Asset Allocation Research Articles

Stocks, Gold and Crude Oil: How Valuable are Volatility and Correlation Timing?

Abstract We study the sources of portfolio returns under parameter uncertainty arising from allocation strategies based on holdings of the NASDAQ index, WTI crude oil and gold. We investigate the contribution of volatility and correlation forecasts for dynamic portfolio allocations in the model of Brandt, Goyal, Clara, and Stroud (2005). We estimate alternative forecasting models using Bayesian methods, and evaluate investor’s utility under the Bayesian predictive density. We then compare the performance of different portfolio strategies through both Sharpe ratios and utility-based metrics. We show that dynamic strategies based on timing volatilities and correlations can add positively to the economic gains generated by non-diversified portfolios involving holdings of either crude oil or gold only. Hence, the economic benefits generated by holding crude oil and gold in asset allocations with stocks arise from the predictability of their volatilities and correlations. JEL classification numbers: C22, C52, G11, G23. Keywords: Bayesian models, dynamic asset allocation, multivariate correlation models, crude oil, gold.

Dynamic Asset Allocation and Retirement Decision with Consumption Ratcheting and Effort Choice

This study examines the problem of choosing optimal consumption, investment, early retirement, and effort levels for an economic agent who is unwilling to reduce their consumption over time. The agent’s income depends on a fixed wage rate, and varies proportionally with their level of effort, which incurs disutility due to labor. By applying the dual-martingale method, we reformulate the problem into its dual form, dividing it into two distinct sub-problems: one focuses on selecting a consumption path that does not decrease, while the other determines the optimal timing for early retirement. Using this method, we find optimal strategies in the form of explicit closed-form solutions.

The quality of extra-financial information in question

The quality of extra-financial information is an important determinant of the quality of asset allocation. This article illustrates the difficulty of defining the quality of information by comparing the two main sustainability accounting standards. It then explains why high-quality sustainability reports are hard to come by. An analysis of the main steps required to calculate ESG ratings illustrates the difficulties in synthesizing a company’s ESG performance, and the paper concludes with empirical studies showing that the quality of ESG ratings still needs to be improved.

Do global COVOL and geopolitical risks affect clean energy prices? Evidence from explainable artificial intelligence models

We investigate the impact of global common volatility and geopolitical risks on clean energy prices. Our study utilizes daily data from January 1, 2001, to March 18, 2024. Using a new framework based on explainable artificial intelligence (XAI) methods, our findings demonstrate that the COVOL index outperforms the geopolitical risk index in accurately predicting clean energy prices. Furthermore, the Extreme Trees algorithm shows superior performance compared to traditional regression techniques. Our findings indicate that XAI improves transparency, thereby making a substantial contribution to agile decision-making in predicting clean energy prices. Practitioners, including investors and portfolio managers, can enhance investment decisions and manage systemic risks by incorporating COVOL into their risk assessment and asset allocation models.

Enhancing financial portfolio management with predictive analytics and scalable data modeling techniques

The financial industry faces an increasingly complex landscape for portfolio management, where data-driven insights are crucial for optimizing asset allocation and managing risk. This paper explores the integration of predictive analytics and scalable data modeling techniques in enhancing financial portfolio management. By leveraging machine learning algorithms, big data architectures, and real-time data processing, predictive analytics can forecast asset trends, detect market anomalies, and assess portfolio risk with high precision. We evaluate several predictive models, including time-series forecasting, neural networks, and ensemble methods, for their efficacy in financial prediction. The study also discusses the role of scalable data modeling frameworks, such as Apache Spark and cloud-based data lakes, in handling vast volumes of unstructured data across different markets and asset classes. Findings indicate that predictive analytics, when paired with robust data models, can deliver real-time, actionable insights, enhancing decision-making for fund managers and institutional investors. Furthermore, this paper highlights best practices for implementing scalable models in financial institutions, addressing challenges like data latency, model interpretability, and system scalability. By adopting these advanced analytics frameworks, portfolio managers can achieve improved risk-adjusted returns, better asset diversification, and enhanced adaptability to market volatility.

Investment Portfolio Allocation and Insurance Solvency: New Evidence from Insurance Groups in the Era of Solvency II

This study examines the effect of the investment portfolio structure on insurers’ solvency, as measured by the Solvency Capital Requirement ratio. An empirical sample of 88 EU-based insurance groups was analyzed to provide robust evidence of the portfolio’s impact on the Solvency Capital Requirement ratio from 2016 to 2022. Linear regression and supervised machine learning models, particularly extra trees regression, were used to predict the solvency ratios, with the latter outperforming the former. The investigation was supplemented with panel data analysis. Firm-specific factors, including, unit-linked and index-linked liabilities, firm size, investments in property, collective undertakings, bonds and equities, and the ratio of government bonds to corporate bonds and country-specific factors, such as life and non-life market concentration, domestic bond market development, private debt development, household spending, banking concentration, non-performing loans, and CO2 emissions, were found to have an important effect on insurers’ solvency ratios. The novelty of this research lies in the investigation of the connection of solvency ratios with variables that prior studies have not yet explored, such as portfolio asset allocation, the life and non-life insurance market concentration, and unit-linked and index-linked products, via the employment of a battery of traditional and machine enhanced methods. Furthermore, it identifies the relation of solvency ratios with bond market development and investments in collective undertakings. Finally, it addresses the substantial solvency risks posed by the high banking sector concentration to insurers under Solvency II.

Optimal strategies for collective defined contribution plans when the stock and labor markets are co-integrated

This paper investigates a collective defined contribution (CDC) pension fund scheme in continuous time, where members' contributions are fixed in advance, and benefit payments depend on the final salary rate. We take account of the co-integration between labor income and the stock market by letting the difference between logs of labor and dividends follow a mean-reverting process. Further, labor income is also a product of aggregate labor income and member's idiosyncratic shocks whose constant growth rate is unknown. It can be modeled by a continuous-time two-state hidden Markov chain. Further, the pension fund can be invested in the financial market consisting of one risky asset and one risk-free asset to enhance profits. After using Hamilton-Jacobi-Bellman (HJB) equations, the closed-form solutions for the optimal asset allocation and the benefit payment policies are obtained to maximize the social welfare and the terminal surplus wealth. Numerical examples are also conducted to illustrate the sensitivity of parameters on the optimal strategies.

Labour Unemployment Insurance and Pension Asset Allocations

Labour Unemployment Insurance and Pension Asset Allocations

Post-compulsory education of children and household asset allocation

We investigate the impact of Post-compulsory Education (PCE) on household asset allocation in this paper. As an investment in children's development, families face a trade-off between PCE and other assets. Based on the data from six representative rounds of the China Household Finance Survey (CHFS), we conduct extensive empirical analyses and find that households with children in the PCE stage, such as senior high schools or universities, exhibit lower proportions of illiquid assets and high-risk assets. We further show that liquidity constraints and expectations of future income are two basic mechanisms through which PCE affects household asset allocation, and the former is relatively stronger. In the heterogeneity analyses, we find that the impact of PCE on family asset allocation is more pronounced in families where parents have educational attainment below the university degree, in families with male children, in rural households, and in families that value education.

Forecasting Realized Volatility: The Choice of Window Size

ABSTRACTDifferent window sizes may produce different empirical results. However, how to choose an ideal window size is still an open question. We investigate how the window size affects the predictive performance of volatility. The empirical results show that the loss function for volatility prediction takes on a U‐shape as the window size increases. This suggests that if the window size is chosen too large or too small, the loss function tends to be large and the model's predictive accuracy decreases. A window size of between 1000 and 2000 observations is ideal for various assets because it can produce relatively minimal forecast errors. From an asset allocation perspective, a mean–variance investor can obtain sizeable utility by using a model with a low loss function value for her portfolio. Moreover, the results are robust in a variety of settings.

Benefiting from the frightened herd: Dynamic asset allocation amid panic sentiment

This paper introduces an innovative dynamic asset allocation approach to hedge against panic sentiment and demonstrates that adopting such an anti-panic strategy can be beneficial. We first develop a novel measure of panic sentiment and analyze its spillover effects across major stock markets. The strategy’s diversification advantages in mitigating panic sentiment are then evaluated. Backtesting results reveal substantial long-term excess returns over benchmarks, with risk-adjusted metrics and statistical tests confirming the robustness of these results.

Allocating investigative resources within the Hungarian police force

The distribution of authority and competence is essential for all organizations, including law enforcement. The main focus is not just the existence of this distribution but its actual functionality, particularly from a professional and scientific standpoint regarding resource allocation. Distributing work tasks involves determining the general investigative authority's capacity to respond to crimes across different geographical areas. The aim is to deploy the appropriate forces and tools to different cases. Efficient distribution of resources is crucial as it impacts the quality of criminal investigation work, minimizes crime-related costs, and mitigates social effects. This study aims to evaluate how well the current regulations and practices match investigative resources with arising tasks and what principles guide the allocation of police forces and assets for law enforcement purposes. The focus is on whether the police's investigative activities are effective socially and scientifically rather than purely from a statistical viewpoint.

AI companies' strategies with traditional vs. digital assets amid geopolitical and banking crises

The financial industry evolves rapidly, offering numerous opportunities but also introducing inherent risks that require careful navigation by institutions. This paper explores the performance of traditional and digital assets as tools for hedging, diversification, and safe-haven strategies during the Russian-Ukrainian crisis and the Silicon Valley Bank (SVB) collapse. Utilizing daily data from April 30, 2021, to September 15, 2023, and employing the Asymmetric Dynamic Conditional Correlation (ADCC) model, which combines four Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models and two residual distributions, we conduct a comprehensive analysis. The results indicate that Bitcoin's role as a safe haven is limited during the geopolitical crisis, impacting specific stocks, while gold's effectiveness varies. Digix Gold (DGX) demonstrates robust safe-haven properties, and Paxos Gold (PAXG) proves notably effective for certain stocks. Before the SVB crisis, gold primarily acts as a diversifier but later emerges as a significant safe haven during the banking crisis. These findings provide valuable insights to portfolio managers, emphasizing the importance of adaptive asset allocation in addressing financial uncertainties.

Research on Digital Transformation and Corporate Financial Asset Allocation Strategy

The new-generation information technology revolution is profoundly changing the economy and society, and promoting industrial innovation and leapfrogging. China actively promotes the synergistic development of industrial digitization and digital industrialization, which has significantly increased the scale of the digital economy. In this context, the digital transformation of enterprises has become a key issue, and the strategic choice of financial asset allocation, as an important factor affecting the operational performance, investment and financing decisions, and innovation activities of enterprises, has attracted much attention. This study constructs the framework of "financial asset allocation behavior-digital transformation", based on the data of A-share listed companies in Shanghai and Shenzhen from 2012 to 2019, and empirically analyzes it by using the fixed effect and threshold model, and finds that the relationship between the allocation of financial assets and the digital transformation is in an inverted U-shape: moderate allocation and digital transformation have an inverted U-shape relationship. It is found that the relationship between financial asset allocation and digital transformation is "inverted U-shaped": moderate allocation promotes transformation, while excessive inhibits it. Short-term financial assets play the role of "reservoir" and support the transformation, while long-term financial assets may produce crowding-out effect. Financing constraints and financial return thresholds also have differentiated impacts on the relationship. This study provides new perspectives and theoretical support for enterprises to optimize resource allocation and achieve sustainable development.

A Parallel Monte Carlo Algorithm for the Life Cycle Asset Allocation Problem

Life cycle asset allocation is a crucial aspect of financial planning, especially for pension funds. Traditional methods often face challenges in computational efficiency and applicability to different market conditions. This study aimed to innovatively transplant an algorithm from reinforcement learning that enhances the efficiency and accuracy of life cycle asset allocation. We synergized tabular methods with Monte Carlo simulations to solve the pension problem. This algorithm was designed to correspond states in reinforcement learning to key variables in the pension model: wealth, labor income, consumption level, and proportion of risky assets. Additionally, we used cleaned and modeled survey data from Chinese consumers to validate the model’s optimal decision-making in the Chinese market. Furthermore, we optimized the algorithm using parallel computing to significantly reduce computation time. The proposed algorithm demonstrated superior efficiency compared to the traditional value iteration method. Serial execution of our algorithm took 29.88 min, while parallel execution reduced this to 1.42 min, compared to the 41.15 min required by the value iteration method. These innovations suggest significant potential for improving pension fund management strategies, particularly in the context of the Chinese market.

Analysis of ABSL Multicap Fund and Textile sector with Innerwear and Hosiery as a Subsector with reference to Lux Industries Ltd

The internship project entitled "Analysis of ABSL Multicap Fund and Textile Sector with Innerwear and Hosiery as a subsector with reference to Lux Industries Ltd." was taken up at Aditya Birla Sun Life Ltd. by Rohan Ganesh Kolhapure. The project would involve assessing the performance, strategies, and market positioning of the ABSL Multicap Fund for the period between June-August 2024. This also included assessing the textile sector dynamics, with special inward titude toward the innerwear hosiery subsector, using Lux Industries Ltd as a case study. The main tasks were related to the assessment of historical performance of ABSL Multicap Fund and its investment strategies, as well as the analysis of sectoral trends influencing the innerwear market. Specific methodologies included quantitative analysis of financial statements, asset allocation, and risk management practices, in addition to strategic frameworks like SWOT, PESTEL, and Porter's Five Forces.

Comparative Analysis of Mutual Funds and Gold ETFs: A Study on Investment Performance

This project analysis the returns of specific large-cap equity mutual funds and Gold Exchange Traded Funds (ETFs) to determine the best investment options for investors looking to balance growth and stability. To create a well-rounded portfolio, the study assesses the performance metrics and risk factors of these investment options. Large-cap equity funds are examined for their ability to offer growth in investment value, while gold ETFs are evaluated for their function as protection against market fluctuations and rising prices. A Decision matrix framework is utilized to methodically assess every fund and ETF based on factors including past performance and level of risk. This method based on data allows for a comprehensive analysis, pointing out the advantages and drawbacks of equity and gold investments. The research provides information on strategic asset allocation, highlighting diversification to maximize profits and handle risk efficiently. The results are meant to assist investors in making knowledgeable, eco-friendly investment choices in a changing financial environment.

AI-ENHANCED PORTFOLIO MANAGEMENT: CRAFTING OPTIMAL INVESTMENT STRATEGIES FOR DIVERSE FINANCIAL MARKETS

This study investigates the transformative potential of artificial intelligence (AI) in portfolio management across diverse financial markets. It aims to develop a comprehensive framework that enhances investment strategies in developed, emerging, and frontier markets through AI techniques. The research will employ a mixed-methods approach, combining quantitative and qualitative methodologies. Initially, extensive secondary data will be collected from market reports, financial databases, and previous studies. This data will encompass market-specific characteristics, AI-based portfolio strategies, and financial indicators. AI methods, including neural networks, machine learning models, and natural language processing applications, will then be utilized to analyze portfolio performance across various market scenarios. The anticipated outcome of this study is a versatile AI-driven framework that accommodates the unique aspects of different financial markets. By optimizing asset allocation, risk management, and predictive analytics, this framework will provide portfolio managers with evidence-based recommendations to enhance the stability and performance of their investments. The study aims to demonstrate how AI can mitigate risks associated with market volatility, liquidity issues, and regulatory constraints, leading to a more resilient and data-driven approach to portfolio management on a global scale KEYWORDS: AI integration in finance, Portfolio performance metrics, Compounded Annual Growth Rate (CAGR), Volatility reduction

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.

Determinants on the selection of rural households’ financial assets in China: marketization or financial literacy?

Financial literacy is an indispensable factor in household asset allocation, but market-oriented reforms are also an external factor that cannot be ignored. Using data from the 2013 and 2019 China Household Financial Survey (CHFS), this paper investigates whether the individual’s ability or the market system hinders rural households’ participation and asset investment in the financial market. Findings reveal that both marketization and financial literacy positively affect residents’ financial activities, but the roles of the two are not the same. Financial literacy is an essential factor in increasing participation in the financial market. Moreover, marketization and financial literacy significantly affect rural financial asset allocation decisions in four ways: changing risk preference, using the Internet, alleviating liquid constraints and increasing education expenditure on offspring. Besides, households’ financial behaviors exhibit a stronger correlation with improved network infrastructure, robust legal frameworks, favorable business credit environments, higher social media penetration, and enhanced human capital.