Optimal Portfolio Research Articles

Estimating changepoints in extremal dependence, applied to aviation stock prices during COVID-19 pandemic

The dependence in the tails of the joint distribution of two random variables is generally assessed using χ-measure, the limiting conditional probability of one variable being extremely high given the other variable is also extremely high. This work is motivated by the structural changes in χ-measure between the daily rate of return (RoR) of the two Indian airlines, IndiGo and SpiceJet, during the COVID-19 pandemic. We model the daily maximum and minimum RoR vectors (potentially transformed) using the bivariate Hüsler-Reiss (BHR) distribution. To estimate the changepoint in the χ-measure of the BHR distribution, we explore two changepoint detection procedures based on the Likelihood Ratio Test (LRT) and Modified Information Criterion (MIC). We obtain critical values and power curves of the LRT and MIC test statistics for low through high values of χ-measure. We also explore the consistency of the estimators of the changepoint based on LRT and MIC numerically. In our data application, for RoR maxima and minima, the most prominent changepoints detected by LRT and MIC are close to the announcement of the first phases of lockdown and unlock, respectively, which are realistic; thus, our study would be beneficial for portfolio optimization in the case of future pandemic situations.

Identifying Rule-Based Rationality

Abstract The revealed preference methodology allows an observer to infer preferences from choices. This paper extends this fundamental idea by experimentally identifying the preference for basing choices on simple decision rules. Subjects not only make case-by-case portfolio allocations but also design a simple investment rule for selecting portfolios. They then choose between these two decision modes for an additional set of problems. The majority opt for the rule interface and in most cases choose a simple investment rule that cannot be rationalized by any simple utility function or accounted for by reductions in decision time or by cognitive costs.

Computational analysis of expectile and deviation expectile portfolio optimization models

Computational analysis of expectile and deviation expectile portfolio optimization models

Optimal scheduling of a RES – Electrolyzer aggregator in electricity, hydrogen and green certificates markets

The emerging threat of climate change underscores the necessity for increased investments in renewable energy sources and hydrogen energy to enhance the transition towards a more sustainable and resilient future. This work presents a simple Linear Programming (LP) mathematical framework to ensure the optimal portfolio management and participation of an aggregator owning a Polymer Electrolyte Membrane (PEM) electrolyzer and renewable energy assets in the electricity, hydrogen and green certificates markets. The objective aims to maximize the aggregator's overall profits by optimally managing green certificates issuance and diminishing the curtailment of the renewable energy production. The proposed approach is applied to an illustrative case study, while additionally a number of scenarios are considered to account for fluctuations in electricity price and wind power production. Furthermore, the impact of expanding electrolyzer's power capacity from the aggregator's side is also investigated. The results obtained illustrate the impact of the above-mentioned scenarios on the optimal operation and profitability of the aggregator.

Wealth shocks and portfolio choice

We use new euro area representative data from the Consumer Expectations Survey (CES) to elicit household-specific propensities to invest and consume out of positive wealth shocks. Using a randomized assignment of hypothetical lottery gains ranging from €5,000 to €50,000 and a realistic menu of consumption, saving and asset choices, we estimate the causal effect of wealth shocks on risky asset ownership and conditional asset shares. Wealth shocks have a positive effect on stockholding (between 8.4 and 12.8 percentage points increase in participation for the largest wealth shock). The majority of households do not participate in the stock market, even after a large increase in wealth. The conditional asset share invested in risky assets is constant for wealth shocks up to €20,000, and edges up slightly (by at most 2 %) for larger prizes. Our evidence is consistent with constant relative risk aversion for the majority of risky asset investors, while we also find important heterogeneity in the level of risk aversion across individuals.

Application of Financial Engineering in Quantitative Investment Strategy

This paper mainly discusses the application of financial engineering in quantitative investment strategy. Firstly, the importance and function of financial engineering in quantitative investment strategy are introduced, and then the principle and technology of quantitative investment strategy are expounded. Then, the application of financial engineering in quantitative investment strategy is analyzed in detail, including stock market analysis, financial risk management, portfolio optimization, market liquidity analysis and financial innovation. Finally, the contribution of financial engineering in quantitative investment strategy and its future development trend are summarized.

Exploring the essential features influencing the synthesis of methylenedianiline to support industrial processes

The most important intermediate of methylene diphenyl diisocyanate (MDI), the most widely and quantitatively produced isocyanate in the world, is methylenedianiline (MDA). MDA is industrially produced from the reaction of aniline and formaldehyde catalysed by inorganic acids, most commonly HCl. The reaction parameters used during the synthesis of MDA have a fundamental impact on the quality parameters of the resulting MDA product mixture, and thus on the properties of the final MDI product mixture as well. Although MDA is an important industrial intermediate, currently its synthesis at the industrial level is based on empirical rules, knowledge and rule of thumbs, the effects of production parameters and their magnitude on product properties are not known and not published in the literature. In this study, the correlations between the independent operating parameters and dependent product quality parameters characterizing the MDA mixture were explored by developing different regression models with the use of experimental laboratory synthesis data in order to support the industrial synthesis process with easily deployable models with satisfactorily high accuracy. After investigating the laboratory experiment data from a total of 46 individual laboratory experiments, it was found that with machine learning models almost all of the independent parameters can be described with satisfactory accuracy. The models presented in this work demonstrate that it is possible to develop models that can be used to adjust the ring distribution, isomer ratios and selectivity of the reaction with minimising by-product quantities according to market demands or to different objective functions by fine-tuning the production parameters., thus achieving an optimal product portfolio and operating cost for the industrial process as well.

Asymmetric TVP-VAR connectedness between highly traded commodities and hedging strategies: Evidence from major contagions

The objective of this study is to examine the return interconnectedness and asymmetric spillover effects in global commodity futures markets, with a focus on the impact of contagion. A competent asymmetric time-varying parameter vector autoregressive (TVP-VAR) model was employed for highly traded commodity futures (cocoa, coffee, corn, cotton, soy, sugar, wheat, and oil) between January 1, 2000, and March 31, 2024. This study investigates the connectedness of commodities in three dimensions: asymmetric spillovers, the influence of oil and oil substitutes on the network, and the impact of major contagions. The average total connectedness index (TCI) indicates that the connectedness is significant throughout the period and increases during the invasion. The findings imply that contagion effects trigger a potential alteration in the structure of the network integration level of the commodities, amplifying system-wide dynamic connectivity due to disurptions caused by oil and oil substitutes. The net plot depicts corn and soy as the net transmitters, with their magnitude increasing during the contagions. The pairwise connectedness index (PCI) revealed that corn-soy, corn‒wheat, and soy-wheat were the primary interactors, while oil became a significant interactor, particularly during the oil crash and the COVID-19 outbreak. Additionally, compared with other contagions, GFC had a potential asymmetric effect on the network. Positive returns dominate the interaction between the primary transmitter and receivers, whereas negative returns do not significantly dominate the total network. These investigations contribute to the literature on the food-fuel nexus in terms of asymmetries and the impact of contagions on the futures market. It also identified the optimal portfolio allocation based on the hedging effectiveness of three portfolio construction strategies.

Retail investors and ESG news

An important debate exists around the extent to which retail investors make sustainable investments and, if they do, why. We contribute to this debate by investigating the aggregate trading patterns of retail investors around a comprehensive sample of key environmental, social, and governance (ESG) news events for U.S. firms. We show that ESG news events appear to be an important factor in retail investors’ portfolio allocation decisions. Yet, inconsistent with arguments about retail investors’ nonpecuniary preferences, our evidence shows that retail investors mainly trade on this information when they deem it financially material to a company’s stock performance. We also find their net trading demand predicts abnormal returns in the subsample of financially material events, consistent with retail traders benefiting from incorporating ESG-related information into their decision-making when it influences firm value. Overall we conclude that the average U.S. retail investor cares about firms’ ESG activities but primarily to the extent these activities matter for company financial performance.

Stocks and Options Portfolio Optimisation With Reinforcement Learning

We present a model-free reinforcement learning (RL) framework for portfolio optimization across multiple assets and option prices. We directly model the relationship between the portfolio weights and the predictors with a network and maximize performance metrics for portfolio construction by using RL. We construct a portfolio, such that for any quantity of option that we buy or sell, we have a quasi-replicating portfolio made of a quantity of stock and a quantity of cash. We propose Cross-Asset-Option Transformers (CAOTs) to recover the interrelationships among options and their corresponding assets, which we use to construct a long-short or a bottom-up portfolio. We then maximize performance metrics of our portfolio with RL.

Sparse portfolio optimization via [formula omitted] over [formula omitted] regularization

Sparse portfolio optimization, which significantly boosts the out-of-sample performance of traditional mean–variance methods, is widely studied in the fields of optimization and financial economics. In this paper, we explore the ℓ1/ℓ2 fractional regularization constructed by the ratio of the ℓ1 and ℓ2 norms on the mean–variance model to promote sparse portfolio selection. We present an ℓ1/ℓ2 regularized sparse portfolio optimization model and provide financial insights regarding short positions and estimation errors. Then, we develop an efficient alternating direction method of multipliers (ADMM) method to solve it numerically. Due to the nonconvexity and noncoercivity of the ℓ1/ℓ2 term, we give the convergence analysis for the proposed ADMM based on the nonconvex optimization framework. Furthermore, we discuss an extension of the model to incorporate a more general ℓ1/ℓq regularization, where q>1. Moreover, we conduct numerical experiments on four stock datasets to demonstrate the effectiveness and superiority of the proposed model in promoting sparse portfolios while achieving the desired level of expected return.

Portfolio Optimization with Multi-Trend Objective and Accelerated Quasi-Newton Method

We propose a portfolio optimization method with a multi-trend objective and an accelerated quasi-Newton method (MTO-AQNM). It leverages a BFGS-based quasi-Newton algorithm and incorporates an ℓ1 regularization term and the self-funding constraint. The MTO is designed to identify multiple trend reversals. Different trend reversals are asymmetric, and we hoped to extract rich and effective information from them. The AQNM adopts the BFGS method with the Wolfe conditions, which reduces computational complexity and improves convergence speed. We wanted to evaluate the performance of our algorithm through financial markets that were asymmetric in all respects. To this end, we conducted comprehensive experimental approaches on six benchmark data sets of real-world financial markets that were asymmetric in time, frequency, and asset type. Our method demonstrated superior performance over other state-of-the-art competitors across several mainstream evaluation metrics.

From Portfolio Selection to Portfolio Choice: Remembering Harry Markowitz

From Portfolio Selection to Portfolio Choice: Remembering Harry Markowitz

A Mean-Variance Approach to Portfolio Optimisation for Effective Stock Selection in the Malaysian Stock Market

Optimization of portfolio selection is very important for overall financial engineering performance. Recent developments in portfolio selection have been developed by many researchers. However, many studies show that prediction with manual models is not enough to achieve highly accurate predictions and prosperous returns. In this paper, a novel portfolio construction approach is developed using the mean-variance (MV) model and maximizing the Sharpe ratio for selecting the best portfolio. The approach model is by choosing the best ratio from the comparison between optimal return and annual standard deviation. Experimental data was taken from the Malaysian Stock Exchange from 2014 to 2020, from several securities the ten most active securities were selected. The purpose of this study is to assist investors in verifying the Malaysian stock market to reduce risk and maximize returns. The results of the study show that the ratio of the optimal return and the annual standard deviation is 0.711. These results are obtained from a comparison between the optimal return of 33.96% and the annual standard deviation of 47.73%. The proposed model by maximizing the comparison between the optimal return and the annual standard deviation can be used to support portfolio selection decisions. The results showed that the efficiency limits of the selected stocks and optimal portfolio weights could be achieved.

Quantum computing applications, challenges, and prospects in financial portfolio optimization

Quantum computing applications, challenges, and prospects in financial portfolio optimization

Portfolio Design Beta, Alpha, Variance on Inconsistent Stocks in Indonesia LQ45 Index

Optimal portfolios have become a major concern in finance, especially in the investment context. This research aims to explore the concept of an optimal portfolio by applying beta, alpha, and variance methods, as well as considering active and passive strategies. The background of this research involves the ever-evolving complexity of financial markets, where investors are challenged to create portfolios that can provide optimal returns with acceptable levels of risk. This research aims to identify and analyze the factors that influence the formation of an optimal portfolio. The methods used include beta measurement as a measure of systematic risk, alpha calculation as an additional performance measure, and using variance to measure portfolio volatility. Active and passive strategies are then applied to understand the impact of strategy choices on portfolio performance. The findings of this research highlight the importance of understanding market dynamics and effective strategies to achieve investment goals. This research provides in-depth insight into how to use beta, alpha, and variance methods in an integrated manner to design optimal portfolios with strategies that suit investor preferences and goals. The contribution of this research lies in presenting a comprehensive framework for developing optimal portfolios, combining elements of active and passive strategies. The results of this research can provide practical guidance for investors in making more informed investment decisions and can improve their portfolio performance.

Tail risk spillovers between Islamic sectoral equities and bond markets: a time-frequency domain approach

ABSTRACT This study examines the tail risk spillover between Islamic sectoral stock indices and country specific investable Islamic bonds in time and frequency domain and provides useful implications for portfolio management. For the analyses, Conditional Autoregressive ValueatRisk (CAViaR), Quantile Connectedness, and DCCGARCH t-Copula models are estimated utilizing daily data from 15 countries. The findings show that the connectedness and spillover of risks are much stronger at tails than at median, and in the short-term than in long-term. Whereas median risk connectedness surges during COVID-19 pandemic, the connectedness between tail risks is usually elevated even before the COVID-19 pandemic with industrial sector being consistently a significant net transmitter of shocks. Moreover, all the sectoral stock market indices are significant and consistent transmitter of left tail shocks indicating a much stronger role of sectoral stock markets in transmitting large negative shocks. A heightened connectedness is also observed during Russia-Ukraine war mainly in the short-term frequency. The portfolio analysis shows that long positions in sectoral stocks can usually be hedged by taking short positions in Islamic bonds. Hedging effectiveness and optimal portfolio weights are also calculated to provide market participants with further information.

Optimal portfolios with anticipating information on the stochastic interest rate

Optimal portfolios with anticipating information on the stochastic interest rate

A model for optimizing an investment portfolio in modern conditions

Subject. This article discusses the issues of improving the methodology for optimizing an investment portfolio in modern conditions. Objectives. The article aims to develop a model for optimizing the investment portfolio, taking into account the investor's risk profile and the limited budget. Methods. For the study, I used a comparative analysis, mathematical modeling in economics, machine learning methods, genetic algorithms, and the Python programming language. Results. The article presents the author-developed model of optimizing the investment portfolio of an enterprise, based on the symbiosis of individual elements and principles of the most effective methods of optimizing the investment portfolio, algorithms for their implementation, mathematical model and practical recommendations. Conclusions and Relevance. The combination of classical methods of investment portfolio optimization and machine learning in the model helps improve the accuracy of assessing the future return of assets and determine their weights in the investor's portfolio. The theoretical significance of the work lies in the fact that the scientific provisions that actualize the task of improving existing or developing new approaches to optimizing the investment portfolio are proved. The practical significance of the study lies in the fact that the presented model is of an applied nature, taking into account the risk profile of the investor, tactical and strategic goals of investment, and budget constraints. The results of the study may be of interest to novice stock market participants, experienced investors, IT specialists engaged in the creation of programs and robots for trading on the stock market using machine learning tools.

MUTUAL FUND PORTFOLIO ANALYSIS

Abstract — Mutual funds serve as popular investment vehicles due to their potential for diversification and professional management. However, choosing the right funds and optimizing one’s portfolio allocation can be complex. This project presents a comprehensive Mutual Fund Portfolio Analysis system designed to assist investors in making informed decisions. The system allows users to input their mutual fund holdings, tracking them over time to assess performance. It offers a range of analytical tools, including portfolio diversification analysis, risk assessment, and comparison against relevant benchmark indices. These features provide investors with a holistic view of their portfolio’s performance and risk profile. Moreover, the system employs advanced algorithms to generate personalized recommendations for optimizing the portfolio based on the user’s investment goals and risk tolerance. These recommendations aim to enhance returns while managing risk effectively. By utilizing this system, investors can gain deeper insights into their mutual fund investments, enabling them to make well-informed decisions. Ultimately, this project aims to empower investors to achieve their financial goals through strategic mutual fund portfolio management. Keywords – Mutual funds, Portfolio analysis, Investment decisions, Diversification, Risk assessment, Benchmark comparison, Portfolio optimization, Investment goals, Risk tolerance.