Active Portfolio Management Research Articles

An Entropy Approach to Measure the Dynamic Stock Market Efficiency.

We measure stock market efficiency by drawing the comprehensive sample from Asia, Europe, Africa, North–South America, and Pacific Ocean regions and rank the cross-regional stock markets according to their level of informational efficiency. The study period spans from January 1, 1994, to August 3, 2017. We employ the approximate entropy approach and find that stock market efficiency evolves over the period. The degree and nature of evolution vary across regions and the development stage of the markets. The global, regional, domestic economic, and non-economic factors influence the adaptive nature of the stock markets. The emerging stock markets have improved efficiency by financial liberalization policy but are adversely affected by global shocks. The estimates validate the relevance of the adaptive market framework to describe the rejection of random walk without excess returns. The results suggest the growing presence of technical analysis and active portfolio managers. The emerging markets in Asia hold policy lessons for their peers. The findings suggest that global investors need to overcome the homogeneity bias as returns opportunities exist within the region and types of markets.

Directional predictability and time-frequency spillovers among clean energy sectors and oil price uncertainty

This paper analyzes the dependence among clean energy sectors and oil price uncertainty using the NASDAQ OMX Green Economy Index for the Building, Economy, Edge, Financial, Technology and Transport sectors in the United States. First, we use the wavelet and Cross-Quantilogram (CQ) techniques to examine the directional predictability from oil price uncertainty to these clean energy sectors across different investment horizons and market conditions. Secondly, we use the Time-Varying Parameter (TVP-VAR) model with stochastic volatility to characterize the level of spillovers among the clean energy sectors and oil market uncertainty under different investment horizons. Results from the CQ demonstrate strong evidence of heterogeneous dependence and predictability from oil market uncertainty to clean energy sectors across different market conditions and investment horizons, pointing out the importance of active portfolio management. Also, we find that the level of shock spillovers is weak in the short-term but strengthens towards the intermediate- and long-term. In addition, there are other notable heterogeneities regarding the amount of information content for the different sectors’ and at different investment horizons from oil price uncertainty. Taken together, our results demonstrate that the direction and magnitude of the response of clean energy sectors to oil market uncertainty vary across sectors, and depend on market conditions and investment horizons. We document some crucial market conditions as well as horizon-specific implications for portfolio diversification for clean energy investors and portfolio managers.

Straightening skewed markets with an index tracking optimizationless portfolio

Among professionals and academics alike, it is well known that active portfolio management is unable to provide additional risk-adjusted returns relative to their benchmarks. For this reason, passive wealth management has emerged in recent decades to offer returns close to benchmarks at a lower cost. In this article, we first refine the existing results on the theoretical properties of oblique Brownian motion. Then, assuming that the returns follow skew geometric Brownian motions and that they are correlated, we describe some statistical properties for the \emph{ex-post}, the \emph{ex-ante} tracking errors, and the forecasted tracking portfolio. To this end, we develop an innovative statistical methodology, based on a benchmark-asset principal component factorization, to determine a tracking portfolio that replicates the performance of a benchmark by investing in a subset of the investable universe. This strategy, named hybrid Principal Component Analysis (hPCA), is applied both on normal and skew distributions. In the case of skew-normal returns, we propose a framework for calibrating the model parameters, based on the maximum likelihood estimation method. For testing and validation, we compare four alternative models for index tracking. The first two are based on the hPCA when returns are assumed to be normal or skew-normal. The third model adopts a standard optimization-based approach and the last one is used in the financial sector by some practitioners. For validation and testing, we present a thorough comparison of these strategies on real-world data, both in terms of performance and computational efficiency. A noticeable result is that, not only, the suggested lean PCA-based portfolio selection approach compares well versus cumbersome algorithms for optimization-based portfolios, but, also, it could provide a better service to the asset management industry.

Decision Making in Real Estate: Portfolio Approach

Abstract An investment policy is suggested about assets on real estate markets. Such analysis recommends investments in non-financial assets and optimization of the results from such decisions. The formalization of the investment policy is based on the portfolio theory for asset allocation. Two main criteria are applied for the decision making: return and risk. The decision support is based on Mean-Variance portfolio model. A dynamical and adaptive investment policy is derived for active portfolio management. Sliding procedure in time with definition and solution of a set of portfolio problems is applied. The decision defines the relative value of the investment to which real estates are to be allocated. The regional real estate markets of six Bulgarian towns, which identify the regions with potential for investments, are compared. The added value of the paper results in development of algorithm for a quantitative analysis of real estate markets, based on portfolio theory.

PORTFOLIO MANAGEMENT OF ENERGY SAVING PROJECTS BASED ON THE MARKOVITS THEORY

The goal of the work was to identify research and compare methods of portfolio management of energy saving projects and to develop software for optimizing portfolio investments using several methods. The key elements and strategies of creating an effective investment portfolio are considered: diversification, rebalancing, active portfolio management, passive portfolio management.
 Given the basic principles of investment theory, the task of portfolio investment is to form an investment portfolio with known shares of certain assets to maximize returns and minimize risk. To solve this problem, the method of Harry Markowitz, known as modern portfolio theory, was chosen. This is the theory of financial investment, in which statistical methods are used to make the most profitable risk distribution of the securities portfolio and income valuation, its components are asset valuation, investment decisions, portfolio optimization, evaluation of results. From a mathematical point of view, the problem of forming an optimal portfolio is the problem of optimizing a quadratic function (finding the minimum) with linear constraints on the arguments of the function.
 Methods of optimization of portfolios of energy saving projects taking into account the specifics of the subject area are analyzed. According to the results of the analysis, the methods of finding the maximum Sharpe’s ratio and the minimum volatility from randomly generated portfolios were chosen.
 A software application has been developed that allows you to download data, generate random portfolios and optimize them with selected methods. A graphical display of portfolio optimization results has also been implemented. The program was tested on data on shares of energy saving companies. The graphs built by the program allow the operator to better assess the created portfolio of the energy saving project.

Decision Support for Portfolio Management by Information System with Black–Litterman Model

An algorithm is derived for the development of portfolio decision-support information service. The algorithm allows being automated evaluations for the definition and solution of portfolio problems. Small set of historical data of asset returns with limited set of assets are used for the portfolio, which is the case for no institutional portfolio manager. The algorithm applies analytical relations for decreasing the computational workload for the estimation of the market parameters due to the limited number of assets. The subjective expert views in the Black–Litterman (BL) model are defined from additional assessment of historical data of the asset returns. The algorithm makes comparisons of the results for active portfolio management from the mean variance (MV) model, the BL one and the equal-weighted investment strategy. Benefits of the algorithm are the usage of small set of historical data and limited number of assets, which are proved in investment rolling horizon.

Evaluating the Performance and Ability Explain of Market Index Returns by Selected Stock Portfolios Based on Throughput Accounting Criteria in Comparison with the New Network Matrix Model

One of the strategies used in active portfolio management is the matrix model which can be used to form different portfolios with different characteristics of stocks or companies. In this study, with the data of 156 companies listed on the Tehran Stock Exchange during the period 2011 to 2018 using the network matrix model and based on throughput accounting criteria, portfolio formation and their performance with the portfolios of the new network matrix model (Defensive, neutral and aggressive stocks) and market portfolio were compared. The results show that the proposed network matrix model portfolios based on throughput accounting criteria have higher performance than the new network matrix model in terms of Sharpe, Sortino, upside potential, and omega criteria. Also, portfolios consisting of stocks of companies with high system performance, in addition to the above criteria, have higher performance in terms of Jensen's Alpha criteria than the new network matrix model, and in terms of upside potential and omega criteria, have higher performance than the market portfolio. The performance of portfolios consisting of stocks of companies with low system performance has a stronger correlation with the market portfolio compared to the new network matrix model.

Application of modified Black-Litterman model for active portfolio management

An active policy for portfolio optimization is developed based on repetitive application of modified Black-Litterman (BL) portfolio model and new formal definition of the expert views. New subjective views are defined which are based on the differences between the historical mean asset returns and their implied return values. An algorithm for the implementation of active management with the modified BL model is derived. The active management policy allows using short time series of historical data of assets, providing portfolio optimization with limited set of assets. New market point is evaluated, because the small set of assets does not allow market index to be used as characteristics of the market. The new formalization of the expert views allows to be compared the Mean Variance and BL portfolios on common basis. The experiments and comparisons between the Mean Variance optimization and the modified BL problem give advantages to the last one.

Time-varying Black–Litterman portfolio optimization using a bio-inspired approach and neuronets

The Black–Litterman model is a very important analytical tool for active portfolio management because it allows investment analysts to incorporate investor’s views into market equilibrium returns. In this paper, we define and study the time-varying Black–Litterman portfolio optimization under nonlinear constraints (TV-BLPONC) problem as a nonlinear programming (NLP) problem. More precisely, the nonlinear constraints refer to transaction costs and cardinality constraints. Furthermore, a speedy weights-and-structure-determination (WASD) algorithm for the power-activation feed-forward neuronet (PFN) is presented to solve time-series modeling and forecasting problems. Inhere, the investor’s views in the TV-BLPONC problem are considered as a forecasting problem and, thus, they are produced by the WASD-based PFN. In addition, using the beetle antennae search (BAS) algorithm a computational method is introduced to solve the TV-BLPONC problem. For all we know, this is an innovative approach that integrates modern neural network and meta-heuristic optimization methods to provide a solution to the TV-BLPONC problem in large portfolios. Our approach is tested on portfolios of up to 90 stocks with real-world data, and the results show that it is more than 30 times faster than other methods. Our technique’s speed and precision are verified in this way, showing that it is an outstanding alternative to ordinary methods. In order to support and promote the findings of this work, we have constructed two complete MATLAB packages for the interested user, which are freely available through GitHub.

Machine Learning for Active Portfolio Management

Machine learning (ML) methods are attracting considerable attention among academics in the field of finance. However, it is commonly believed that ML has not transformed the asset management industry to the same extent as other sectors. This survey focuses on the ML methods and empirical results available in the literature that matter most for active portfolio management. ML has asset management applications for signal generation, portfolio construction, and trade execution, and promising findings have been reported. Reinforcement learning (RL), in particular, is expected to play a more significant role in the industry. Nevertheless, the performance of a sample of active exchange-traded funds (ETF) that use ML in their investments tends to be mixed. Overall, ML techniques show great promise for active portfolio management, but investors should be cautioned against their main potential pitfalls. <b>TOPICS:</b>Big data/machine learning, portfolio construction, exchange-traded funds and applications, performance measurement <b>Key Findings</b> ▪ Machine learning (ML) methods have several advantages that can lead to successful applications in active portfolio management, including the ability to capture nonlinear patterns and a focus on prediction through ensemble learning. ▪ ML methods can be applied to different steps of the investment process, including signal generation, portfolio construction, and trade execution, with reinforcement learning expected to play a more significant role in the industry. ▪ Empirically, the investment performance of ML-based active exchange-traded funds is mixed.

Mean–variance combining rules that outperform naïve diversification

The mean–variance framework developed by Markowitz (1952). Portfolio selection, The Journal of Finance, 7(1), 77–91 is still the major model used nowadays in asset allocation and active portfolio management. However, the estimated mean–variance rules often fail to deliver superior performance compared with the simple naïve rule (the equally weighted portfolio) due to the problem of estimation errors. In this paper, I propose a portfolio construction method that is effective in dealing with estimation errors in the optimization process. Particularly, I specify the portfolio weights as an optimal combination of the equally weighted portfolio and a sample zero-investment portfolio. I show analytically that the proposed method alleviates the problem of estimation errors and dominates naïve diversification. I suggest two implementable versions of the combining method and show, empirically, their good performances relative to the naïve rule. The newly developed rules work well, particularly, for portfolios with a medium and high number of assets. Moreover, the outperformance persists generally even in the presence of transaction costs. Since the combinations are theory-based, my study may be interpreted as reaffirming the usefulness of the Markowitz portfolio theory in practice.

Profitability and Risk of Selected Forms of Investment on the Polish Capital Market

Abstract Subject and purpose of work: The subject of this study concerns profitability and risk of selected forms of investment on the Polish capital market. The aim of the study was to compare profitability and risk of active and passive investment portfolios. Materials and methods: The research material consisted of the rates of return on investments in the undervalued shares and growth shares included in the WIG-20 index and the results of the Lyxor WIG 20 UCITS ETF fund in 2014-2019. The study compares the rates of the return and standard deviations of returns of the portfolios managed actively and passively. Results: The analysis of the rates of return of the created investment portfolios indicates a higher profitability and a lower risk of the passive form of investment consisting in the purchase of an ETF fund. Among active strategies the growth investing strategy showed a higher profitability and a lower risk. Conclusions: Passive forms of investment are an effective alternative to active portfolio management.

Applications of Game- Theory in Active Strategic Portfolio Management- the Case of Hedge - Funds Adaptation for the Real Constraints in Romanian Capital Market

Abstract The application is focused on strategies for portfolio management in the case of hedge-funds for emerging markets taking into account the severe constraints for a real-world implementation. In the case of Romanian capital market, the design of a hedge-fund architecture should respond to the typical constraints for using alternative strategies. Beyond the liquidity problems there exits only a limited set of alternative instruments and strategies with impact on diversification, on the functionality and efficiency. The objective is to develop a better understanding of alternative actions and innovations for real adaptation of the architecture of a hedge-fund at these emerging market conditions, especially the lack of short and hedging instruments and the liquidity problems. Based on this new innovative framework that could capture the value of multiple rotating satellite sub-portfolio paradigm, as an active strategy, it is possible to build a different paradigm for active portfolio management in a dynamic manner. Based on an adequate dynamic of rotation of these sub-portfolios it results an optimal risk- return-liquidity profile for the whole hedge-fund portfolio, adaptable for different contexts.

Aspects Regarding a New Methodology for Active Portfolio Management of Hedge Funds Alternative in Emerging Markets-the Case of Romanian Capital Market in the Actual Context of Post-Crisis Recovery

Abstract In emerging markets, the processes in portfolio management should be adapted according to the typical constraints (market liquidity aspect and other market imperfections)that limits the use of alternative instruments / strategies and diversification capabilities. The aim is to develop a new way to understand and implement innovative solutions in real portfolio management in the case of Romanian capital market. The innovation is based on a scalable hedge-fund (HF) structure that capture different alternative instruments. This HF architecture represents a versatile dynamic AIF, equipped with capabilities to integrate the synergies between diversification based on alternative instruments but also the diversification based on alternative strategies and it integrates a core thematic sub-portfolio and 2-4 satellite rotating sub-portfolios capable to compensate the impossibility to use short sales and leverage strategies.

Investors’ risk perception in the context of efficient market hypothesis: A conceptual framework for malaysian and indonesian stock exchange

The advocates of the Efficient Market Hypothesis (EMH) theory postulates that share prices depict all the available information concerning its intrinsic worth. EMH espouses the Random Walk Theory i.e. future stock returns cannot be predicted based on past movement patterns. Contrary to that, there are believers of the Adaptive Market Hypothesis (AMH) who have questioned the adaptability of EMH and argues that market efficiency and investor’s risk perception varies across time, thus, stock returns can be predicted through active portfolio management. Various Studies have argued on market efficiency debate for developed markets, however, limited studies have examined the same for emerging markets such as Malaysia and Indonesia, which are most volatile among ASEAN-5 indices. Therefore, the primary objective of this study is to conceptualize the manifestation of efficient market hypothesis and investors’ risk perception in volatile markets of Malaysia (Kuala Lumpur Composite Index) and Indonesia (Jakarta Composite Index) by testing the 10 years (2010-2019) of daily, weekly and monthly data for the return predictability. The findings of this study will provide insight into stock market behavior to help investors to better strategize their portfolio investment positioning to reap the most efficient risk-based return.

Суверенные инвестиционные фонды: уроки коронакризиса

The paper deals with the sovereign wealth funds’ (SWFs) reactions to the economic and geo-political turbulence of 2020 emerged from COVID 19 pandemic. In recent decade, SWFs as state-run investment vehicles have proved to be the fastest growing group of institutional investors and have noticeably strengthened their role in the global financial system. According to the latest estimates, their cumulative AUM reached 9.1 billion US dollars as of December 2020. There are persuasive evidences that top 10 SWFs from Norway, China, Singapore, Persian Gulf and some other nations have evolved into full-fledged market-driven investors with an active portfolio management strategy. The fact that they continue to contribute the lion’s share (around 2/3) into the integral figures of SWFs’ performance as a group explains why the pandemic failed to undermine their progress. Also, it should be taken in mind that, in contrast to GFC of 2008–2009, the recent coronacrisis was featured by a seemingly surprising gap between the deep recession in real sector businesses and eventual growth of basic indexes of capital markets. Generally, this turned good for the financial market community, including many SWFs. For instance, the world’s largest Norwegian GPFG, despite an urgent sellout of some of its assets in order to help the national government in financing somewhat undermined state budget in the situation of deep slump at world oil market, reported, by the end of the year, the 10.9% operational profit and 8.2% increment of AUM (in terms of local currency). In the upcoming years, we may see the state funds vehicles to increasing change the whole landscape of the global investment playfield. It is highly likely that the post-COVID turbulent environment will increasingly push the SWFs not only to seek for viable alternatives to the increasingly fading traditional fixed-income instruments but to fundamentally change the mandates and the modes of operation. The traditional stabilization and saving state investment funds will step down in favor of a subgroup of the so called strategic SWFs, such as Singaporean Temasek, Chinese CIC, Saudi PIF, Indian NIIF, Malaysian Khazanah, Turkish TVF, Bahraini Mubadala, and Russian RDIF. Basically, they are charged to operate as the catalysts of national economic and social development. It may be expected that in the near future this category of state investors will see a growing number of newcomers.

The Impact of the Obama and Trump Presidential Election Cycles on the S and P500

ABSTRACT. The relationship between the US presidential election cycle and the S&P500 index is examined in this paper. We focus our attention on President Barack Obama and President Donald Trump presidential election cycles, as there is a research gap examining these two presidency periods. A comparative study is limited as Obama served two terms in office while Trump only one. Daily data from January 20, 2009 to November 16, 2020 was analysed. The selected dataset covers Obamas two terms and Trumps single term in office to examine cointegration (long-run relationships) and volatility dynamics exhibited by the S&P500. The study is framed around economic and political uncertainty that was captured by the US Economic Policy Uncertainty index (EPU). The ARDL, the LASSO and the GARCH models were implemented to facilitate the analysis of long-run dynamics, and volatility patterns, and to deal with modelling over parameterisation issues. The results suggest the existence of a long-run relationship for both the Obama and Trump administrations, showing that the EPU index could be connected to the behaviour of the S&P500. The long-term association between political uncertainty and the performance of the S&P500 offers insights into the importance of monitoring market volatility during presidential cycles as investors can generate profit and losses derived of markets reactions to the presidential cycle, suggesting the importance of an active portfolio management strategy during times of election. The research findings indicate low-levels of volatility persistence registered during Obamas second presidential cycle. Higher levels of market uncertainty were identified during the first cycle, results that are not surprising, as the Obamas first term in office was impacted by the global economic and financial crisis. This period was characterised by a delicate economic and financial climate, followed by government active intervention to address the effects of the crisis. In the context of the Trump admin- istration, his single term in office was shaken by the outburst of the global health crisis. Volatility persistence levels exhibited during Obama's first term in office were manifest again towards the end of Trump administration, mirroring the impact of the new health crisis and the administration lack of strategy and incoherent approach to the domestic and international impact of COVID-19.

Volatility Connectedness between Clean Energy Firms and Crude Oil in the COVID-19 Era

The work investigates the volatility connectedness between oil price and clean energy firms over the period 2011–2020 (including the COVID-19 outbreak). Using the volatility spillover models, and dynamic conditional correlation, we are able to identify the volatility spillover effect between these financial markets and its implications for portfolio diversification. The results indicate a significant change in both static and dynamic volatility connectedness around the COVID-19 outbreak. For instance, total connectedness index changes from 21.36% (pre-COVID-19) to 61.23% (COVID-19). This finding shows the strong effect of the COVID-19 pandemic on these financial markets. Furthermore, we show how the WTI oil from the volatility transmitter (before the outbreak of the pandemic) becomes a risk receiver after the start of the global pandemic COVID-19. Our findings indicate that recent pandemic intensified volatility spillovers, supporting the financial contagion effects. Finally, we determine the optimal hedge ratios and portfolio weights. The estimates provided suggest the need for active portfolio management, taking into account the distinct characteristics of each sector and thus, the firm. For example, the optimal weight analysis shows how the clean sector has become important in optimal diversification strategies. Our results can be used for portfolio decisions and regulatory policymaking, particularly in the current context of high uncertainty.

Portfolio performance under tracking error and asset weight constraints

Orientation: Active portfolio managers must simultaneously maximise excess returns (over benchmarks), limit risk and observe constraints on, for example, tracking errors (TRs), betas and asset weights. Research purpose: Determining the range of possible risk and returns attainable by such constrained portfolios is of interest to active portfolio managers. Weight restrictions reduce the range of achievable returns. This work demonstrates the magnitude of these reductions. Motivation for the study: This research installs and augments an approach that ascertains the effect on a TR (active) constrained portfolio in absolute risk–return space. The effects are displayed in risk–return space, demonstrating the impact on such constraints. Research approach/design and method: A theoretical approach to plot the constant TR frontier was used. Theoretical and quantitative analytical approaches to establish changes in the constant TR frontier on a simulated (but highly stylistic) market portfolios were also employed. Main findings: Considerable reduction is observed in possible investable portfolios, even for limited asset weight restrictions. This effect is amplified if multiple restrictions are imposed simultaneously, driven by both a reduced area in risk–return space enclosed by the constant TR frontier and changes in the frontier long-axis slope. Practical/managerial implications: The change in the long-axis slope sign is also a feature of changing economic conditions, thereby acting as an early warning signal with associated ramifications for asset managers. Contribution/value add: The combined effects on active portfolio performance of TR and asset weight constraints have not been investigated and demonstrated before.

Establishing the risk denominator in a Sharpe ratio framework for share selection from a momentum investment strategy approach

Background: Based on the static mean-variance portfolio optimisation theory, investors will choose the portfolio with the highest Sharpe ratio to achieve a higher expected utility. However, the traditional Sharpe ratio only accounts for the first two moments of return distributions, which can lead to false portfolio performance diagnostics with the presence of asymmetric, highly skewed returns. Aim: With many criticising the standard deviation's applicability and with no consensus on the ascendency of which other risk denominator to consult, this study contributes to the literature by validating the importance of consulting value-at-risk as the more commendable risk denominators for the Johannesburg Stock Exchange. Method: These results were derived from a novel index approach that produces a comprehensive risk-adjusted performance evaluation score. Results: Of the 24 Sharpe ratio variations under evaluation, this study identified the value-at-risk Sharpe ratio as the better variation, which led to more profi table share selections for long-only portfolios from a one-year and five-year momentum investment strategy perspective. However, the attributes of adjusting for skewness and kurtosis exhibited more promise from a three-year momentum investment strategy perspective. Conclusion: The results highlighted the ability to outperform the market, which further emphasised the importance of active portfolio management. However, the results also conrfimed that active and more passive equity portfolio managers will have to consult different Sharpe ratio variations to enhance the ability to outperform the market and a buy-and-hold strategy.