Portfolio Theory Research Articles

Optimization of Stock Portfolio in Indonesian Health Sector using Markowitz Modern Portfolio Theory

This study analyzes the optimization of the health sector stock portfolio on the Indonesia Stock Exchange using the Markowitz Modern Portfolio Theory method. The data used are the daily closing prices of health sector stocks over the last three years obtained through web scraping techniques from Yahoo Finance. The analysis includes the calculation of daily returns, daily risks, and covariance matrices between stocks. The results of the portfolio optimization show that out of the ten stocks analyzed, the optimal portfolio consists of four stocks, namely MIKA.JK (62.82%), KLBF.JK (15.58%), CARE.JK (15.37%), and SAME.JK (6.23%). This portfolio generates a daily return of 0.216% with a risk level of 1.996%. MIKA.JK contributes the highest return of 0.02063% with a risk of 1.52601%. This study provides guidance for investors in optimizing fund allocation in the health sector stock portfolio in Indonesia.

Sustainable Synergy: Static and Dynamic nexus between ESG and BRICS Equity Markets

Sustainable finance has become the new frontier in global investments, redefining success beyond mere profit margins. For the BRICS economies, this paradigm shift presents both a challenge and an opportunity to reimagine their role in the evolving world of responsible investing. Therefore, this study examines the connectedness between environmental, social, and governance (ESG) leader indices and the BRICS equity markets using the TVP-VAR model. Our return and volatility connectedness results demonstrate a moderate level of transmission between ESG and conventional equity markets. Moreover, the system integration increased substantially during COVID-19 compared to pre-COVID-19, highlighting the strong impact of global events on financial market behavior. ESG (conventional) stocks predominantly emerge as net transmitters (recipients) of return and volatility shock to the system. The dynamic analysis reveals a notable increase in system connectedness, exhibiting increased transmission during an uncertain market environment. Our portfolio analysis suggests that investors should increase their investment in BRICS equity markets during COVID-19 to get higher diversification benefit, however hedging ESG with conventional stocks becomes expensive (higher hedge ratio) during a turbulent period. These results have substantial implications for portfolio management, suggesting that ESG can effectively mitigate risk and optimize portfolio performance.

Virtual land in the metaverse? Exploring the dynamic correlation with physical real estate

PurposeAs blockchain-based virtual worlds gain prominence within the emerging metaverse and Web3, numerous global companies and investors are buying purely virtual land to explore new business potentials and capitalize on digital assets. Given the similarities to physical real estate, this study examines the dynamics of the secondary market for virtual land and relates its returns to those of physical real estate.Design/methodology/approachUsing transaction-level data from a prominent virtual land platform, the authors construct a virtual land market index based on repeat sales index methodology from traditional real estate studies. Wavelet coherence analysis is employed to examine the dynamic correlation between virtual land and various physical real estate market returns. The determinants of this correlation are estimated using stepwise regression analysis. A portfolio analysis explores the implications of adding virtual land to traditional asset portfolios.FindingsThe correlation between virtual and physical real estate market returns is generally low, reaching its lowest during the Covid-19 lockdowns from 2020 to 2022. It spikes during acute economic turmoil such as the initial Covid-19 outbreak or interest rate change announcements. The correlation is primarily driven by consumer and economic climate, the price of the virtual economy token and investor attention. Portfolio analysis indicates that virtual land can enhance risk-adjusted returns within a traditional portfolio, particularly when added to a commercial real estate portfolio.Research limitations/implicationsThis study examines a single virtual land market, despite it being the oldest and one of the largest. Given the rapidly evolving nature of virtual worlds, it is crucial to further test the results and include new virtual land platforms as they emerge.Practical implicationsThe findings provide actionable insights on portfolio implications for investors seeking alternative real-estate-like assets in the digital space. Additionally, this study offers strategic guidance for entering the metaverse, including a comprehensive overview of established virtual presences.Originality/valueWith the advancing digitization of real estate markets, this study is the first to explore the correlation between market returns of virtual land in the metaverse and traditional physical real estate. The findings provide valuable empirical insights for investors, policymakers, entrepreneurs and companies interested in the intersection of digital and traditional property markets.

Optimal allocation of 30 GW offshore wind power in the Norwegian economic zone

Abstract. The Norwegian Government aims to install offshore wind power with a total capacity of 30 GW by 2040, and the Norwegian Water Resources and Energy Directorate has suggested 20 candidate regions. We show that the potential for reducing overall power production variance across these regions is high using modern portfolio theory and the hourly and spatially rich reanalysis NORA3 Wind Power data set (NORA3-WP). The geographical diversification effect is demonstrated under various relevant scenarios, including a sequential build-out scenario with a fully connected Norwegian power grid assumption. By considering 20 alternative regions selected using a recently developed suitability score, we further illustrate that the diversification effect is robust to location changes.

Early Evidence of “Finance for Normal People” in the First Era of Globalization

ABSTRACTIn this paper, we unearth a forgotten study of Pierre Des Essars, the director of research at the Banque de France in the late 19th century, enriched with comments from contemporary financial analysts. Des Essars provides the first and only existing example of the actual composition of French individual portfolios during the Belle Époque. By revisiting what is generally taught, our analyses show that many of the intuitions of “finance for normal people” were already present in the financial analysts' writings during the Belle Époque. Specifically, we show in an unprecedented way that these writings prefigure behavioral portfolio theory word for word a century before its emergence. In addition, our findings highlight that this popular science of investment was born from observations of individual portfolios and the objective of providing financial education for the masses. Finally, we reveal strong invariants in the construction of behavioral finance versus standard finance a century apart.

Interacting Anomalies

Abstract An extensive literature studies interactions of stock market anomalies using double-sorted portfolios. But given hundreds of known candidate anomalies, examining selected interactions is subject to a data mining critique. In this paper, we conduct a comprehensive analysis of all possible double-sorted portfolios constructed from 102 underlying anomalies. We find hundreds of statistically significant anomaly interactions, even after accounting for multiple hypothesis testing. An out-of-sample trading strategy that invests in the top backward-looking double-sort strategy generates equal-weighted (value-weighted) monthly average returns of 4% (2.7%) at an annualized Sharpe ratio of 2 (1.38), on par with state-of-the-art anomaly-based machine learning strategies.

Tracing the ties that bind: navigating the static and dynamic connectedness between NFTs and equity markets in ASEAN based on QVAR-approach

Based on market integration theory, we investigate the static and dynamic connectedness between nonfungible tokens (NFTs) and the Association of Southeast Asian Nations (ASEAN) equity markets using the Quantile Vector Auto Regressive model. We also compute optimal weights and hedge ratios for our variable of interest to establish their diversification and hedging potential. Our analysis infers a moderate level of return transmission at the median quantile, where equity markets evolved as the net recipients of return spillover from the system, while NFTs emerge as key transmitters. In extreme market conditions, transmission between variables is amplified, but the increase is symmetrical across extreme quantiles, suggesting a similar impact. However, the interlinkage among assets is symmetric across conditional quantiles. The dynamic analysis demonstrates that the system integration amplifies during uncertain times (e.g., COVID-19 and the Russia–Ukraine conflict). Our portfolio analysis shows that NFTs provide diversification and hedging in all market conditions. However, the period of turmoil dampened the diversification potential, and hedging became expensive. Our study offers detailed and insightful information about the transmission mechanism and enables the participants of financial markets to diversify and hedge their portfolio.

Explainable post hoc portfolio management financial policy of a Deep Reinforcement Learning agent.

Financial portfolio management investment policies computed quantitatively by modern portfolio theory techniques like the Markowitz model rely on a set of assumptions that are not supported by data in high volatility markets such as the technological sector or cryptocurrencies. Hence, quantitative researchers are looking for alternative models to tackle this problem. Concretely, portfolio management (PM) is a problem that has been successfully addressed recently by Deep Reinforcement Learning (DRL) approaches. In particular, DRL algorithms train an agent by estimating the distribution of the expected reward of every action performed by an agent given any financial state in a simulator, also called gymnasium. However, these methods rely on Deep Neural Networks model to represent such a distribution, that although they are universal approximator models, capable of representing this distribution over time, they cannot explain its behaviour, given by a set of parameters that are not interpretable. Critically, financial investors policies require predictions to be interpretable, to assess whether they follow a reasonable behaviour, so DRL agents are not suited to follow a particular policy or explain their actions. In this work, driven by the motivation of making DRL explainable, we developed a novel Explainable DRL (XDRL) approach for PM, integrating the Proximal Policy Optimization (PPO) DRL algorithm with the model agnostic explainable machine learning techniques of feature importance, SHAP and LIME to enhance transparency in prediction time. By executing our methodology, we can interpret in prediction time the actions of the agent to assess whether they follow the requisites of an investment policy or to assess the risk of following the agent's suggestions. We empirically illustrate it by successfully identifying key features influencing investment decisions, which demonstrate the ability to explain the agent actions in prediction time. We propose the first explainable post hoc PM financial policy of a DRL agent.

Extreme frequency connectedness, determinants and portfolio analysis of major cryptocurrencies: Insights from quantile time-frequency approach

Extreme frequency connectedness, determinants and portfolio analysis of major cryptocurrencies: Insights from quantile time-frequency approach

Portfolio Management in Times of Elevated Risk. Safe-Haven and Hedge Assets in CAPM Setting

The purpose of the article. The purpose of the article is to present the safe-haven concept according to the latest academic literature and distinguish it from the hedge and diversifier terms that are sometimes used interchangeably by researchers and portfolio managers. The ultimate goal of the paper is to place the safe-haven and hedge assets in the portfolio theory setting by introducing the negative beta parameter as stated in the Capital Asset Pricing Model. According to the literature, this article proposes a few approaches to identify and characterize safe-haven assets and to discover the perspective and outline further research in the portfolio theory. Methodology. The work uses the method of descriptive and comparative analysis of literature, i.e., Systematic Literature Review (SLR). This method is used to present scientific overview of portfolio management when uncertainty rises to identify safe-haven and hedge assets. Results of the research. This paper aims to characterize and identify three main types of assets helping investors to reduce the portfolio risk: safe haven, hedge, and diversifier. It introduces an improved analytical framework of beta parameter and drawdown beta concept to contribute to the rapidly expanding research on portfolio theory. Lastly it depicts a trade-off effect, which is stronger in-crisis performance of safe-haven assets. The returns of safe-haven assets are more positive when the stock market returns are more negative that may safeguard the financial system.

Predicting Stock Price Movements Using Deep Learning: An Analysis of Machine Learning Models for Portfolio Optimization

Capital markets play a strategic role in the global economy; however, high volatility often poses challenges for investors in making optimal investment decisions. Stock price prediction has become one of the key elements in effective portfolio management, but the complexity of market data demands more advanced analytical approaches. This study aims to explore the potential of Long Short-Term Memory (LSTM) models in predicting stock price movements and integrating these predictions into portfolio optimization strategies based on modern portfolio theory. The research methodology utilizes historical stock data from the S&P 500 and IDX Composite indices with daily frequency. The LSTM model is compared with Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and accuracy. The LSTM model demonstrated superior performance with an MSE of 0.0018, RMSE of 0.042, and prediction accuracy of 91.5%, significantly outperforming RNN (87.3%), SVM (80.2%), and ANN (82.7%). The prediction results were then integrated into portfolio optimization using the Mean-Variance Optimization approach, resulting in an increase in expected returns from 8.5% to 12.3%, a reduction in risk from 6.2% to 5.8%, and an improvement in the Sharpe ratio from 1.37 to 2.12. This study demonstrates that the LSTM model not only excels in stock price prediction accuracy but also contributes significantly to enhancing portfolio management efficiency. This Deep Learning-based approach offers a more adaptive, data-driven investment strategy, supporting more informed decision-making in the dynamic capital market

Portfolio Choice Using Additional Information from Financial Statements - Evidence from the Frankfurt Stock Exchange

Classical portfolio construction models consider only the information contained in the market prices of stocks, but ignore the financial performance of companies. Typically, the variance is used as the dispersion parameter. This symmetric measure of risk may be inadequate if the distribution of returns differs significantly from the normal or symmetric distribution. With this in mind, we introduce some additional portfolio selection criteria based on companies' financial performance. In addition, we consider semi-variance as an alternative risk measure to variance. The main research objective is to develop a classical portfolio theory by incorporating firms' financial indicators and using semi-variance as a measure of investment risk. In the paper a novel method for portfolio selection has been proposed and we have developed an original computer code to find the efficient three criteria portfolios. A set of 13 types of portfolios was constructed, differing in terms of fundamental values and risk measures. The proposed models were evaluated under the condition of an economic crisis caused by the COVID-19 pandemic. The research sample consisted of the largest companies listed on the Frankfurt Stock Exchange, the DAX 30 index. The research showed that the highest returns were observed for portfolios based on indicators related to the financial condition of the companies. These types of portfolios were also characterized by the highest level of risk, as measured by variance and semi-variance. The analysis thus confirms the usefulness of corporate financial indicators in portfolio decisions and points to the need for further research in this area.

Evaluation of Portfolio Assessment of Student Work in Arabic Language Learning

This research discusses the evaluation of portfolio assessment in Arabic learning, especially related to students' ability to understand fi'il madhi and fi'il mudhari' in the form of mabni majhul and mabni ma'lum at Pomdok Pesantern Al-Anwar Muara Jambi. The background of this study focuses on the importance of a comprehensive evaluation that not only measures the final results, but also the overall learning process of students. The purpose of the research is to evaluate work portfolios as an assessment tool in measuring student development related to Arabic grammar. The research used a qualitative approach of case study type, with data collection techniques through semi-structured interviews and documentation analysis. The data analysis technique used is the Miles and Huberman model, which involves collecting, reducing, and presenting data, as well as drawing conclusions. This study used primary data sources and secondary data sources. Primary data sources were obtained from semi-structured interviews with Ustadzah Fitriawati as well as analysis of student portfolios to assess understanding of fi'il madhi and fi'il mudhari' in the form of mabni majhul and mabni ma'lum. Secondary data sources include archived academic grade documents and learning outcome reports, as well as literature related to portfolio assessment in language education to provide a theoretical foundation and comparison with previous research. The results showed that the use of portfolio work assessment was effective in monitoring the continuous development of students' understanding, both in theory and application of concepts. Portfolios encourage collaboration and discussion between students, and improve critical thinking skills.

PASS: Portfolio Analysis of Selecting Strategies on quantitative trading via NSGA-II

Quantitative trading, driven by technological advancements, utilizes machine learning and mathematical models to develop trading strategies. Evaluating strategy performance through backtesting is common, but ensuring sufficient funding and stability is crucial. Portfolio construction mitigates risk and promotes sustained profit growth. This article introduces the portfolio analysis of selecting strategies (PASS) model, integrating a stability indicator, ‘drawdown duration’ (DDD) and multi-objective evolutionary algorithms (MOEAs). Results demonstrate that PASS reduces DDD risk by 73.9% and increases profit by 4.1% compared to other MOEAs. The model is applied successfully to trading markets like the New York Mercantile Exchange (NYMEX), the West Texas Intermediate (WTI) and the Mini Dow Jones Industrial Average (YM), aiding investors in crafting profitable strategies and managing risk in leveraged trades.

Pemanfaatan Canva sebagai Alat Kreatif untuk Meningkatkan Strategi Promosi GURAFIX: Bisnis Jasa Desain Grafis

In the rapidly evolving digital era, business promotion strategies have undergone significant transformations, particularly through the utilization of online graphic design platforms. This study aims to explore the use of Canva as a creative tool to enhance the promotional strategies of GURAFIX, a graphic design service business. The method employed is qualitative with direct observation techniques and portfolio analysis. The findings indicate that Canva plays a crucial role in improving the productivity and efficiency of the GURAFIX team in producing visually appealing content aligned with current market trends. Utilizing Canva has also positively impacted the quality of designs and promotional appeal, contributing to an increase in clients. However, the study also identifies Canva's limitations in meeting advanced design requirements. This research provides practical implications for other graphic design service businesses to optimally utilize Canva as part of their promotional strategies.

On the efficiency of trading intangible fixed assets in Major League Baseball

Abstract This paper proposes novel approaches to measuring team productivity and evaluating trading efficiency in Major League Baseball (MLB) from 1995 to 2021 through an application of portfolio theory. The performance of individual players is measured using a structural approach relating player outcomes to team runs developed by Lindsey (1963. An investigation of strategies in baseball. Oper. Res. 11: 477–501). Using a portfolio theory framework, we treat MLB teams as a portfolio of players (assets), each of which can be defined by an expected contribution of runs per game and the variance of this measure. It is found that both the expected value and variance have a positive impact on team runs scored. Given our definition of teams characterized by their expected values and variances, we evaluate trading efficiency between teams given their pre-trade expected values and variances and the acquired player’s pre-trade expected value and variance. We find that trade efficiency has improved over our timeframe, consistent with the growth in data-driven decision making used in MLB front offices.

Neural Networks for Portfolio Analysis With Cardinality Constraints.

Portfolio analysis is a crucial subject within modern finance. However, the classical Markowitz model, which was awarded the Nobel Prize in Economics in 1991, faces new challenges in contemporary financial environments. Specifically, it fails to consider transaction costs and cardinality constraints, which have become increasingly critical factors, particularly in the era of high-frequency trading. To address these limitations, this research is motivated by the successful application of machine learning tools in various engineering disciplines. In this work, three novel dynamic neural networks are proposed to tackle nonconvex portfolio optimization under the presence of transaction costs and cardinality constraints. The neural dynamics are intentionally designed to exploit the structural characteristics of the problem, and the proposed models are rigorously proven to achieve global convergence. To validate their effectiveness, experimental analysis is conducted using real stock market data of companies listed in the Dow Jones Index (DJI), covering the period from November 8, 2021 to November 8, 2022, encompassing an entire year. The results demonstrate the efficacy of the proposed methods. Notably, the proposed model achieves a substantial reduction in costs (which combines investment risk and reward) by as much as 56.71% compared with portfolios that are averagely selected.

Analysis of Business Portfolio Using BCG Matrix to Develop Directional Strategies: Case of FIBH

Analysis of Business Portfolio Using BCG Matrix to Develop Directional Strategies: Case of FIBH

Speculative Nature of the Housing Market and Household Polarization: A Portfolio Theory Perspective*

Speculative Nature of the Housing Market and Household Polarization: A Portfolio Theory Perspective*

Neural Networks for Portfolio Analysis in High-Frequency Trading.

High-frequency trading proposes new challenges to classical portfolio selection problems. Especially, the timely and accurate solution of portfolios is highly demanded in financial market nowadays. This article makes progress along this direction by proposing novel neural networks with softmax equalization to address the problem. To the best of our knowledge, this is the first time that softmax technique is used to deal with equation constraints in portfolio selections. Theoretical analysis shows that the proposed method is globally convergent to the optimum of the optimization formulation of portfolio selection. Experiments based on real stock data verify the effectiveness of the proposed solution. It is worth mentioning that the two proposed models achieve 5.50 % and 5.47 % less cost, respectively, than the solution obtained by using MATLAB dedicated solvers, which demonstrates the superiority of the proposed strategies.