scholarly journals The Optimal Allocation for Capital Preservation: an Evidence Australian Portfolio

2018 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Riznaldi Akbar
Keyword(s):  
Asset Allocation ◽  
Optimal Allocation ◽  
Risk Measure ◽  
Alternative Measure ◽  
Annual Maximum ◽  
Expected Return ◽  
Risky Assets ◽  
Asset Class ◽  
Maximum Drawdown ◽  
Investor Preference

<p>This study analyzes optimal asset mix for Australian portfolios with the main investment objective for capital preservation. An alternative measure of risk of annual maximum drawdown has been used to reflect investor preference for capital preservation as opposed to conventional risk measure of standard deviation and variance. The contribution of the study is two folds. First, this study has put different perspective to look at portfolio risk in the view of capital preservation. Second, the optimal weight for asset class mix that minimizes annual maximum drawdown has been analyzed for the case of Australian market. The results suggest that for capital preservation, investors should expect lower returns and need to put a greater allocation on less risky assets such as cash or bond. To this end, cash and bond have provided stable long term annual returns along with contained level of annual maximum drawdowns. In contrast, when investors demand higher expected return, they should increase asset allocation into stocks (equities) market at the expense of higher maximum drawdowns.</p><p><strong>Bahasa Indonesia Abstrak</strong>: <em>Studi ini menganalisis bauran aset optimal untuk portofolio Australia dengan tujuan investasi utama untuk pelestarian modal. Ukuran alternatif risiko penarikan maksimum tahunan telah digunakan untuk mencerminkan preferensi investor untuk pelestarian modal dibandingkan dengan ukuran risiko konvensional standar deviasi dan varians. Kontribusi dari penelitian ini adalah dua lipatan. Pertama, penelitian ini telah menempatkan perspektif yang berbeda untuk melihat risiko portofolio dalam pandangan pelestarian modal. Kedua, bobot optimal untuk campuran kelas aset yang meminimalkan penarikan maksimum tahunan telah dianalisis untuk kasus pasar Australia. Hasilnya menunjukkan bahwa untuk pelestarian modal, investor harus mengharapkan pengembalian yang lebih rendah dan perlu menempatkan alokasi yang lebih besar pada aset yang kurang berisiko seperti uang tunai atau obligasi. Untuk tujuan ini, uang tunai dan obligasi telah memberikan pengembalian tahunan jangka panjang yang stabil bersama dengan tingkat penarikan maksimum tahunan. Sebaliknya, ketika investor meminta pengembalian yang diharapkan lebih tinggi, mereka harus meningkatkan alokasi aset ke pasar saham (ekuitas) dengan mengorbankan penarikan maksimum yang lebih tinggi</em></p>

scholarly journals CVaR Robust Mean-CVaR Portfolio Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Maziar Salahi ◽  
Farshid Mehrdoust ◽  
Farzaneh Piri
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Risk Measure ◽  
Optimization Technique ◽  
Large Error ◽  
Data Uncertainty ◽  
Final Decision ◽  
Estimation Risk ◽  
Expected Return ◽  
The Impact

One of the most important problems faced by every investor is asset allocation. An investor during making investment decisions has to search for equilibrium between risk and returns. Risk and return are uncertain parameters in the suggested portfolio optimization models and should be estimated to solve the problem. However, the estimation might lead to large error in the final decision. One of the widely used and effective approaches for optimization with data uncertainty is robust optimization. In this paper, we present a new robust portfolio optimization technique for mean-CVaR portfolio selection problem under the estimation risk in mean return. We additionally use CVaR as risk measure, to measure the estimation risk in mean return. To solve the model efficiently, we use the smoothing technique of Alexander et al. (2006). We compare the performance of the CVaR robust mean-CVaR model with robust mean-CVaR models using interval and ellipsoidal uncertainty sets. It is observed that the CVaR robust mean-CVaR portfolios are more diversified. Moreover, we study the impact of the value of confidence level on the conservatism level of a portfolio and also on the value of the maximum expected return of the portfolio.

PRIX – A risk index for global private investors

2017 ◽  
Vol 18 (2) ◽  
pp. 214-231 ◽  
Author(s):  
Sebastian Stöckl ◽  
Michael Hanke ◽  
Martin Angerer
Keyword(s):  
Asset Allocation ◽  
Risk Measures ◽  
Risk Measure ◽  
Risk Index ◽  
Portfolio Risk ◽  
Dynamic Asset Allocation ◽  
Content Type ◽  
Private Investor ◽  
Asset Class ◽  
Private Investors

Purpose The purpose of this paper is to create a universal (asset-class-independent) portfolio risk index for a global private investor. Design/methodology/approach The authors first discuss existing risk measures and desirable properties of a risk index. Then, they construct a universal (asset-class-independent) portfolio risk measure by modifying Financial Turbulence of Kritzman and Li (2010). Finally, the average portfolio of a representative global private investor is determined, and, by applying the new portfolio risk measure, they derive the Private investor Risk IndeX. Findings The authors show that this index exhibits commonly expected properties of risk indices, such as proper reaction to well-known historical market events, persistence in time and forecasting power for both risk and returns to risk. Practical implications A dynamic asset allocation example illustrates one potential practical application for global private investors. Originality/value As of now, a risk index reflecting the overall risk of a typical multi-asset-class portfolio of global private investors does not seem to exist.

scholarly journals Grading Investment Diversification Options in Presence of Non-Historical Financial Information

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 692
Author(s):  
Clara Calvo ◽  
Carlos Ivorra ◽  
Vicente Liern ◽  
Blanca Pérez-Gladish
Keyword(s):  
Risk Measure ◽  
Statistical Information ◽  
Expected Returns ◽  
Financial Assets ◽  
Expected Return ◽  
Additional Information ◽  
Subjective Risk ◽  
The Impact ◽  
Statistical Risk ◽  
Reduction Index

Modern portfolio theory deals with the problem of selecting a portfolio of financial assets such that the expected return is maximized for a given level of risk. The forecast of the expected individual assets’ returns and risk is usually based on their historical returns. In this work, we consider a situation in which the investor has non-historical additional information that is used for the forecast of the expected returns. This implies that there is no obvious statistical risk measure any more, and it poses the problem of selecting an adequate set of diversification constraints to mitigate the risk of the selected portfolio without losing the value of the non-statistical information owned by the investor. To address this problem, we introduce an indicator, the historical reduction index, measuring the expected reduction of the expected return due to a given set of diversification constraints. We show that it can be used to grade the impact of each possible set of diversification constraints. Hence, the investor can choose from this gradation, the set better fitting his subjective risk-aversion level.

DYNAMIC ASSET ALLOCATION FOR TARGET DATE FUNDS UNDER THE BENCHMARK APPROACH

2021 ◽  
pp. 1-26
Author(s):  
Jin Sun ◽  
Dan Zhu ◽  
Eckhard Platen
Keyword(s):  
Asset Allocation ◽  
Dynamic Asset Allocation ◽  
Growth Optimal Portfolio ◽  
Risky Assets ◽  
Target Date ◽  
Target Date Funds ◽  
Asset Allocations ◽  
Investment Choices ◽  
Glide Paths

ABSTRACT Target date funds (TDFs) are becoming increasingly popular investment choices among investors with long-term prospects. Examples include members of superannuation funds seeking to save for retirement at a given age. TDFs provide efficient risk exposures to a diversified range of asset classes that dynamically match the risk profile of the investment payoff as the investors age. This is often achieved by making increasingly conservative asset allocations over time as the retirement date approaches. Such dynamically evolving allocation strategies for TDFs are often referred to as glide paths. We propose a systematic approach to the design of optimal TDF glide paths implied by retirement dates and risk preferences and construct the corresponding dynamic asset allocation strategy that delivers the optimal payoffs at minimal costs. The TDF strategies we propose are dynamic portfolios consisting of units of the growth-optimal portfolio (GP) and the risk-free asset. Here, the GP is often approximated by a well-diversified index of multiple risky assets. We backtest the TDF strategies with the historical returns of the S&P500 total return index serving as the GP approximation.

scholarly journals Portfolio Optimization Constrained by Performance Attribution

2021 ◽  
Vol 14 (5) ◽  
pp. 201
Author(s):  
Yuan Hu ◽  
W. Brent Lindquist ◽  
Svetlozar T. Rachev
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Selection Effect ◽  
Conditional Value At Risk ◽  
Positive Role ◽  
Performance Attribution ◽  
Dow Jones Industrial Average

This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize conditional value-at-risk and investigate two performance attributes, asset allocation (AA) and the selection effect (SE), as constraints on asset weights. The test portfolio consists of stocks from the Dow Jones Industrial Average index. Values for the performance attributes are established relative to two benchmarks, equi-weighted and price-weighted portfolios of the same stocks. Performance of the optimized portfolios is judged using comparisons of cumulative price and the risk-measures: maximum drawdown, Sharpe ratio, Sortino–Satchell ratio and Rachev ratio. The results suggest that achieving SE performance thresholds requires larger turnover values than that required for achieving comparable AA thresholds. The results also suggest a positive role in price and risk-measure performance for the imposition of constraints on AA and SE.

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.

Selecting Mutual Funds for Retirement Accounts (B)

2018 ◽  
pp. 1-16
Author(s):  
Phillip A. Braun
Keyword(s):  
Mutual Funds ◽  
Optimal Allocation ◽  
Portfolio Theory ◽  
Equity Fund ◽  
Expected Return ◽  
Modern Portfolio Theory ◽  
Related Case ◽  
Retirement Plan ◽  
Retirement Accounts ◽  
Northwestern University

Alice Monroe was an admissions officer at the Kellogg School of Management at Northwestern University. It was early January 2017 and Alice had enrolled in Northwestern's 403(b) retirement plan two months earlier. After spending a considerable amount of time examining the mutual funds available through the university's retirement plan, Alice had picked two to invest in: a large-cap equity growth fund and a mid-cap equity fund. (See the related case "Selecting Mutual Funds for Retirement Accounts (A).") Her initial allocations were 50% of her investment dollars in each fund. Upon further reflection, however, she realized these initial allocations were somewhat simplistic. She recalled, from an investments class she had taken at college, the topic of modern portfolio theory, which held that by adding more funds to her portfolio she might be able to achieve greater diversification and thereby reduce the overall risk of her portfolio and/or achieve a higher expected return. Alice now was considering adding an intermediate-term bond fund and a real estate fund to her retirement account. She hoped to use modern portfolio theory to prove that these new funds would indeed help her diversify her portfolio. If they did, she would also reassess her portfolio weights to determine the optimal allocation.

Portfolio Optimization and Asset Allocation With Metaheuristics

2021 ◽  
pp. 78-96
Author(s):  
Jhuma Ray ◽  
Siddhartha Bhattacharyya ◽  
N. Bhupendro Singh
Keyword(s):  
Portfolio Optimization ◽  
Portfolio Selection ◽  
Asset Allocation ◽  
Value At Risk ◽  
Optimal Allocation ◽  
Short Note ◽  
Objective Functions ◽  
Cardinality Constraints ◽  
Distribution Of Wealth ◽  
Risk Optimization

Portfolio optimization stands to be an issue of finding an optimal allocation of wealth to place within the obtainable assets. Markowitz stated the problem to be structured as dual-objective mean-risk optimization, pointing the best trade-off solutions within a portfolio between risks which is measured by variance and mean. Thus the major intention was nothing else than hunting for optimum distribution of wealth over a specific amount of assets by diminishing risk and maximizing returns of a portfolio. Value-at-risk, expected shortfall, and semi-variance measures prove to be complex for measuring risk, for maximization of skewness, liquidity, dividends by added objective functions, cardinality constraints, quantity constraints, minimum transaction lots, class constraints in real-world constraints all of which are incorporated in modern portfolio selection models, furnish numerous optimization challenges. The emerging portfolio optimization issue turns out to be extremely tough to be handled with exact approaches because it exhibits nonlinearities, discontinuities and high-dimensional, efficient boundaries. Because of these attributes, a number of researchers got motivated in researching the usage of metaheuristics, which stand to be effective measures for finding near optimal solutions for tough optimization issues in an adequate computational time frame. This review report serves as a short note on portfolio optimization field with the usage of Metaheuristics and finally states that how multi-objective metaheuristics prove to be efficient in dealing with portfolio selection problems with complex measures of risk defining non-convex, non-differential objective functions.

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.

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