Core Portfolio Research Articles

Home bias and expected returns: A structural approach

We study the effect of the home bias on international asset pricing by extending the core-satellite approach of active asset allocation to an equilibrium analysis. In this framework, investors combine a common core portfolio with an active investment in their home asset. In equilibrium, the core portfolio will deviate from the global market portfolio in characteristic ways, which we exploit to propose a new test of the home premium in expected returns. Unlike previous findings, our evidence suggests that the premium is almost negligible even though the home bias is substantial. This result is mainly driven by the generally high correlation of index returns and the distribution of the relative level of the home bias across countries.

Factor Momentum and Regime-Switching Overlay Strategy

Investors are faced with challenges in diversifying risks and protecting capital during crash periods. In this article, the authors incorporate regime information in the portfolio optimization context by identifying regimes for historical time periods using an ℓ₁-trend filtering algorithm and exploring different machine learning techniques to forecast the probability of an upcoming stock market crash. They then apply a regime-based asset allocation to nominal risk parity strategy. Investors can further improve their investment performance by implementing a dollar-neutral factor momentum strategy as an overlay in conjunction with the core portfolio. The authors demonstrate that the time-series factor momentum strategy generates high risk-adjusted returns and exhibits pronounced defensive characteristics during market crashes. A volatility scaling approach is employed to manage the risk and further magnify the benefits of factor momentum. Empirical results suggest that the approach improves risk-adjusted returns by a substantial amount over the benchmark from both the standalone perspective and the contributory perspective. <b>Key Findings</b> ▪ The authors identify historical regimes with ℓ₁-trend filtering and implement a regime-switching risk parity strategy with supervised learning methods to optimize the core portfolio allocation. ▪ By adding a long–short factor momentum strategy on top of the core diversified portfolios, the authors are able to further enhance the portfolio’s risk-adjusted return. ▪ The factor momentum strategy exhibits defensive characteristics during crashes, and its risks can be further managed by scaling the leverage based on the realized volatility.

Home Bias and Expected Returns: A Structural Approach

We study the effect of the home bias on international asset pricing by extending the core-satellite approach of active asset allocation to an equilibrium analysis. In this framework, investors combine a common core portfolio with an active investment in their home asset. In equilibrium, the core portfolio will deviate from the global market portfolio in characteristic ways, which we exploit to propose a new test of the home premium in expected returns. Unlike previous findings, our evidence suggests that the premium is almost negligible even though the home bias is substantial. This result is mainly driven by the generally high correlation of index returns and the distribution of the relative level of the home bias across countries.

Naïve diversification in thematic investing: heuristics for the core satellite investor

In recent years, thematic exchange-traded funds (ETF) have given core satellite strategies a new impetus. Thematic investing attempts to participate in certain trends, or to serve any conceivable subjective interest such as ethics and sustainability by supplementing the corresponding ETFs to conventional ones. Hence, the question arises how to weight the thematic satellite in relation to the diversified core portfolio. Complex research and factor models are hardly suitable for private investors, and the short history of thematic products would not provide reliable information anyway. Therefore, this study develops naive diversification for thematic core satellite investors and provides three heuristics. The first strategies focus on portfolio and stock amounts; the later considers minimum concentration as an allocation rule based on the Herfindahl index. The heuristics prove to be useful and competitive to provide diversification regarding volatility of portfolio returns compared to a minimum variance optimization in out-of-sample tests. Hence, this study offers some pragmatic and truly practical aid for thematic investors.

Structuring stock investment funds with a limited number of qualified students

PurposeThe purpose of this paper is to outline the problems encountered by a student-managed investment program (SMIP) when the pool of qualified finance majors is limited in number. Restructuring the program to a single-semester course and opening the class to motivated/intelligent non-finance majors increased the number of applicants, but resulted in alternative difficulties, particularly time constraints and inadequate student preparedness. A prerequisite exam and regimented classroom structure were the solutions.Design/methodology/approachThe paper discusses the problems encountered and solutions devised to address the early year difficulties experienced by a newly developed SMIP at a relatively small university. The core of the paper chronicles the classroom approach to solving the main problem of a single-semester portfolio management course, the handling of an investment learning curve in a short period of time.FindingsThough empirically limited due to the program’s infancy, portfolio performance has been encouraging and student feedback exceptional. Regarding the former, stocks purchased by the fund have created greater wealth in total than that of equal dollar investments in an S&P500 index fund.Practical implicationsUniversities interested in running a student-managed fund should feel secure in a one-semester approach, regardless of talent pool size, as measured by the number of motivated, intelligent finance majors.Originality/valueAside from the uniqueness of requiring a mastery of entrance exam investing materials prior to the first class, this paper’s outline of core portfolio management activities includes several strategies and methods meant to streamline the process within a groupthink design.

ROLE OF GRAPHIC DESIGN FOR THE INNOVATION PROCESS OF AN EDUCATION PROJECT

Increasingly, society is faced with the need to acquire a great deal of educational and cultural information, which is constantly produced by science in a short time. This fact makes more and more the teaching system to walk in search of innovating its tools that, in an objective way and with the desired efficiency, aim to supply this demand. This article illustrates the importance of graphic design in the production of various support materials that help in the generation of content for the different courses that are offered and make up the core portfolio of transdisciplinary studies of the present university. The importance of the role of the graphic designer in the organizational structure of the nucleus has been shown to be of high relevance, including gaining a prominent role in making decisions about the process of creating a given course.

Big Data in Portfolio Allocation: A New Approach to Successful Portfolio Optimization

In the classic mean-variance portfolio theory as proposed by Harry Markowitz, the weights of the optimized portfolios are directly proportional to the inverse of the asset correlation matrix. However, most contemporary portfolio optimization research focuses on optimizing the correlation matrix itself, and not its inverse. In this article, the author demonstrates that this is a mistake. Specifically, from the Big Data perspective, she proves that the inverse of the correlation matrix is much more unstable and sensitive to random perturbations than is the correlation matrix itself. As such, optimization of the inverse of the correlation matrix adds more value to optimal portfolio selection than does optimization of the correlation matrix. The author further shows the empirical results of portfolio reallocation under different common portfolio composition scenarios. The technique outperforms traditional portfolio allocation techniques out of sample, delivering nearly 400% improvement over the equally weighted allocation over a 20-year investment period on the S&P 500 portfolio with monthly reallocation. In general, the author demonstrates that the correlation inverse optimization proposed in this article significantly outperforms the other core portfolio allocation strategies, such as equally weighted portfolios, vanilla mean-variance optimization, and techniques based on the spectral decomposition of the correlation matrix. The results presented in this article are novel in the data science space, extend far beyond financial data, and are applicable to any data correlation matrixes and their inverses, whether in advertising, healthcare, or genomics.

Big Data in Portfolio Allocation

In the classic mean-variance portfolio theory as proposed by Harry Markowitz, the weights of the optimized portfolios are directly proportional to the inverse of the asset correlation matrix. However, most of contemporary portfolio optimization research focuses on optimizing the correlation matrix itself, and not its inverse. In this article, the author demonstrates that this is a mistake. Specifically, from the Big Data perspective, she proves that the inverse of the correlation matrix is much more unstable and sensitive to random perturbations than the correlation matrix itself. As such, optimization of the inverse of the correlation matrix adds more value to optimal portfolio selection than that of the correlation matrix. The author further shows the empirical results of portfolio reallocation under different common portfolio composition scenarios, and outperforms traditional portfolio allocation techniques out-of-sample, delivering nearly 400% improvement over the equally-weighted allocation over a 20-year investment period on the S&P 500 portfolio with monthly reallocation. In general, the author demonstrates that the correlation inverse optimization proposed in this article significantly outperforms the other core portfolio allocation strategies, such as equally-weighted portfolios, vanilla mean-variance optimization, and the techniques based on the spectral decomposition of the correlation matrix. The results presented in this article are novel in Data Science space, extend far beyond financial data, and are applicable to any data correlation matrices and their inverses, whether in advertising, healthcare or genomics.

Robo Advisors: quantitative methods inside the robots

Robo Advisors (RAs) are perhaps the most important disruptive trend in wealth and asset management today. There is an immense amount of information about RA systems, but still little is known about the core portfolio optimization and asset allocation methods applied within such systems. Thus, to date, there is no comprehensive analysis of the methods used in RAs, their occurrences in these systems, the respective volumes of assets under management (AuM), and the future methodological prospects of the RAs. We analyzed 219 existing RAs worldwide and showed that Modern Portfolio Theory remains the main framework used in RAs. The current trend is to improve and augment this framework rather than applying and developing entirely new approaches. However, we also revealed that the AuM volumes tend to be higher for the systems applying newer and more sophisticated methods. In general, there is a clear gap between the predominant methods applied in RAs and new methodological developments. In the future, as the RA services mature, we can expect that the RAs system will adopt many of the new approaches since they promise good performance and have certain marketing potential.

Core Versus Satellite: How Much Should a Taxable Investor Allocate to the Core Equity Portfolio?

This article examines the question facing taxable investors: How much of the equity portfolio should be invested in a tax-managed core portfolio versus active satellite managers? Historical simulations, which vary the level of active management skill, demonstrate the trade-offs between excess returns, turnover, and taxes. The authors’ results suggest that even in the presence of skilled active managers, the allocation to the core portfolio should exceed 50% for most investors. For taxable investors with moderate levels of aversion to risk, they find that the optimal allocation to the core portfolio should typically be greater than 50%, even if one expects satellite managers to deliver pretax excess returns of up to 4%. If investors are highly risk averse or expect lower levels of alpha from the satellite managers, then the optimal allocation is higher. <b>TOPICS:</b>Portfolio theory, portfolio construction, legal/regulatory/public policy, performance measurement

Optimal acquisition and remanufacturing policies for multi-product remanufacturing systems

This paper studies an acquisition and remanufacturing problem in a market-driven multi-product remanufacturing system, where the remanufacturer acquires multiple used products (cores) of highly variable quality under independently uncertain demand and carbon emissions regulation. In order to balance between the environmental and economic benefit, the problem is formulated as a nonlinear programming model in presence of the quality variability of cores and carbon tax scheme, subject to budget and risk constraints. Since the average cost of the remanufactured products is independent of the remanufacturing quantity, the model is then decomposed into two separated sub-problems, namely the remanufacturing problem and the acquisition problem. In the remanufacturing problem, the optimal remanufacturing policies, including the optimal remanufacturing cost thresholds and the optimal remanufacturing rate, are derived. In the acquisition problem, the optimal policies of core portfolio, acquisition quantities, and remanufacturing quantities can be derived. The acquisition problem, viewed as an extended Multi-Product Newsvendor Problem (MPNP), is a convex optimization problem. Furthermore, a two-tier bisection method with polynomial computational complexity can be developed to obtain the optimal policies based on the Karush-Kuhn-Tucker (KKT) conditions. Finally, numerical experiments are provided to show the application of the model and the effect of the quality variability.

Application of CNC Programming Technology in the during of Processing for the Key Parts of Disappearing Mould

Disappearing mould casting is a new accurately processes with nearly free margin and precision forming, in which is no modulus, no parting, no sand core, so the casting without splash, burr and taper angle. It can improve the dimensional accuracy of the core portfolio for complex surface of the key compose of the disappearing mould. It has completed the design and manufacturing of these by the three-dimensional CAD/CAM technology, and obtains a series of effective methods and experience for the development of the products. The researched results have important directive to production practices for the design and machining process for the expendable pattern casting.

Liability Index Fund: The Beta Portfolio for LDI

This article deals with a Custom Liability Index (CLI) as the best representation of the client objective when it is a liabilitydriven objective. Once installed, all asset functions should be in harmony with the true objective, unlike tradition where the objective is market driven, leading to an asset-only operation. Asset allocation should focus on the funded ratio (market value of assets and liabilities), and alpha and beta need to be redefined with a liability objective. The core portfolio should be a Liability Index Fund (beta portfolio). Alpha is the excess return versus liabilities, and performance measurement should be the risk–reward comparison of asset growth versus liability growth. All of these asset functions require a CLI to measure and manage. <b>TOPICS:</b>Mutual fund performance, volatility measures, portfolio construction

HP Transforms Product Portfolio Management with Operations Research

Hewlett-Packard (HP) offers many innovative products to meet diverse customer needs. The breadth of its product offering has helped the company achieve unparalleled market reach; however, it has come with significant costs and challenges. By offering multiple similar products, a manufacturer increases its overall demand volatility, reduces forecast accuracy, and can adversely affect revenue and costs across the entire product life cycle. At HP, these impacts included increases in inventory-driven costs and order-cycle time; liabilities to channel partners; and costs of operations, research and development, marketing, and administration. Furthermore, complexity in HP's product lines confused customers, sales representatives, and channel partners, sometimes driving business to competitors. HP developed two powerful operations research-based solutions for managing product variety. The first, a framework for screening new products, uses custom-built return-on-investment (ROI) calculators to evaluate each proposed new product before introduction; those that do not meet a threshold ROI level are targeted for exclusion from the proposed lineup. The second, HP's Revenue Coverage Optimization (RCO) tool, which is based on a fast, new maximum-flow algorithm, is used to manage product variety after introduction. By identifying a core portfolio of products that are important to order coverage, RCO enables HP businesses to increase operational focus on their most critical products. These tools have enabled HP to increase its profits across business units by more than $500 million since 2005. Moreover, HP has streamlined its product offerings, improved execution, achieved faster delivery, lowered overhead, and increased customer satisfaction and market share.

Equity Home Bias and Regret: An International Equilibrium Model

IWe develop an equilibrium international asset pricing model assuming that investors treat foreign assets as an asset class separate from domestic assets, their core portfolio. When the foreign portfolio underperforms the domestic portfolio, investors feel regret of having invested abroad. Such an attitude could be justified by numerous behavioral heuristics often mentioned in the home bias literature. Following regret theory, investors are assumed to construct their portfolios taking simultaneously into account their aversion to risk and to regret. In equilibrium, the expected return of foreign equity relative to domestic equity is equal to that dictated by the traditional CAPM plus a regret premium. This regret premium is a function of the difference of regret aversion across investors and net foreign positions. With similar regret aversion worldwide, the traditional CAPM pricing relation holds, even though investors exhibit home bias in their portfolio holdings. In equilibrium, it is sufficient to have regret aversion in a single country to observe home bias in portfolio holdings of investors from all countries. An implication of the model is that countries with larger home bias should have lower expected return on their equity markets. The article is theoretical, but provides some model calibration and a crude and simple test that lends support to this conclusion.

Improving performance for long-term investors: wide diversification, leverage, and overlay strategies

Long-term investors can markedly improve their investment performance by incorporating specialized ‘overlay’ securities/strategies in conjunction with widely diversified and leveraged multi-stage portfolios. The overlays require no dedicated capital beyond the core portfolio, providing higher risk-adjusted portfolio returns than approaches based on traditional leverage. A primary example involves the futures market for commodities, currencies and fixed income. These liquid markets display novel patterns of returns relative to traditional equity/bond asset categories. We measure benefits via back tests with several fixed-mix rules, as well as within a stochastic program.

Core/Satellite Strategies for the High-Net-Worth Investor

Although a core/satellite structure makes a great deal of sense on a pretax basis, it makes even more sense on an after-tax basis. Implementing the strategy “optimally,” however, is crucial to a positive outcome. Of note, the strategy must use a broad, tax-managed core portfolio. High-net-worth investors should consider adopting the core/satellite structure, but they must be aware of its limitations, such as high transition/tax costs and noisy inputs.

The Ideal Blend of Growth and Value

Style-based investment strategies have flourished in the United States. Recently, international style-based strategies have also been increasing. In addition, many investment professionals have advocated diversified style-based portfolios, that is, portfolios holding both growth and value funds. In practice, however, it is not clear that style-based diversification works well to increase return and decrease risk. The authors9 empirical analysis strongly suggests that style-based diversification in the U.S. is not ideal. They find that a well-constructed, genuinely style-neutral core portfolio will likely generate higher long-term risk-adjusted performance in the U.S. than comparable growth and value portfolios. Internationally, the results are not as clear. Although international style-based portfolios outperformed core funds, the relatively new and changing style environment, coupled with the strong growth bias of international core funds, suggests that these historical results may not be indicative of future results.

Core Specialization, Performance, and the Market for International Analysts

In this article, we investigate the relationship between core specialization, earnings forecast accuracy, and employment prospects of international equity analysts over the period 1996-2002. We find that forecast accuracy is higher for stocks that are in an analyst's core portfolio, and analysts provide more accurate forecasts when core stocks account for a larger fraction of their portfolios. However, analysts whose entire portfolio is comprised of core stocks provide less accurate forecasts than those for whom non-core stocks account for a relatively small fraction of their portfolio. These results bring out the tensions between the benefits that arise through a higher degree of specialization and the potential informational benefits that arise because of a broader perspective about the economy when an analyst also follows firms outside her core in a less specialized research portfolio. With regards to employment prospects, we find that the market for international analysts does reward analysts for higher forecast accuracy through more favorable employment outcomes, and this relation is more sensitive to performance in the core than in the non-core. Thus, international analysts appear to be rewarded by favorable job outcomes primarily because of their performance in their core portfolio. Finally, for the same level of performance, we find that an analyst is less likely to get a more favorable job outcome if the core size is larger, suggesting that a higher standard is applied to analysts with simpler portfolio tasks. The novel contribution of our paper is that, unlike the existing literature that focuses on the accuracy of forecasts over an analyst's entire portfolio, it explicitly makes the distinction between stocks that are inside and outside the core of an analyst's research portfolio. This distinction helps us reconcile the conflicting evidence on the relation between overall forecast accuracy and task complexity. It also allows us to provide deeper insights into the determinants of analyst forecast accuracy and analyst employment prospects. Finally, our finding of differences in forecast accuracy between core stocks versus non-core stocks in an analyst's research portfolio should be of interest to all market participants. Specifically, they need to be keenly aware that not all stocks are equal in assessing the reliability of an analyst's forecasts for any given stock in her research portfolio.

Optimal Allocation of a Taxable Core and Satellite Portfolio Structure

Optimal Allocation of a Taxable Core and Satellite Portfolio Structure