Predictive power of ARIMA models in forecasting equity returns: a sliding window method

Dong, Huijian; Guo, Xiao; Reichgelt, Han; Hu, Ruizhi

doi:10.1057/s41260-020-00184-z

Cited by 11 publications

(5 citation statements)

References 27 publications

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“…However, Dong et al [34] argued that many of the failures of ARIMA could be avoided by using a sliding window approach. They concluded that the lack of non-linearity capture by ARIMA is not a concern as long as the sliding window of training is carefully selected.…”

Section: Methodsmentioning

confidence: 99%

A Comparative Study for Using Deep LSTMs and ARIMA for Imputing Missing Data for Wind Data in the Irish Sea

Shoukat,

Malekjafarian,

Pakrashi

2024

Preprint

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Achieving Net Zero emissions target is driving up the need for offshore wind as an alternate energy source. Ireland has the capacity to produce upwards of 30GWof power through offshore wind alone. Wind resource assessments are extremely vital in determining the long term trends of a site. The instruments used for this often suffer breakdowns or miss readings which impacts the long term trend analysis. Wind time series data from Ireland’s Marine Institute is used which is shown to have significant gaps in it’s 20 years of service. Data imputation is necessary to fill in these gaps as accurately as possible. This paper compares LSTMs and ARIMA as two competing methodologies for data imputation. LSTMs are shown to be marginally superior to ARIMA imputation with a mean squared error of 0.45 compared to that of ARIMA of 0.60.

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Section: Methodsmentioning

confidence: 99%

A Comparative Study for Using Deep LSTMs and ARIMA for Imputing Missing Data for Wind Data in the Irish Sea

Shoukat,

Malekjafarian,

Pakrashi

2024

Preprint

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“…Apart from machine learning, compelling research in the real estate market domain is evident in the application of artificial intelligence (Kabaivanov and Markowska, 2021), specifically artificial neural networks (Peter et al, 2020;Yasnitsky et al, 2021), and genetic algorithms (Del Giudice et al, 2017;Sun 2019). In terms of real estate market analysis, it is crucial to acknowledge studies conducted employing the ARIMA method (Dong et al, 2020;Jadevicius et al, 2015;Tse, 1997).…”

Section: The Origin Of Harmonic Fluctuations In the Real Estate Marketmentioning

confidence: 99%

The Identification of Seasonality in the Housing Market Using the X13-ARIMA-SEATS Model

Mach,

Dąbrowski,

Wotzka

et al. 2023

Econometrics

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Aim: In the conducted research, profiles of seasonality in the housing market were determined, which provided an opportunity to answer two fundamental questions: what is the nature of harmonic variation in the seasonality and periodicity of the studied components of the construction process? what parameters of the ARIMA model optimally describe the construction market? Methodology: In the conducted research, using the X13-ARIMA-SEATS model, seasonal decomposition was carried out in the various stages of the housing construction process. Results: The research process conducted to identify seasonal fluctuations in the housing construction market showed that harmonic fluctuation profiles can be identified on an annual basis. An analysis of seasonal fluctuations was carried out for each of the three stages of the housing construction process, while also checking how these profiles function for Poland in general, and for individual investors, and for those building apartments for sale or to rent. The study showed that the market for real estate development activity differs in its seasonal characteristics from that of individual investors. Implications and recommendations: The conclusions obtained from the research can provide support in the decision-making process, both from a macro and microeconomic perspective. Parameterisation of the occurring fluctuations, and taking them into account in the process of developing a forecast can provide decision-making rationale in the implementation of macroprudential and financial stability policies Originality/Value: A novelty is in the demonstration that the residential real estate market in Poland shows different seasonal parameters, divided into the market of individual investors and investors who build apartments for sale or rent.

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“…Price prediction is one of the prevailing areas of price analysis. Researchers often employ ARIMA and ARDL models, two powerful forecasting techniques in price analysis in many areas (Dong et al, 2020;Sun et al, 2017). They can forecast future trends based on existing data.…”

Section: Literature Reviewmentioning

confidence: 99%

Food price dynamics and regional clusters: machine learning analysis of egg prices in China

Chang

Zhou

Höschle

et al. 2022

CAER

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PurposeThe study uses machine learning techniques to cluster regional retail egg prices after 2000 in China. Furthermore, it combines machine learning results with econometric models to study determinants of cluster affiliation. Eggs are an inexpensiv, nutritious and sustainable animal food. Contextually, China is the largest country in the world in terms of both egg production and consumption. Regional clustering can help governments to imporve the precision of price policies and help producers make better investment decisions. The results are purely driven by data.Design/methodology/approachThe study introduces dynamic time warping (DTW) algorithm which takes into account time series properties to analyze provincial egg prices in China. The results are compared with several other algorithms, such as TADPole. DTW is superior, though it is computationally expensive. After the clustering, a multinomial logit model is run to study the determinants of cluster affiliation.FindingsThe study identified three clusters. The first cluster including 12 provinces and the second cluster including 2 provinces are the main egg production provinces and their neighboring provinces in China. The third cluster is mainly egg importing regions. Clusters 1 and 2 have higher price volatility. The authors confirm that due to transaction costs, the importing areas may have less price volatility.Practical implicationsThe machine learning techniques could help governments make more precise policies and help producers make better investment decisions.Originality/valueThis is the first paper to use machine learning techniques to cluster food prices. It also combines machine learning and econometric models to better study price dynamics.

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Predictive power of ARIMA models in forecasting equity returns: a sliding window method

Cited by 11 publications

References 27 publications

A Comparative Study for Using Deep LSTMs and ARIMA for Imputing Missing Data for Wind Data in the Irish Sea

A Comparative Study for Using Deep LSTMs and ARIMA for Imputing Missing Data for Wind Data in the Irish Sea

The Identification of Seasonality in the Housing Market Using the X13-ARIMA-SEATS Model

Food price dynamics and regional clusters: machine learning analysis of egg prices in China

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