çalışmanın amacı Borsa İstanbul'da işlem gören teknoloji, mali, hizmetler ve sanayi olmak üzere dört temel sektör bazındaki getiriler üzerinde Ramazan ayının herhangi bir oynaklık etkisi yaratıp yaratmadığını 2000-2017 dönemleri arasında incelemektir. Literatürde hisse senetleri üzerinde hareketli takvim etkisine dair çeşitli çalışmalar mevcut olup Ramazan ayının etkisi bu çerçevede incelenebilir, çünkü bu ayda oruç tutma, artan dini aktiviteler veya borsa işlemlerinin bir çeşit kumar olduğuna dair artan negatif algı etkili olabilmektedir. Bu çerçevede, Ramazan ayına ait olası etkileri araç değişken kullanıldığı regresyon ve GARCH modelleri ile tahmin edilmiştir. Bulgulara göre söz konusu ayda hisse senedi piyasası getirilerinin değişikliğe uğramadığı ve bu ayda oynaklığın arttığı gözlemlense de hizmet sektörü haricinde bulgular istatistiksel olarak anlamlı değildir.

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“…Autoregressive integrated moving average (ARIMA) models were proposed by Box and Jenkins (1970) for time series analysis and forecasting. Some studies have been conducted by employing ARIMA models to forecast stock market returns (Al-Shaib, 2006;Ojo and Olatayo, 2009;Adebiyi and Oluinka, 2014;Mondal et al, 2014). Quite a few studies found that ARIMA models produced inferior forecasts for financial time series data (Zhang, 2003;Adebiyi and Oluinka, 2014;Khandelwal et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of forecasting methods from selected stock market returns

Mallikarjuna

Rao

2019

Financ Innov

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Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification. There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making. Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices. However, there have been very few studies of groups of stock markets or indices. The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets. In this context, this study aimed to examine the predictive performance of linear, nonlinear, artificial intelligence, frequency domain, and hybrid models to find an appropriate model to forecast the stock returns of developed, emerging, and frontier markets. We considered the daily stock market returns of selected indices from developed, emerging, and frontier markets for the period 2000-2018 to evaluate the predictive performance of the above models. The results showed that no single model out of the five models could be applied uniformly to all markets. However, traditional linear and nonlinear models outperformed artificial intelligence and frequency domain models in providing accurate forecasts.

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Study of Effectiveness of Time Series Modeling (Arima) in Forecasting Stock Prices

Abstract: Stock price prediction has always attracted interest because of the direct financial

Cited by 177 publications

References 16 publications

Predictive analytics for detecting sensor failure using autoregressive integrated moving average model

Predictive analytics for detecting sensor failure using autoregressive integrated moving average model

Mevsimsel Değişkenliklerin Borsa İstanbul Üzerindeki Oynaklık Etkisi: Ramazan Ayı Örneği

Evaluation of forecasting methods from selected stock market returns

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