Asset Allocation vs. Factor Allocation—Can We Build a Unified Method?

2019 ◽  
Keyword(s):  
Asset Allocation ◽  
Building Blocks ◽  
Unified Framework ◽  
Intuitive Appeal ◽  
Optimal Allocations ◽  
Asset Class ◽  
Asset Classes ◽  
Unified Method

There is increasing interest in the idea of allocating across factors instead of across traditional asset classes. Allocating across factors has the intuitive appeal of allocating across building blocks that are in theory purer sources of return. In practice, factor-based allocation is not easy: Factors are unobservable and must be specified. However, the authors believe there is merit in integrating insights from factors with traditional asset allocation. Information and views about factors and asset classes can be a powerful combination. In this article, the authors present a framework for combining the two paradigms in an innovative way, resulting in optimal allocations that blend insights from both paradigms. Specifically, their approach derives asset class return prediction from factor-based asset allocation, which allows construction of portfolios for various investment objectives from a unified framework.

Internationally Diversified Investment Using an Integrated Portfolio Model

1998 ◽  
Vol 01 (01) ◽  
pp. 145-160 ◽  
Author(s):  
Hiroshi Konno ◽  
Jing Li
Keyword(s):  
Capital Market ◽  
Asset Allocation ◽  
Large Scale ◽  
Historical Data ◽  
Forward Contracts ◽  
Absolute Deviation ◽  
New Approach ◽  
Asset Class ◽  
Asset Classes ◽  
Mean Variance

In this paper, we use a new integrated portfolio model which takes care of stocks and bonds of several countries to construct an internationally diversified portfolio. This serves as an alternative to the popular asset allocation strategy, in which the fund is first allocated to indices corresponding to diverse asset classes and then allocated to individual assets using appropriate models for each asset class. Our model, on the other hand, determines the allocation of the fund to individual assets in one stage by solving a large scale mean-variance or mean-absolute deviation model. Another important feature of this article is a newly developed strategy for hedging the exchange rate risk by using forward contracts on currencies. Computational experiments using historical data collected in the capital market show that the new approach can serve as a more reliable and less expensive method for allocating the fund to diverse classes of assets.

Index correlation: implications for asset allocation

2015 ◽  
Vol 41 (11) ◽  
pp. 1236-1256
Author(s):  
Allen Michel ◽  
Jacob Oded ◽  
Israel Shaked
Keyword(s):  
Standard Deviation ◽  
Asset Allocation ◽  
Market Performance ◽  
Portfolio Risk ◽  
Market Returns ◽  
Content Type ◽  
Shape Distribution ◽  
Asset Class ◽  
Asset Classes ◽  
Equity Indices

Purpose – The cornerstone of Modern Portfolio Theory with implications for many aspects of corporate finance is that reduced correlation among assets and reduced standard deviation are key elements in portfolio risk reduction. The purpose of this paper is to analyze the conditional correlation and standard deviation of a broad set of indices with the S & P 500 conditioned on market performance. Design/methodology/approach – The authors examined volatility and correlation for a set of indices for a 19-year period based on weekly data from July 2, 1993 to June 30, 2012. These included the NASDAQ, MSCI EAFE, Russell 1000, Russell 2000, Russell 3000, Russell 1000 Growth, Russell 1000 Value, Gold, MSCI EM and Dow Jones UBS Commodity. The data for the Wilshire US REIT, Barclays Multiverse, Multiverse 1-3, Multiverse 3-5 and Multiverse 10+ became available starting July 2, 2002. For these indices the authors used weekly data from July 1, 2002 through June 30, 2012. For the iBarclays TIPS, the authors used weekly data from the time of availability, namely, for the period December 12, 2003 through June 29, 2012. Findings – The findings demonstrate that both the conditional correlations and standard deviations vary as a function of market performance. Moreover, the authors obtain a U-shape distribution of correlations conditioned on market performance for equity indices, such as NASDAQ, as well as for the Wilshire REIT. Namely, correlations tend to be high when market returns are at low or high extremes. For more typical market performance, correlations tend to be low. A modified U-shape is found for bond indices and the Dow Jones UBS Commodity Index. Interestingly, the correlation between gold and the S & P 500 is unrelated to the return on the S & P. Originality/value – While it has been observed that asset classes move together, this paper is the first to systematically analyze the nature of these asset class correlations.

Asset Allocation Strategies and Commodities

2018 ◽  
Author(s):  
Claudio Boido
Keyword(s):  
Asset Allocation ◽  
Asset Management ◽  
Central Banking ◽  
Fixed Income ◽  
Fixed Income Securities ◽  
Asset Classes ◽  
Alternative Beta ◽  
Asset Managers ◽  
Selection Of ◽  
Allocation Strategies

As a result of the financial crisis of 2007–2008 and subsequent central banking decisions, the asset management industry changed its asset allocation choices. Asset managers are focusing their attention on the search for new asset classes by taking advantage of the new opportunities to capture risk premia with the aim of exceeding the returns given by traditional investments, including traded equities, fixed income securities, and cash. By doing so, they are trying to improve the selection of alternative assets, such as commodities that sometimes have relatively low correlations with traditional assets. The chapter begins by describing the principles of asset allocation, distinguishing between passive and active asset allocation, also focusing on beta and alternative beta. It then concentrates on how investors can gain exposure to commodities through different investment vehicles and strategies.

scholarly journals Street context of various demographic groups in their daily mobility

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ariel Salgado ◽  
Weixin Li ◽  
Fahad Alhasoun ◽  
Inés Caridi ◽  
Marta Gonzalez
Keyword(s):  
Geographical Information Systems ◽  
Building Blocks ◽  
Geographical Information ◽  
Unified Framework ◽  
Demographic Groups ◽  
Daily Mobility ◽  
Urban Science ◽  
Science Framework ◽  
The City ◽  
Racial Ethnic

AbstractWe present an urban science framework to characterize phone users’ exposure to different street context types based on network science, geographical information systems (GIS), daily individual trajectories, and street imagery. We consider street context as the inferred usage of the street, based on its buildings and construction, categorized in nine possible labels. The labels define whether the street is residential, commercial or downtown, throughway or not, and other special categories. We apply the analysis to the City of Boston, considering daily trajectories synthetically generated with a model based on call detail records (CDR) and images from Google Street View. Images are categorized both manually and using artificial intelligence (AI). We focus on the city’s four main racial/ethnic demographic groups (White, Black, Hispanic and Asian), aiming to characterize the differences in what these groups of people see during their daily activities. Based on daily trajectories, we reconstruct most common paths over the street network. We use street demand (number of times a street is included in a trajectory) to detect each group’s most relevant streets and regions. Based on their street demand, we measure the street context distribution for each group. The inclusion of images allows us to quantitatively measure the prevalence of each context and points to qualitative differences on where that context takes place. Other AI methodologies can further exploit these differences. This approach presents the building blocks to further studies that relate mobile devices’ dynamic records with the differences in urban exposure by demographic groups. The addition of AI-based image analysis to street demand can power up the capabilities of urban planning methodologies, compare multiple cities under a unified framework, and reduce the crudeness of GIS-only mobility analysis. Shortening the gap between big data-driven analysis and traditional human classification analysis can help build smarter and more equal cities while reducing the efforts necessary to study a city’s characteristics.

A Downside Risk Analysis of Inflation Hedging Capabilities of Individual Assets and Optimal Asset Allocation

2021 ◽  
Vol 8 (1) ◽  
pp. 80
Author(s):  
Aftab Hussain Tabassam ◽  
Zafar Iqbal ◽  
Arshad Ali Bhatti ◽  
Amna Mushtaq
Keyword(s):  
World Bank ◽  
Asset Allocation ◽  
Foreign Currency ◽  
Investment Horizon ◽  
Medium Term ◽  
Optimal Portfolios ◽  
Monthly Data ◽  
Short Run ◽  
Asset Classes ◽  
Inflation Hedging

The objective of this study is to examine the inflation hedging capabilities of most widely used asset classes in Pakistan. It also attempts to find out the possibility of creating an inflation protected optimal asset mix. The sample consists of monthly data of cash, gold, stocks, foreign currency, real estate and inflation from 2005 to 2015. The major sources of data are SBP, World Bank and Pakistan Statistics Bureau. The downside analysis of these assets concludes that cash act as an inflation hedge for all the investment horizons. The findings showed that the Gold and stocks also have inflation hedging abilities in short run which extend to medium term investment horizon for gold only, while stocks appear to be a good inflation hedge for longer investment horizons. This study also suggests that investors can strategically create optimal portfolios that are hedged against inflation.

Asset Classes and Asset Allocation

2000 ◽  
Vol 2 (4) ◽  
pp. 27-32 ◽  
Author(s):  
Jeffrey E. Horvitz
Keyword(s):  
Asset Allocation ◽  
Asset Classes

scholarly journals A critical evaluation of risk-return characteristics of environmentally focused stock’s companies

2013 ◽  
Vol 61 (2) ◽  
pp. 501-506
Author(s):  
Stanislav Škapa ◽  
Tomáš Meluzín ◽  
Marek Zinecker
Keyword(s):  
Critical Evaluation ◽  
Risk Return ◽  
Ex Post ◽  
Asset Class ◽  
Asset Classes

The objective of the paper is to critically evaluate and determine risk-return profile environmentally focused stock’s companies which are covered by STOXX Global ESG Environmental Leaders Index and whether this index should be taken in as an independent asset class of investments portfolio for its risk-return improvement. This paper gives an empirical view on the ex-post asset classes characteristics focused mainly on risk side of investment.

scholarly journals Market Timing For active Asset Allocation: A Discrete Regression Model Approach

2011 ◽  
Vol 11 (1) ◽  
pp. 125
Author(s):  
Glen A. Larsen, Jr. ◽  
Gregory D. Wozniak
Keyword(s):  
Regression Model ◽  
Asset Allocation ◽  
Market Timing ◽  
The Other ◽  
Portfolio Performance ◽  
Empirical Results ◽  
Portfolio Strategies ◽  
Fixed Weight ◽  
Asset Class ◽  
Model Approach

A discrete regression model (DRM) approach to timing the asset class markets for stocks, bonds, and cash in active asset allocation is presented. The technique involves investing in the asset class whose return is predicted to exceed the other asset class return after observing a sequential signal of estimated probabilities. The empirical results show that the DRM approach provides enhanced portfolio performance when compared to more passive fixed-weight portfolio strategies.

scholarly journals Value Return Predictability across Asset Classes and Commonalities in Risk Premia*

2020 ◽  
Author(s):  
Fahiz Baba Yara ◽  
Martijn Boons ◽  
Andrea Tamoni
Keyword(s):  
Risk Premia ◽  
Return Predictability ◽  
Expected Returns ◽  
Government Bonds ◽  
Common Component ◽  
Common Value ◽  
Asset Class ◽  
Asset Classes ◽  
Global Government ◽  
Stock Indexes

Abstract We show that returns to value strategies in individual equities, industries, commodities, currencies, global government bonds, and global stock indexes are predictable in the time series by their respective value spreads. In all these asset classes, expected value returns vary by at least as much as their unconditional level. A single common component of the value spreads captures about two-thirds of value return predictability and the remainder is asset class specific. We argue that common variation in value premia is consistent with rationally time-varying expected returns, because (i) common value is closely associated with standard proxies for risk premia, such as the dividend yield, intermediary leverage, and illiquidity, and (ii) value premia are globally high in bad times.

scholarly journals The missing link: economic exposure and pension plan risk

2015 ◽  
Vol 9 (2) ◽  
pp. 290-303
Author(s):  
Paul Sweeting ◽  
Alexandre Christie ◽  
Edward Gladwyn
Keyword(s):  
Asset Allocation ◽  
Pension Plan ◽  
Asset Returns ◽  
Oil Price ◽  
Defined Benefit ◽  
Financial Health ◽  
Defined Benefit Pension Plan ◽  
Asset Class ◽  
Normal Shape ◽  
The Impact

AbstractThe funding position of a defined benefit pension plan is often closely linked to the performance of the sponsoring company’s business. For example, a plan sponsor whose financial health is dependent on high oil prices may struggle during periods of oil price weakness. If the pension plan’s assets perform poorly at this time, the ability of the sponsor to address any funding requirement could be restricted precisely when the need for funding is heightened. In this paper, we propose an approach to dealing with joint plan and sponsor risk that can provide protection against extreme adverse events for the sponsor. In particular, adopt a strategy of minimising a portfolio’s expected losses in the event of an assumed drop of x% in the oil price. Our methodology relies on an asset allocation framework that takes into account the impact of serial correlation in asset returns, as well as the negative skewness and leptokurtosis resulting from the non-normal shape of marginal distributions of historical asset returns. We also make use of copulas to measure the dependence between asset class returns.

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