Active Portfolio Management Research Articles

Iterative Deep Learning Approach to Active Portfolio Management with Sentiment Factors

Iterative Deep Learning Approach to Active Portfolio Management with Sentiment Factors

Research on the Development of Investment Portfolio Theory

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

Reconciling Tracking Error Volatility and Value-at-Risk in Active Portfolio Management: A New Frontier

AbstractThis article introduces the Risk Balancing Frontier (RBF), a new portfolio boundary in the absolute risk-total return space: the RBF arises when two risk indicators, the Tracking Error Volatility (TEV) and the Value-at-Risk (VaR), are both constrained not to exceed pre-set maximum values. By focusing on the trade-off between the joint restrictions on the two risk indicators, this frontier is the set of all portfolios characterized by the minimum VaR attainable for each TEV level. First, the RBF is defined analytically and its mathematical properties are discussed: we show its connection with the Constrained Tracking Error Volatility Frontier (Jorion in Financ Anal J, 59(5):70–82, 2003. https://doi.org/10.2469/faj.v59.n5.2565) and the Constrained Value-at-Risk Frontier (Alexander and Baptista in J Econ Dyn Control, 32(3):779–820, 2008. https://doi.org/10.1016/j.jedc.2007.03.005) frontiers. Next, we explore computational issues implied with its construction, and we develop a fast and accurate algorithm to this aim. Finally, we perform an empirical example and consider its relevance in the context of applied finance: we show that the RBF provides a useful tool to investigate and solve potential agency problems.

The football world upside down: Traditional equities as an alternative for the new fan tokens? A portfolio optimization study

This study provides a comprehensive analysis of the relationship between fan tokens and equities as investment vehicles in football clubs. Using the GO-GARCH methodology to account for time-varying higher-order co-moments, we examine the risk-return characteristics of these assets in different portfolio contexts over the period November 2021-March 2023. Our results show high diversification opportunities between fan tokens and equities, especially with investments in different leagues. Portfolio analysis shows that equities generally outperform fan tokens as an investment vehicle, highlighting the importance of active portfolio management. The study also shows that equities have broader diversification potential than fan tokens when added to a portfolio. Furthermore, Turkish fan tokens have better characteristics than Italian fan tokens, possibly due to the nonfinancial benefits for investors. The results are relevant for both retail and institutional investors, regardless of their risk tolerance profiles.

Performance of active portfolio managers when the benchmark is not observable

We present a specialized methodology designed for evaluating the performance of active portfolio managers in situations where the common benchmark portfolio cannot be directly observed or determined by the analyzing agent. This method assesses performance by examining the excess alpha of an optimal active fund with respect to a combination of such funds that efficiently minimizes residual risk. After establishing the theoretical underpinnings of this approach and deriving the necessary statistical tests, we then showcase its practical application in assessing the historical performance of pension fund administrators operating within the Peruvian Private Pension System.

Contextual Approach for Fundamental Law of Active Management

Active Portfolio Management is a simple yet powerful tool for portfolio selection which has became a popular strategy among individual investors. Theoretical background of active management has been established by Fundamental Law of Active Management. The main postulate of this law suggests that in order to achieve additional return alpha, investors should apply a model with higher forecasting power (IC) and should apply it to a maximum number of assets (breadth). Individual investors are attracted by this straightforward and uncomplicated logic and that is the reason why active management is so popular strategy among them. However, the original and later developed variants of the law lead to the same conclusion – for better results the forecasting model must be applied to as many as possible assets. However, this contradicts with the concentration paradox which investors meet in their investment decisions – applying the same forecasting model to maximum amount of assets results in involving unknown assets with diverse characteristics in analysis. With contextual approach we change this perspective. By applying a contextual approach to active management investors can concentrate the forecasting models to a specific group of assets with similar characteristics. We justify the existence of concentration paradox and by using different fundamental contexts to differentiate stocks in different groups, we test fundamental cross-sectional regression models as forecasting models for active investment. With data from Taiwan Stock market we prove that both ex-ante IC and realized return achieved with contextual models outperform the general portfolios which follow the standard postulate of fundamental law.

Decomposing Countries Consumption in Active Portfolio Management: A Black–Litterman Application

Decomposing Countries Consumption in Active Portfolio Management: A Black–Litterman Application

PORTFOLIO MODELS FOR OPTIMIZING DRAWDOWN DURATION

The drawdown duration, which measures the time elapsed since the portfolio obtained its maximum value, is an important criterion in active portfolio management for institutional investors. Although several optimization models exist for controlling portfolio drawdown magnitude (i.e. the percentage drop in portfolio value from its latest peak value), developing similar models for the drawdown duration has received minimal attention in the literature. Therefore, this paper develops a family of models for optimizing average, maximum and tail drawdown duration formulated as mixed-integer linear programming (MILP) problems, allowing the utilization of powerful solvers to identify optimal asset portfolios. We apply the developed models to real data on historical returns to compare their performance against traditional and drawdown-based portfolio selection models. The results indicate that the developed models lead to decrease in drawdown duration levels both in in-sample and out-of-sample tests. The constructed efficient frontiers also show a clear trade-off between minimizing drawdown duration and maximizing expected returns.

Deciphering Financial Strategy Management: Insights into Performance and Investment Decision-Making

This study explores financial strategy management's implications on organizational performance and investment decision-making processes. Employing a qualitative approach, the research conducts a comprehensive literature review to synthesize existing knowledge. The study highlights the significance of effective financial strategy management in enhancing organizational profitability, liquidity, and growth prospects. Through an analysis of empirical studies by Graham and Harvey (2001), Fama and French (1993), and Carhart (1997), the research underscores the importance of tailored financial strategies and active portfolio management in achieving superior investment performance. Furthermore, integrating environmental, social, and governance (ESG) criteria into financial strategy formulation is examined, drawing on research by Li and Tang (2020), which suggests that companies embracing sustainability principles can achieve competitive advantages and attract socially responsible investors. The findings emphasize the multifaceted nature of financial strategy management, necessitating a comprehensive understanding of internal and external factors influencing financial decisions. The study also discusses future research directions, including exploring emerging trends such as digitalization, the role of behavioral biases in financial decision-making, and longitudinal studies tracking the performance of financial strategies over time. This research contributes to the theoretical understanding and practical implications of financial strategy management in navigating dynamic market environments.

Across-time risk-aware strategies for outperforming a benchmark

We propose a novel objective function for constructing dynamic investment strategies with the goal of outperforming an investment benchmark at multiple points of evaluation during the investment time horizon. The proposed objective is intuitive, easy to parameterize, and directly targets a favorable tracking difference of the actively managed portfolio relative to the benchmark. Under stylized assumptions, we derive closed-form optimal investment strategies to guide the intuition in more realistic settings. In the case of discrete rebalancing with investment constraints, optimal strategies are obtained using a neural network-based numerical approach that does not rely on dynamic programming techniques. Compared to the targeting of a favorable tracking difference relative to the benchmark only at some fixed time horizon, our results show that the proposed objective offers a number of advantages: (i) The associated optimal strategies exhibit potentially more attractive asset allocation profiles, in that less extreme positions in individual assets are taken early in the investment time horizon, while achieving a similar terminal terminal wealth distribution. (ii) Across-time risk awareness leads to more robust performance and a higher probability of benchmark outperformance during the investment horizon in out-of-sample testing. The resulting strategies therefore exhibit desirable characteristics for active portfolio managers with periodic reporting requirements.

Crash Prediction Using Fundamental Variables: Evidence from Mainland China

This article investigates how fundamental crash prediction models perform in mainland China’s fast-growing equity markets. We apply three families of fundamental models, price-to-earnings ratio, cyclically adjusted price-to-earnings ratio, and bond-stock earnings yield differential, to the Shanghai and Shenzhen stock indices. Our statistical analysis supports the dominant view that Chinese equity markets behave different from U.S. markets. We find that fundamental models are significant predictors of equity market crashes in China despite these differences. Finally, we show how to use these crash prediction models to improve active portfolio management.

Trading Strategies Using Technical Indicators: An Active Portfolio Management Approach

The study aims to evaluate the effectiveness of active trading strategies developed using technical indicators against the buy-and-hold passive investment strategy. It uses the simple moving average (SMA), Bollinger band (BB), and moving average convergence divergence (MACD) as technical indicators to generate the buy and sell signals. The study uses the data of treasury bills as a proxy for a risk-free portfolio and the Nifty Total Return Index (TRI) as a proxy for a risky equity portfolio in daily frequency from June 30, 1999 to March 31, 2021. It creates the cumulative wealth index for the sample period using active portfolio management based on the buy and sell signals generated using various technical indicators. The findings suggest that SMA and MACD trading strategies provide better risk-adjusted returns than the buy-and-hold and Bollinger band trading strategies. Also, the shorter days moving average outperforms the longer days moving average trading strategy. The trading strategies perform better during the bear market than the bull market.

Practical Applications of Doing Well While Doing Good: The Elusive Quest for Green Bond Returns

In <ext-link><bold><italic>Doing Well While Doing Good: The Elusive Quest for Green Bond Returns</italic></bold></ext-link>, from the Spring 2023 issue of <bold><italic>The Journal of Impact and ESG Investing</italic></bold>, <bold>Juliusz Jablecki</bold>, of <bold>Narodowy Bank Polski</bold> and the <bold>University of Warsaw</bold>, finds that while the green bond label tends to be associated with slightly lower bond spreads, it does not significantly affect overall performance. Credit rating, duration, and industry sector membership have a much stronger influence. Additionally, green bonds do not outperform nongreen bonds during market sell-offs—in fact, they had worse drawdowns in the sell-off of March 2020. However, while green bonds by themselves do not offer a return premium, investors can use them in a data-driven active portfolio management strategy to deliver above-market returns.

Supply Chain Concentration in Active Portfolio Management

Supply Chain Concentration in Active Portfolio Management

Capital Commitment and Performance: The Role of Mutual Fund Charges

AbstractWe study how the scarcity of committed capital affects the equilibrium distribution of net alphas in the asset management industry. We propose a model of active portfolio management with different sales fee structures where committed capital is in short supply. In the model, a portfolio’s excess return is not fully appropriated by the money manager but shared with long-term investors. Empirically, we show that capital commitment allows funds to hold shares longer and take advantage of slow-moving arbitrage opportunities. Consistent with the model, funds with more committed capital generate higher value added, which, net of fees, accrues to long-term investors.

The benefits of the diversification of E&amp;P operators in Brazil and the importance of Petrobras’ active portfolio management strategy

The benefits of the diversification of E&amp;P operators in Brazil and the importance of Petrobras’ active portfolio management strategy

Active portfolio management for the emerging and frontier markets: the use of multivariate time series forecasts

Active portfolio management for the emerging and frontier markets: the use of multivariate time series forecasts

Delegated Investment Management in Alternative Assets

Abstract Institutional investors can be segmented into investors that hold simple portfolios of traditional equities and bonds, and investors that manage complex strategies in public and private markets. Investors implementing active portfolio management and holding diversified portfolios of equities and bonds are more likely to invest in alternative asset classes. The performance of institutional investors in alternative assets is significantly lower than in equities, suggesting that investors accept lower returns in exchange for diversification benefits. Institutions delegate 90% of their alternative investments to external managers and funds-of-funds. These intermediaries capture large part of the potential diversification benefits through higher fees and lower returns. (JEL G11, G23) Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Price discovery and risk management in asset class: a bibliometric analysis and research agenda

ABSTRACT This paper presents a bibliometric review of 2,391 publications on price discovery and hedging in financial assets and commodities. The discipline-wise contribution of publications and journal distribution based on the frequency of relevant research articles are reported. Five overarching themes have emerged from the content analysis, namely: (1) price discovery, (2) hedging in commodities, financial assets, and cryptocurrency, (3) market interconnectedness and volatility spillover, (4) market microstructure in pre- and post-financial crisis, and (5) asset allocation, diversification, and portfolio management. We propose three future research streams, namely predictability in uncertainty, short-term portfolio adjustment, and dynamic trading strategies, with nine research questions that can help investors and stock/commodity exchanges in an active portfolio and derivative contract management.

Green bonds and implied volatilities: Dynamic causality, spillovers, and implications for portfolio management

The long-term and sustainable development focuses of green bond together with its increasing popularity drives the need for a better understanding of its hedging effects against market risks. Our study investigates whether and how green bond can act as a hedging instrument against implied volatility, a measure of forward-looking market uncertainty. We find evidence of significant time-varying connectedness between green bond and implied volatilities of the stock, energy, and commodity markets. Building on this characteristic, investors are required to adopt an active portfolio management strategy to ensure the hedging effectiveness of green bond against implied volatilities. Specifically, this strategy requires frequent switches between long and short positions in the green bond market. Our simple simulation study shows evidence that applying connectedness regime-dependent trading strategies can increase the hedging effectiveness of green bond against implied volatilities in terms of risk-adjusted returns.