Dynamic Portfolio Optimization According to Market States and Diversification Using Hierarchical Clustering

The choice of assets and the state of the market are essential factors to consider in constructing an optimized portfolio for investments. Developments in the research of portfolio optimization techniques introduced models that consider factors such as market state and asset selection. We then explo...

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المؤلفون الرئيسيون: Malabaguio, Andrei Lanz, Pimentel, Ralph Luis Gabriel, Zaballero, Ana Maria Michaela, Go, Clark Kendrick C
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منشور في: Archīum Ateneo 2024
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الوصول للمادة أونلاين:https://archium.ateneo.edu/mathematics-faculty-pubs/298
https://doi.org/10.1063/5.0213407
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المؤسسة: Ateneo De Manila University
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spelling ph-ateneo-arc.mathematics-faculty-pubs-13012025-05-22T04:48:48Z Dynamic Portfolio Optimization According to Market States and Diversification Using Hierarchical Clustering Malabaguio, Andrei Lanz Pimentel, Ralph Luis Gabriel Zaballero, Ana Maria Michaela Go, Clark Kendrick C The choice of assets and the state of the market are essential factors to consider in constructing an optimized portfolio for investments. Developments in the research of portfolio optimization techniques introduced models that consider factors such as market state and asset selection. We then explore and integrate these portfolio optimization methods and techniques into our research. In this study, we propose a portfolio optimization method which optimizes a portfolio given the market state and selects assets based on asset correlation to ensure the diversification of assets in the portfolio. We use machine learning techniques in modelling market states and in the portfolio optimization process. Different market states have different statistical distributions since markets go through bull and bear states, where the value of assets in the market tend to rise or fall respectively. To model the non-stationary nature of the market, we cluster time periods and classify them into market states based on the statistical properties of the stock returns. Market states have varying statistical distributions of returns, so we train a portfolio optimizer in each market state. To optimize the portfolio, we use the Hierarchical Agglomerative Clustering method to group correlated assets together. Afterwards, we select lowly correlated assets with high historical Hierarchical Momentum to construct our portfolio. The constructed portfolio then had a higher Sharpe ratio and mean return than the 1n equally weighted portfolio with comparable variances when tested in sample. By selecting lowly correlated assets with high Sharpe ratios and considering the market state at a given investment period, we then show that a diverse portfolio with high returns can be generated with the proposed method. 2024-07-12T07:00:00Z text https://archium.ateneo.edu/mathematics-faculty-pubs/298 https://doi.org/10.1063/5.0213407 Mathematics Faculty Publications Archīum Ateneo Applied Mathematics Data Science Mathematics Statistics and Probability
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic Applied Mathematics
Data Science
Mathematics
Statistics and Probability
spellingShingle Applied Mathematics
Data Science
Mathematics
Statistics and Probability
Malabaguio, Andrei Lanz
Pimentel, Ralph Luis Gabriel
Zaballero, Ana Maria Michaela
Go, Clark Kendrick C
Dynamic Portfolio Optimization According to Market States and Diversification Using Hierarchical Clustering
description The choice of assets and the state of the market are essential factors to consider in constructing an optimized portfolio for investments. Developments in the research of portfolio optimization techniques introduced models that consider factors such as market state and asset selection. We then explore and integrate these portfolio optimization methods and techniques into our research. In this study, we propose a portfolio optimization method which optimizes a portfolio given the market state and selects assets based on asset correlation to ensure the diversification of assets in the portfolio. We use machine learning techniques in modelling market states and in the portfolio optimization process. Different market states have different statistical distributions since markets go through bull and bear states, where the value of assets in the market tend to rise or fall respectively. To model the non-stationary nature of the market, we cluster time periods and classify them into market states based on the statistical properties of the stock returns. Market states have varying statistical distributions of returns, so we train a portfolio optimizer in each market state. To optimize the portfolio, we use the Hierarchical Agglomerative Clustering method to group correlated assets together. Afterwards, we select lowly correlated assets with high historical Hierarchical Momentum to construct our portfolio. The constructed portfolio then had a higher Sharpe ratio and mean return than the 1n equally weighted portfolio with comparable variances when tested in sample. By selecting lowly correlated assets with high Sharpe ratios and considering the market state at a given investment period, we then show that a diverse portfolio with high returns can be generated with the proposed method.
format text
author Malabaguio, Andrei Lanz
Pimentel, Ralph Luis Gabriel
Zaballero, Ana Maria Michaela
Go, Clark Kendrick C
author_facet Malabaguio, Andrei Lanz
Pimentel, Ralph Luis Gabriel
Zaballero, Ana Maria Michaela
Go, Clark Kendrick C
author_sort Malabaguio, Andrei Lanz
title Dynamic Portfolio Optimization According to Market States and Diversification Using Hierarchical Clustering
title_short Dynamic Portfolio Optimization According to Market States and Diversification Using Hierarchical Clustering
title_full Dynamic Portfolio Optimization According to Market States and Diversification Using Hierarchical Clustering
title_fullStr Dynamic Portfolio Optimization According to Market States and Diversification Using Hierarchical Clustering
title_full_unstemmed Dynamic Portfolio Optimization According to Market States and Diversification Using Hierarchical Clustering
title_sort dynamic portfolio optimization according to market states and diversification using hierarchical clustering
publisher Archīum Ateneo
publishDate 2024
url https://archium.ateneo.edu/mathematics-faculty-pubs/298
https://doi.org/10.1063/5.0213407
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