S&amp;P BSE Sensex and S&amp;P BSE IT return forecasting using ARIMA

Latha, Challa Madhavi; Malepati, Venkataramanaiah; Kolusu, Siva Nageswara Rao

doi:10.1186/s40854-020-00201-5

Cited by 35 publications

(22 citation statements)

References 69 publications

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“…The development of stock prices over time is very dynamic, complex and non-linear and can be predicted through many methods, one of which is the ARIMA model (Shi et al, 2012). For prediction, it is advisable to use the ARIMA model, because it uses and manages to calculate with time series data (Jiang and Subramanian, 2019) increasing returns, yet they gradually approach zero (Challa et al, 2020). The ARIMA method supplemented with the AdaBoost algorithm demonstrated the best results when determining the price movement of the Standard&Poor's 500 index (S&P 500).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Profitability of current investments in stock indexes

Kučera¹,

Kalinová²,

Divoká³

2022

JESI

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Investing in stock indexes has become an integral part of financial portfolios. Fearing the loss of savings value, households move their financial reserves into stocks, bonds and stock indexes; the latest concerns the subject of this article. The article focuses on determining the best method of forecasting the prices of stock indexes, S&P 500 and NASDAQ Composite, comparing the Simple moving average (SMA) technique with the Autoregressive integrated moving average (ARIMA) model based on free software RStudio. We formulated two plausible hypotheses: Which of these two methods -SMA or ARIMA -is more accurate for predicting the prices of selected stock indexes in the last thirty years? How will the price of the selected stock indexes develop according to the better of the suggested method through 2022? The ARIMA model indicated better results, proving great ability to forecast both indexes through 2022. The method determined the NASDAQ Composite stock index value to be 16,115.75 USD per stock through 31.12.2022, whereas S&P 500 index saw the value of 5,025.86 USD per stock through the same date. The follow-up research should deal with forecasting different stock indexes and comparing other conventional techniques for predicting time series. The subsequent study may also compare methods' forecasting accuracy between stock indexes and independent companies, whose stock volatility could favour different forecasting approaches.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Profitability of current investments in stock indexes

Kučera¹,

Kalinová²,

Divoká³

2022

JESI

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“…Researchers have also applied ensemble approaches for TS-forecasting and realized effective forecasting, as apparent from the pieces of literature [14−16]. It is also observed from the contemporary works that researchers have forecast several indices, e.g., stock index [17,18], consumer price index (CPI) [19,20], WPI [21,22] using diverse techniques.…”

Section: *Author For Correspondencementioning

confidence: 99%

“…Recent studies have observed that the popular ARIMA is used to forecast various economic as well as financial TS data successfully -such as the CPI of India [30], Jordan [19], Somaliland [20], Germany [31]; the stock index of Nigeria [17], India [18]; prices of potato [32,33], onion in India [32].…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting non-linear macroeconomic indexes of India: an ensemble of MLP and Holt’s linear methods

2022

IJATEE

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Electricity and electric-equipment play a critical role in modern living, and precise price forecasting for these items aids decision-makers in anticipating changes, planning, and budgeting ahead of time. This present research focused on the wholesale price indexes of items from the "manufacture of electrical equipment (MEEQ)" group of India's existing wholesale price index (WPI) series. This work proposed a novel, state-of-art ensemble forecast approach that used multilayer perceptron (MLP) and Holt's linear (HL) approaches for some specified WPIs from this group. The researchers selected the WPIs that manifest non-linearity as determined by the curve-fit technique. The paper applied a variance-based weighted average scheme to generate the ensemble forecast. The statistical rigor-based curve-fit aided in identifying that seventeen out of forty-eight indexes manifest non-linear fits. The proposed MLP-HL ensemble approach exhibited excellent forecast results (relative root mean squared error, i.e., RRMSE < 10%) for all seventeen WPIs. For each of these seventeen WPI's, the current work compared the proposed MLP-HL with nineteen models (eight statistical, four machine learning, and seven contemporary ensemble strategies). In the majority of the cases, it outran the nineteen models in terms of mean absolute error (MAE) and mean absolute percentage error (MAPE). As an alternative pathway for forecasting these seventeen non-linear WPIs, the present work suggests using this proposed MLP-HL approach.

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“…The most widely used statistical method is autoregressive integrated moving average (ARIMA), deployed by several studies to predict stock price trends. Challa et al (2020), for example, used the ARIMA model to predict the variation in returns of S&P BSE IT and S&P BSE Sensex indices of the Bombay Stock Exchange and found that the ARIMA model has an ability to predict long-or medium-term horizons by using historical observations. In a similar manner, stock prices of the Nigerian stock exchange and New York stock exchange were predicted by Ariyo et al (2014) using the ARIMA model, and they concluded that the ARIMA model has a vigorous predictability for short-term forecasting.…”

Section: Introductionmentioning

confidence: 99%

Stock Market Forecasting Using the Random Forest and Deep Neural Network Models Before and During the COVID-19 Period

Omar

Huang

Salameh

et al. 2022

Front. Environ. Sci.

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Stock market forecasting is considered the most challenging problem to solve for analysts. In the past 2 years, Covid-19 has severely affected stock markets globally, which, in turn, created a great problem for investors. The prime objective of this study is to use a machine learning model to effectively forecast stock index prices in three time frames: the whole period, the pre-Covid-19 period, and the Covid-19 period. The model accuracy testing results of mean absolute error, root mean square error, mean absolute percentage error, and r2 suggest that the proposed machine learning models autoregressive deep neural network (AR-DNN(1, 3, 10)), autoregressive deep neural network (AR-DNN(3, 3, 10)), and autoregressive random forest (AR-RF(1)) are the best forecasting models for stock index price forecasting for the whole period, for the pre-Covid-19 period, and during the Covid-19 period, respectively, under high stock price fluctuations compared to traditional time-series forecasting models such as autoregressive moving average models. In particular, AR-DNN(1, 3, 10) is suggested when the number of observations is large, whereas AR-RF(1) is suggested for a series with a low number of observations. Our study has a practical implication as they can be used by investors and policy makers in their investment decisions and in formulating financial decisions and policies, respectively.

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S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

Cited by 35 publications

References 69 publications

Profitability of current investments in stock indexes

Profitability of current investments in stock indexes

Forecasting non-linear macroeconomic indexes of India: an ensemble of MLP and Holt’s linear methods

Stock Market Forecasting Using the Random Forest and Deep Neural Network Models Before and During the COVID-19 Period

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