Research on the Development of Investment Portfolio Theory

This paper provides a comprehensive analysis of portfolio theory evolution and its practical applications, tracing its development since Harry Markowitz introduced Modern Portfolio Theory (MPT) in 1952. The urgency to enhance capital market efficiency, optimize asset portfolios, and effectively measure and manage risks has intensified due to the rapid transmission of risk across global financial markets. This study reviews historical advancements and recent achievements in portfolio theory and practice. Furthermore, it constructs a theoretical research framework to guide the future development of quantitative investment strategies, focusing on active portfolio management. This framework also delineates several prospective research avenues. The advent of artificial intelligence and big data has transformed portfolio theory into a multidisciplinary nexus involving mathematical statistics, machine learning, and behavioral finance. This integration is crucial for refining asset portfolio optimization and understanding the dynamics of risk spillover and its determinants. The findings and theoretical advancements discussed herein are vitally important for fostering the sustainable development of capital markets and ensuring economic and financial stability. This study not only revisits foundational theories but also sets the stage for pioneering future research in financial analysis and portfolio management.

Authors

Publication Details

Journal
Highlights in Business Economics and Management
Published
2024-09-01
DOI
https://doi.org/10.54097/h6kdjv16
Citations
1
Primary Topic
Stock Market Forecasting Methods
Type
article
Field-Weighted Citation Impact
0.49
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on the Development of Investment Portfolio Theory

Xiaoyu Li
1 citations
Highlights in Business Economics and Management
Stock Market Forecasting Methods
0.49
article

Research on the Development of Investment Portfolio Theory

Xiaoyu Li
article en
1 citations

Abstract

This paper provides a comprehensive analysis of portfolio theory evolution and its practical applications, tracing its development since Harry Markowitz introduced Modern Portfolio Theory (MPT) in 1952. The urgency to enhance capital market efficiency, optimize asset portfolios, and effectively measure and manage risks has intensified due to the rapid transmission of risk across global financial markets. This study reviews historical advancements and recent achievements in portfolio theory and practice. Furthermore, it constructs a theoretical research framework to guide the future development of quantitative investment strategies, focusing on active portfolio management. This framework also delineates several prospective research avenues. The advent of artificial intelligence and big data has transformed portfolio theory into a multidisciplinary nexus involving mathematical statistics, machine learning, and behavioral finance. This integration is crucial for refining asset portfolio optimization and understanding the dynamics of risk spillover and its determinants. The findings and theoretical advancements discussed herein are vitally important for fostering the sustainable development of capital markets and ensuring economic and financial stability. This study not only revisits foundational theories but also sets the stage for pioneering future research in financial analysis and portfolio management.

Highlights in Business Economics and ManagementVol. 40
Openalex Percentile: Top 31%
Stock Market Forecasting Methods
0.49
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.