Portfolio Construction Research Articles

A Study on Optimal Equity Portfolio in the Indian Capital Market

This study evaluates optimal equity portfolio construction in the Indian capital market by applying the Sharpe Single Index Model. Data from 20 companies in the NIFTY 50 index are used to analyze risk-return relationships and identify companies for portfolio inclusion. The study concludes that constructing an optimal portfolio with selected stocks, such as Hero MotoCorp and Bharat Petroleum, based on their risk-adjusted returns, offers a strategy for maximizing returns with controlled risk

Predicting Portfolios Using Fuzzy Logic and Optimization Techniques: A Case Study of BSE-IT Listed Companies

Stock price trends were considered in this study, the companies in the BSE-IT index, with a view to optimizing portfolio allocation based on risk tolerance. The GARCH model has been applied here to allow an investigation into the volatility of the stock returns and to set up different clusters using K-Means clustering that group together companies owning the same attributes related to volatility for strategic classification. Data collection and processing occur between April 1, 2014, and March 31, 2024, for reasons of reliability and relevance. Descriptive statistics report on the variance of stock performance across companies; different risk-return profiles are thus exposed. The GARCH model measures each stock's long-term variance, along with sensitivity to past shocks, and overall volatility persistence, making this an excellent inventory for risk assessment. K-Means clustering groups companies based on similar volatility patterns, thereby informing portfolio construction. Findings present how clustering with econometric modeling may assist in the production of optimal portfolios that depend on specified risk capacities, thus efficiently managing and implementing an IT investment strategy.

Leveraging enhanced egret swarm optimization algorithm and artificial intelligence-driven prompt strategies for portfolio selection

In the financial field, constructing efficient investment portfolios is a focal point of research, encompassing asset selection and optimization of asset allocation. With the advancements in Large Language Models (LLMs), generative Artificial Intelligence (AI) tools have showcased capabilities never seen before. However, the black-box nature of these tools renders their outputs difficult to interpret directly, often necessitating iterative fine-tuning to align with users’ expected outcomes. This study presents a structured prompt framework specifically designed for stock selection, aiming to provide direct and interpretable stock-selecting tools for investors of various levels. By creating representative scenarios and combining them into different cases for experimentation, we can explore how the construction of prompts influences the responses generated by generative AI tools. Additionally, this paper proposes a novel algorithm that combines the Nonlinear-Activated Beetle Antennae Search strategy with the Egret Swarm Optimization Algorithm (NBESOA) to address the Mean-Variance Portfolio Selection problem with Transaction Costs and Cardinality Constraints (MVPS-TCCC), utilizing real stock market data to construct portfolios based on generative AI tools recommendations. Simulation results indicate that, compared to other algorithms, NBESOA prefers optimizing portfolio configurations to achieve the highest Sharpe Ratio with the strictest constraints, bringing the outcomes closer to the portfolio’s efficient frontier.

Analysis of Portfolio Optimization Performance: Markowitz Model and Index Model in Capital Markets

In the post-pandemic era, marked by economic uncertainty and stock market volatility, investors are turning to Modern Portfolio Theory (MPT) to guide their investment decisions. This study aims to develop a robust mathematical framework for portfolio construction using the Markowitz Model (MM) and the Index Model (IM) with added constraints. The objectives are threefold: (1) to create a portfolio framework that reflects investor preferences using MM and IM with additional constraints, (2) to compare these models against the Gaussian Distribution using Python and Excel, (3) to compare the statistical data and correlation tests of daily logarithmic returns in Python with monthly excess returns in Excel, and (4) to evaluate their performance relative to the traditional Markowitz approach through Monte Carlo simulations.The methodology involves incorporating five additional constraints into the MM and IM models. The analysis uses 20 years of historical daily return data for ten stocks from various sectors, one equity index (S&P 500) as a risk-free rate proxy (1-month Fed Funds rate). Daily logarithmic returns are analyzed using Python, while Excel Solver and Solver Table are employed for monthly excess return data.

Modelling Multivariate Return Volatilities by Independent Components Using Johnson SU Likelihood

Time-varying volatility modelling is crucial in financial econometrics. For multivariate data, time-varying correlations across securities are important to explain the co-movement of the data. Therefore, we need to estimate the covariance matrix. Estimating a large conditional covariance matrix requires the estimation of a large number of parameters, which is a hard task and often leads to convergence difficulties in the estimation algorithm. We introduce a framework for forecasting the conditional covariance matrix of a large data set by employing a factor model to reduce dimensionality, with the factors estimated using independent components. The main contribution of our article is the estimation of the unobserved independent components using Johnson SU likelihood, which captures the skewness and excess kurtosis—stylized facts in stock returns. First, we extract the independent components using the Johnson SU distribution, maximizing their non-Gaussianity. Second, we sorted the independent components in terms of their explained variability and fit a univariate GARCH process on a few common independent components. Next, we approximate the conditional covariance matrix of the data through a linear combination of the conditional variances of those select few independent components. We apply this procedure to 50 stock returns data (listed in Nifty 50) that exhibit excess kurtosis and skewness, and the findings show that our methodology dominates other algorithms in extracting the independent components. This method enables fund managers to accurately forecast a large conditional covariance matrix, facilitating effective risk management and portfolio construction.

The prescriptive nature of market timing and predictive portfolios

Abstract We explore the ability of market timing, model-based forecasts that include incremental information over and above momentum, to create long-short portfolios that consistently provide superior returns to traditional benchmarks, and frame our analysis in the idea of “prediction-led prescription” for forward-looking decision making. Our methodology consists of linear and non-linear forecasts with model selection, that predict the direction of future returns and apply then into the construction of portfolios. Our results strongly recommend that predictive-based portfolio allocation can produce excess returns and higher Sharpe ratios, above and beyond the individual return series that are utilized to create the forecasts and traditional portfolio benchmarks. The use of “predictive methods in portfolio construction reinforces the idea of prediction-led prescription” as portfolios are prescriptions for investment management and have an exclusively forward-looking nature.

Hidden neighbours: extracting industry momentum from stock networks

This paper introduces an innovative method for constructing industry momentum portfolios by leveraging two stock networks: one based on stock price correlations and the other on corporate text similarity. We find that these networks capture different aspects of company relationships, motivating us to combine them and form a portfolio that exploits less visible industry momentum. Our Hidden Neighbours portfolio, analysed from 2013 to 2022, delivered an annualised return of 18.16% with a Sharpe ratio of 0.85, outperforming the S&P 500 and other traditional momentum strategies. Factor decomposition attributes returns primarily to the idiosyncratic factor α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document}. Our study employs interdisciplinary methods, merging network analysis and Natural Language Processing (NLP) techniques for portfolio construction. Utilising advanced text embedding models, we enhance portfolio construction by integrating textual insights from corporate disclosures into stock networks. The paper offers a comprehensive strategy across diverse data and the interdisciplinary approach, uniting financial theory, network science, and NLP, advances both theory and practice of portfolio management.

Developing a Competitive Edge: Building an Effective Portfolio in Snowflake and Teradata Data Engineering

This article examines the critical components of an effective portfolio for data engineering professionals specializing in Snowflake and Teradata platforms. As the data landscape evolves, the ability to showcase practical skills alongside theoretical knowledge has become paramount for career advancement. Through an analysis of industry trends and expert interviews, we identify key elements that contribute to a compelling portfolio, including project showcases demonstrating complex problem-solving, relevant certifications, and evidence of continuous learning. The article highlights the importance of balancing technical proficiency in SQL, data warehousing, and cloud technologies with strong documentation and communication skills. Our findings suggest that a well-curated portfolio not only validates a candidate's expertise but also provides tangible evidence of their ability to navigate the challenges of modern data ecosystems. This article provides a framework for data engineers to construct portfolios that effectively demonstrate their capabilities, potentially increasing their competitiveness in the job market and facilitating career growth in the rapidly evolving field of data engineering.

A Hierarchical Approach to a Tri-Objective Portfolio Optimization Problem Considering an ESG Index

Traditional portfolio construction primarily revolves around a bi-objective approach, focusing on minimizing portfolio variance while maximizing expected returns. However, this approach leaves out other objectives that could interest decision makers. In this work, we incorporate an extra objective, namely the environmental, social, and governance index (ESG), as a secondary objective. This addition empowers investors to customize their portfolios by defining explicit trade-off thresholds between expected returns and risk, considering the ESG index. To achieve this goal, we make use of external archiving techniques and evolutionary algorithms. In particular, we first find approximate solutions to the bi-objective problem; then, we look for efficient solutions for ESG. We tested our approach with data on the Dow Jones, S&P500, and Nasdaq100 from Yahoo Finance. The results show that the proposed methodology can identify portfolios with good returns and risks considering ESG.

Adaptive Risk Parity Strategies for Managing Portfolio Risk in Volatile Markets

This paper presents a novel model for Adaptive Risk Parity Portfolio Construction, designed to dynamically adjust portfolio weights based on changing market conditions and economic regimes. Traditional portfolio strategies, such as Modern Portfolio Theory (MPT), often rely on static assumptions that can lead to suboptimal performance in volatile markets. In contrast, the adaptive risk parity approach aims to equalize risk contributions across asset classes while responding to fluctuations in economic indicators, such as GDP growth and inflation rates. By employing techniques such as Markov switching models and machine learning for regime identification, the proposed model allows for real-time adjustments to asset allocations. The back-testing results demonstrate that the adaptive risk parity portfolio outperforms static risk parity and traditional mean-variance optimized portfolios in terms of risk-adjusted returns and drawdown management. This research contributes to the field of portfolio management by providing a framework that enhances resilience and adaptability in investment strategies.

A two-step method for assessing enhanced value in turnaround, spin-off, and value stocks

To assess outright and relative value opportunities in stocks and benchmark their performance against an index with global relevance, it is important to achieve and measure risk-adjusted excess returns. Academic and corporate research has focused quite extensively on analyzing stock returns and comparing the outperformance of specific investment strategies, with value investing being one of the most prominent and longest-known factor strategies. In this event study, to test for the existence of risk-adjusted excess returns, or alpha, a novel two-step approach is proposed to assess Enhanced Value in single stocks for three different investment approaches: plain value investing, investing in spin-offs, and investing in turnaround companies. While the first step of the two-step approach screens companies for a combination of financial company characteristics, the second step ranks and sorts them by either their price-earnings ratio or by their price-book ratio, thus “enhancing” the value assessment. Their short- and mid-term stock performance is investigated for an investment horizon of one year, three years, and five years. Stocks of value companies, spin-offs, and turnaround companies outperform the S&P 500 benchmark on average and on a risk-adjusted basis for all three investment horizons when tested for Enhanced Value with the novel two-step approach. The analysis results provide deeper insights into how the value factor in its different characteristics needs to be understood in the context of investment strategies and how it potentially can be applied to stock selection and portfolio construction, resulting in investment strategies showing a risk-adjusted outperformance.

Risk and Return Analysis of Stocks of Nepalese Commercial Banks

This research article analyzes the impact of risk and return of Nepalese commercial bank stocks on portfolio returns to assist investors in making informed investment decisions. It aims to examine the effects of individual bank stock returns, risks, market return, and market risk (beta) on overall portfolio performance. The study utilizes a quantitative approach, employing descriptive statistics, comparative (trend) analysis, correlation analysis, and hypothesis testing. Secondary data was collected from 18 commercial banks in Nepal, including post-merger banks, covering the fiscal years from 2015/16 to 2021/22. Statistical tools such as SPSS and EViews were used for analysis. The findings indicate that investors seeking higher returns might consider Prabhu Bank, which offers high mean returns but also comes with higher volatility. In contrast, risk-averse investors may prefer banks like NMB and Nabil, which provide lower mean returns but more stable performance. Notably, this research contributes to the understanding of the risk-return relationship in the context of 18 Nepalese commercial banks post-merger, providing new insights for investors. This study highlights the importance of analyzing individual bank stocks and their associated risks for portfolio construction. By understanding the dynamics of risk and return, investors can optimize their investment strategies and enhance portfolio performance. Overall, this research fills a gap in existing literature by focusing on the Nepalese commercial banking sector and its implications for investor decision-making. The insights gained from this study can guide investors in navigating the complexities of the stock market, ultimately leading to more informed and strategic investment choices. Future research could expand on this work by exploring additional factors influencing stock performance and investor behavior in the Nepalese context.

Incorporating Different Sources of Information for Bayesian Optimal Portfolio Selection

This article introduces Bayesian inference procedures for tangency portfolios, with a primary focus on deriving a new conjugate prior for portfolio weights. This approach not only enables direct inference about the weights but also seamlessly integrates additional information into the prior specification. Specifically, it automatically incorporates high-frequency returns and a market condition metric (MCM), exemplified by the CBOE Volatility Index (VIX) and Economic Policy Uncertainty Index (EPU), significantly enhancing the decision-making process for optimal portfolio construction. While the Jeffreys’ prior is also acknowledged, emphasis is placed on the advantages and practical applications of the conjugate prior. An extensive empirical study reveals that our method, leveraging this conjugate prior, consistently outperforms existing trading strategies in the majority of examined cases.

Gender(ed) equity: The growth of female shareholding in Australia, 1857–1937

AbstractLittle is known about the history of female investing in Australia. We develop a new dataset of company shareholders between 1857 and 1937, covering all major sectors of the economy. We calculate the female fraction of shareowners, their occupational and geographic backgrounds, and we analyse their investment patterns and behaviours including their risk profiles and portfolio construction decisions. Our findings suggest that ‘gender equity’—and more—had been reached, for some companies, by the interwar period. Women investors came from many walks of life, had various motives, and appear to have largely acted independently of other women and of men.

A Study of Hierarchical Risk Parity in Portfolio Construction

<p style="text-align: justify;"><span style="font-family: 'times new roman', times, serif; font-size: 14pt;">The construction and optimization of a portfolio is a complex process that has been a historically active research area in finance. For portfolios with highly correlated assets, the performance of traditional risk-based asset allocations methods such as the mean-variance (MV) model is limited when numerous assets are correlated. A novel clustering-based asset allocation method, called Hierarchical Risk Parity (HRP), provides an opportunity to mitigate these limitations in portfolio construction. HRP utilizes the hierarchical relationships between the covariance of assets in a portfolio to determine weight distributions, eliminating the need for the inversion of the covariance matrix that is required by most traditional risk-based asset allocation methods. This research examines the viability of Hierarchical Risk Parity (HRP) method in portfolio construction of a US equity portfolio and compares the performances of HRP to traditional asset allocation methods exemplified by the mean-variance (MV) method. The results of this research show that the performance of the HRP method is comparable to the performance of the MV method. Given these findings, HRP provides an advantageous approach for portfolio construction in practical scenarios where correlated assets are present in the portfolios.</span></p>

Portfolio Optimization Using LSTM for Five Selected Stocks

Portfolio construction is very critical in the financial markets as it assists investors in achieving maximum returns with minimum risks through diversification. In this paper, Long Short-Term Memory (LSTM) models are employed to predict stock prices and optimize investment portfolios. Historical adjusted closing prices from Yahoo Finance for the period from January 1, 2023, to March 1, 2024, were used to predict the future stock prices of Apple Inc. (AAPL), Microsoft Corporation (MSFT), BlackRock Inc. (BLK), JPMorgan Chase & Co. (JPM), and Tesla Inc. (TSLA) using LSTM models. Portfolios were constructed using mean-variance optimization based on these predictions, and Monte Carlo simulations were employed to capture the uncertainty around the estimates. The results are treasured for investors in financial markets as they spotlight the limitations and capability risks of depending totally on gadget learning predictions for portfolio optimization. This emphasizes the want for a greater comprehensive method to investment decision-making that considers more than one factors and methodologies.

Portfolio Optimization Based on Rolling Window Using 5 Stocks

This paper aims to explore a method of constructing dynamically adjusted investment portfolios through the combination of rolling windows and mean-variance models. The construction of investment portfolios plays a crucial role in financial markets by effectively diversifying risks and achieving stable investment returns. This study analyzed adjusted closing price data from five stocks (AAPL, JPM, JNJ, XOM, and PG), sourced from Yahoo Finance. The rolling window method was employed to predict future stock prices and construct investment portfolios based on these predictions. This study calculated annualized average return and covariance matrix for each window. The mean-variance model and optimization algorithms were then used to determine the optimal weights for daily portfolio composition. The results demonstrate the high accuracy of the rolling window method in predicting short-term stock prices. Notably, Procter & Gamble (PG) and Johnson & Johnson (JNJ) exhibited the lowest prediction errors. Investment portfolios based on rolling window predictions performed well over a certain period. Nevertheless, the S&P 500 index did not show a substantial disadvantage compared to their long-term performance. This research provides dynamic portfolio optimization methods of practical value for investors in financial markets.

Integrating LSTM Networks with Mean-Variance Optimization for Enhanced Portfolio Construction: An Empirical Study on UK Stock Market

Portfolio optimization has long been a central theme in finance. With ongoing advancements in machine learning, there is a significant opportunity to integrate predictive methods into portfolio optimization. This paper proposes leveraging Long Short-Term Memory (LSTM) networks alongside the established Mean-Variance (MV) optimization framework to construct optimal portfolios. These portfolios aim to help financial investors effectively manage and mitigate risk while maximizing returns. The study meticulously screened the leading stocks of 12 prominent UK companies listed on the London Stock Exchange (LSE), known for their influence and visibility. Initially, the study applies the LSTM networks to predict stock price volatility and integrates these predictions into the MV model to allocate portfolio weights effectively. To underscore the superiority of the proposed approach, the study compares cumulative returns from portfolios optimized for maximum and minimum variance Sharpe ratios with real data against the FTSE100 index over the same period. The results demonstrate that the portfolio optimized with the maximum Sharpe ratio significantly outperforms both the conservative minimum variance portfolio and the FTSE100 index. This empirical evidence underscores the practical value of integrating advanced machine learning techniques with established financial theory, enabling more innovative and efficient investment strategies to meet the evolving needs of financial investors.

Enhancing Portfolio Construction with Synthetic Data

Enhancing Portfolio Construction with Synthetic Data

The craft of portfolio construction in estate planning: A comprehensive review on equity and mutual fund strategies, and its risks

The craft of portfolio construction in estate planning: A comprehensive review on equity and mutual fund strategies, and its risks