Predictability of HK-REITs returns using artificial neural network

Loo, Wei Kang

doi:10.1108/jpif-07-2019-0090

“…In a similar way, [14] applied multivariate, ML-based regression algorithms (including Neural Networks) to predict REIT returns. Other authors compared ML algorithms to ARIMA for the prediction of REIT returns [15,16,17]. Such works focused mainly on artificial neural networks relying on multiple variables.…”

Section: Related Workmentioning

confidence: 99%

Improving REITs Time Series Prediction Using ML and Technical Analysis Indicators

Habbab

¹

,

Kampouridis

²

,

Papastylianou

³

2023

2023 International Joint Conference on Neural Networks (IJCNN)

0

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One of the most popular ways to reduce the risk of an investment portfolio is by holding shares of Real Estate Investment Trusts (REITs), which own and manage real estate. An important aspect of this process is to be able to forecast future REITs prices, as this allows investors to achieve higher returns at lower risk. This paper examines the performance of five different machine learning algorithms in the task of REITs price forecasting: Ordinary Least Squares Linear Regression, Support Vector Regression, k-Nearest Neighbours Regression, Extreme Gradient Boosting, and Long/Short-Term Memory Neural Networks. In addition to past REITs prices, we also use Technical Analysis indicators to assist the algorithms in the task of price prediction. While such indicators are very popular in stocks forecasting, they have never been used to forecast REITs. Our experiments show that (i) all ML algorithms produce low error and standard deviation, and are able to outperform the well-known statistical benchmark of AutoRegressive Integrated Moving Average (ARIMA), and (ii) the introduction of Technical Analysis (TA) indicators into the feature set leads to an error reduction of up to 50%.

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“…One technique that is often used in advanced linear regression is linear regression with regularization [23]- [25]. Regularization is an approach that involves a penalty to the regression coefficients to prevent overfitting and improve model generalization.…”

Section: Introductionmentioning

confidence: 99%

Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting

Wahyuningsih,

Iriani,

Dwi Purnomo

et al. 2024

Comput. Sci. Inf. Technol.

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This research employs a hybrid approach, integrating advanced linear regression and extreme gradient boosting (XGBoost), to forecast student success rates in exams within the dynamic educational landscape. Utilizing Kaggle-sourced data, the study crafts a model amalgamating advanced linear regression and XGBoost, subsequently assessing its performance against the primary dataset. The findings showcase the model's efficacy, yielding an accuracy of 0.680 on the fifth test and underscoring its adeptness in predicting students' exam success. The discussion underscores XGBoost's prowess in managing data intricacies and non-linear features, complemented by advanced linear regression offering valuable coefficient interpretations for linear relationships. This research innovatively contributes by harmonizing two distinct methods to create a predictive model for students' exam success. The conclusion emphasizes the merits of an ensemble approach in refining prediction accuracy, recognizing, however, the study's limitations in terms of dataset constraints and external factors. In essence, this study enhances comprehension of predicting student success, offering educators insights to identify and support potentially struggling students.

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“…However, in some cases, simple linear regression may not be robust enough to cope with higher data complexity. In this context, the concept of "advanced linear regression" emerges which refers to the use of additional techniques to improve the performance of linear regression [23]- [25].…”

Section: Introductionmentioning

confidence: 99%

Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting

Wahyuningsih,

Iriani,

Purnomo

et al. 2024

Comput Sci Inf Technol

1

0

View full text Add to dashboard Cite

This research employs a hybrid approach, integrating advanced linear regression and extreme gradient boosting (XGBoost), to forecast student success rates in exams within the dynamic educational landscape. Utilizing Kaggle-sourced data, the study crafts a model amalgamating advanced linear regression and XGBoost, subsequently assessing its performance against the primary dataset. The findings showcase the model's efficacy, yielding an accuracy of 0.680 on the fifth test and underscoring its adeptness in predicting students' exam success. The discussion underscores XGBoost's prowess in managing data intricacies and non-linear features, complemented by advanced linear regression offering valuable coefficient interpretations for linear relationships. This research innovatively contributes by harmonizing two distinct methods to create a predictive model for students' exam success. The conclusion emphasizes the merits of an ensemble approach in refining prediction accuracy, recognizing, however, the study's limitations in terms of dataset constraints and external factors. In essence, this study enhances comprehension of predicting student success, offering educators insights to identify and support potentially struggling students.

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Predictability of HK-REITs returns using artificial neural network

Cited by 13 publications

References 39 publications

Improving REITs Time Series Prediction Using ML and Technical Analysis Indicators

Improving REITs Time Series Prediction Using ML and Technical Analysis Indicators

Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting

Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting

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