Construction of Optimal Stock Market Portfolios Using Outlier Detection Algorithm

Tong, Gee Kok; Ng, Keng Hoong; Yap, Wun‐She; Khor, Kok-Chin

doi:10.1007/978-981-16-7334-4_12

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…The aforementioned problems highlight the necessity of implementing strategic planning, expanding the economy, and effectively managing building supplies in order to minimise delays and cost overruns. The knowledge obtained from this analysis can provide valuable guidance for policy-making, project management approaches, and future research endeavours, therefore enhancing the resilience and sustainability of the construction industry in Kuwait and comparable economies [14,15]. This research paper is strategically designed to delve into the complexities of the Kuwait Construction Market, utilising data sourced from The Public Authority for Civil Information and the Ministry of Justice to underpin our analysis.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Kuwait Construction Market Data Using Autoencoder Neural Networks

Al-Sabah,

Anbarjafari

2024

Preprint

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In the ambitiously evolving construction industry of Kuwait, characterized by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. This research paper introduces a groundbreaking deep learning approach utilising an autoencoder neural network to analyse the complexities of the Kuwait Construction Market and identify data irregularities. The construction sector’s significant investment influx and project expansion make it an ideal candidate for deploying sophisticated analytical techniques to detect anomalous patterns indicating inefficiencies or unveiling potential opportunities. Our approach leverages the capabilities of autoencoder architectures to delve into and understand the prevalent patterns in market behaviours. This meticulous analysis allows for the detection of deviations that may lead to operational or financial consequences. We elucidate the mathematical foundations of autoencoders, highlighting their proficiency in managing the complex, multidimensional data typical of the construction industry. Through training on an extensive dataset—comprising variables like market sizes, investment distributions, and project completions—our model demonstrates its ability to pinpoint subtle yet significant anomalies. The outcomes of this study enhance our understanding of deep learning’s pivotal role in construction and building management. Additionally, our findings proactively sketch the future market trajectory and facilitate anomaly detection, thereby contributing substantially to the academic discussion on integrating machine learning into construction management. This research offers valuable insights for professionals within Kuwait, aiming to harness deep learning techniques to boost operational efficiency and strategic foresight in the construction sector.

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Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Kuwait Construction Market Data Using Autoencoder Neural Networks

Al-Sabah,

Anbarjafari

2024

Preprint

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“…The method based on the Local Outlier Factor constructed some optimal portfolios in the stock market for stock investors [6].…”

Section: Introductionmentioning

confidence: 99%

Portfolio Construction Based on the Modern Portfolio Theory Including Gold, Crude Oil and Cryptocurrency

Wang¹

2023

BCPBM

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Finding and optimizing the best investment strategies is one of the most important skills in financial markets. However, the research on the construction of different types of investment portfolios including stocks, goods, and cryptocurrency is not perfect enough. This article constructs an optimal portfolio utilized Modern Portfolio Theory and Sharpe ratio. Based on the price data of two stocks in China Securities Index 300, three stocks in Standard and Poor's 500, Gold, Crude Oil and Bitcoin, the portfolio of 8 kinds of price data are simulated and calculated. A variety of optimal investment portfolios have been constructed, including the minimum risk investment portfolio and the highest Sharpe ratio investment portfolio. In addition, by setting the lower limit of the expected rate of return, the lowest-risk investment portfolio with customized expected rate of returns is obtained. Nevertheless, an attempt was made in the article to construct investment strategies for different types of investment projects. These results shed light on guiding further exploration of portfolio construction with different type of stock or goods.

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Analysis and Prediction of Stock Market Movements using Machine learning

Bhardwaj

Rahul

Kumar

et al. 2022

2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA)

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Construction of Optimal Stock Market Portfolios Using Outlier Detection Algorithm

Cited by 3 publications

References 17 publications

Anomaly Detection in Kuwait Construction Market Data Using Autoencoder Neural Networks

Anomaly Detection in Kuwait Construction Market Data Using Autoencoder Neural Networks

Portfolio Construction Based on the Modern Portfolio Theory Including Gold, Crude Oil and Cryptocurrency

Analysis and Prediction of Stock Market Movements using Machine learning

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