Journal of Risk and Financial Management

Research

12 pages, 447 KiB

Open AccessArticle

Factor-Based Optimization of a Fundamentally-Weighted Portfolio in the Illiquid and Undeveloped Stock Market

by Davor Zoričić, Denis Dolinar and Zrinka Lovretin Golubić

J. Risk Financial Manag. 2020, 13(12), 302; https://doi.org/10.3390/jrfm13120302 - 1 Dec 2020

Viewed by 2251

In this paper, the possibility of using fundamental weighting as a tool to intentionally tilt a portfolio toward specific and unobservable risk factors in the illiquid and undeveloped Croatian stock market is explored. Thus far, fundamental-weighting has been shown to be able to [...] Read more.

In this paper, the possibility of using fundamental weighting as a tool to intentionally tilt a portfolio toward specific and unobservable risk factors in the illiquid and undeveloped Croatian stock market is explored. Thus far, fundamental-weighting has been shown to be able to outperform the cap-weighted index in such environments but no attempt regarding control for implicit factor exposure of such portfolios has been reported. Therefore, in this study principal component analysis is performed to capture the underlying risk factors of the fundamentally-weighted portfolio in order to optimize the portfolio’s performance by minimizing its volatility. Previous attempts focusing purely on portfolio risk reduction by estimating minimum variance portfolios failed both from an in-sample and out-of-sample perspective. Results in this study are based on 22 in-sample and out-of-sample tests in the period from March 2009 till March 2020. On the in-sample estimation basis, the proposed approach significantly improves the portfolio’s performance and, if restrictions to weights are imposed, it can outperform the cap-weighted benchmark. However, out-of-sample testing yielded poor results both in terms of risk and return. Such results are in contrast to findings for the developed markets but corroborate the claim that a broad investment base is needed for successful risk exposure in the long run. Full article

(This article belongs to the Special Issue Modern Portfolio Theory)

► Show Figures

Figure 1

22 pages, 622 KiB

Open AccessArticle

University Endowment Committees, Modern Portfolio Theory and Performance

by Mimi Lord

J. Risk Financial Manag. 2020, 13(9), 198; https://doi.org/10.3390/jrfm13090198 - 3 Sep 2020

Cited by 1 | Viewed by 3945

Abstract

University endowments with broad portfolio diversification have been correlated with performance, but committees’ decision-making process has received relatively little attention. This study is unique in postulating that the committee’s learning commitment and open-mindedness are significant contributors to a decision process that is based [...] Read more.

University endowments with broad portfolio diversification have been correlated with performance, but committees’ decision-making process has received relatively little attention. This study is unique in postulating that the committee’s learning commitment and open-mindedness are significant contributors to a decision process that is based on the principles of Modern Portfolio Theory (or, simply, Portfolio Theory). The use of Portfolio Theory as a decision-making framework leads to greater portfolio diversification, which, in turn, leads to higher risk-adjusted returns. This study also demonstrates that greater committee expertise across multiple asset classes contributes to more diversified portfolios. Full article

(This article belongs to the Special Issue Modern Portfolio Theory)

► Show Figures

Graphical abstract

21 pages, 2411 KiB

Open AccessArticle

Portfolio Theory in Solving the Problem Structural Choice

by Oleg S. Sukharev

J. Risk Financial Manag. 2020, 13(9), 195; https://doi.org/10.3390/jrfm13090195 - 1 Sep 2020

Cited by 5 | Viewed by 3208

Abstract

The purpose of the article is to reveal the problem (and to determine the possibility of solving the structural choice problem) as one of the areas in modern portfolio theory development. The article also argues that portfolio analysis is a method of structural [...] Read more.

The purpose of the article is to reveal the problem (and to determine the possibility of solving the structural choice problem) as one of the areas in modern portfolio theory development. The article also argues that portfolio analysis is a method of structural analysis for various economic units. The research methodology is defined by the portfolio theory, optimization models implemented by the numerical gradient projection method, the empirical static method of analysis and simulation cases when the models are implemented. The research supported by the above- mentioned methodology aimed to reach the goal results in substantiating the structural choice. This choice differs from the classical portfolio choice as it is necessary to find how the investments are allocated for the portfolio units, and the same should be done for the characteristics points, where it is a challenge to apply the efficient set theorem, because different structures for the allocation of the resources, investments give the same or nearly the same combination of the expected return and total portfolio risk. Economic sectors characterized by the profitability and business risk are seen to be the portfolio units in terms of the macroeconomic approach from the portfolio theory developed by Tobin. Total income maximization model and total portfolio risk minimization demonstrate both the structural choice problem, including at the characteristic points, and choice dependence on the expansion of the resource allocated to the portfolio, and on the number of portfolio units. The analysis and model simulations enhance the efficient set theorem with the criteria for structural choice—income and risk correlation on the effective distribution curve, among other factors. A portfolio with two real sectors of the Russian economy illustrates that profitability and risk ratio determines the resource allocation between them under the income maximization model, so one sector grabs a more substantial resource. Thus, being a tool to support the structural choice, portfolio analysis gives structural diagnostics for the resource distribution, investments allocation by portfolio units. Full article

(This article belongs to the Special Issue Modern Portfolio Theory)

► Show Figures

Graphical abstract

26 pages, 344 KiB

Open AccessArticle

Portfolio Strategies to Track and Outperform a Benchmark

by Paskalis Glabadanidis

J. Risk Financial Manag. 2020, 13(8), 171; https://doi.org/10.3390/jrfm13080171 - 1 Aug 2020

Cited by 5 | Viewed by 3226

Abstract

I investigate the question of how to construct a benchmark replicating portfolio consisting of a subset of the benchmark’s components. I consider two approaches: a sequential stepwise regression and another method based on factor models of security returns’ first and second moments. The [...] Read more.

I investigate the question of how to construct a benchmark replicating portfolio consisting of a subset of the benchmark’s components. I consider two approaches: a sequential stepwise regression and another method based on factor models of security returns’ first and second moments. The first approach produces the standard hedge portfolio that has the maximum feasible correlation with the benchmark. The second approach produces weights that are proportional to a “signal-to-noise” ratio of factor beta to idiosyncratic volatility. Using a factor model of securities returns allows the use of a larger number of securities than the number of time periods used to estimate the parameters of the factor model. I also consider a second objective that maximizes expected returns subject to a target tracking error variance. The security selection criterion naturally extends to the product of the information ratio and the signal-to-noise ratio. The optimal tracking portfolio is either a one-fund or a two-fund portfolio rule consisting of the optimal hedging portfolio, the tangent portfolio or the global minimum variance portfolio, depending on what constraints are imposed on the objective function. I construct buy-and-hold replicating portfolios using the algorithms presented in the paper to track a widely followed stock index with very good results both in-sample and out-of-sample. Full article

(This article belongs to the Special Issue Modern Portfolio Theory)

19 pages, 2459 KiB

Open AccessArticle

Realized Measures to Explain Volatility Changes over Time

by Christos Floros, Konstantinos Gkillas, Christoforos Konstantatos and Athanasios Tsagkanos

J. Risk Financial Manag. 2020, 13(6), 125; https://doi.org/10.3390/jrfm13060125 - 13 Jun 2020

Cited by 14 | Viewed by 3680

Abstract

We studied (i) the volatility feedback effect, defined as the relationship between contemporaneous returns and the market-based volatility, and (ii) the leverage effect, defined as the relationship between lagged returns and the current market-based volatility. For our analysis, we used daily measures of [...] Read more.

We studied (i) the volatility feedback effect, defined as the relationship between contemporaneous returns and the market-based volatility, and (ii) the leverage effect, defined as the relationship between lagged returns and the current market-based volatility. For our analysis, we used daily measures of volatility estimated from high frequency data to explain volatility changes over time for both the S&P500 and FTSE100 indices. The period of analysis spanned from January 2000 to June 2017 incorporating various market phases, such as booms and crashes. Based on the estimated regressions, we found evidence that the returns of S&P500 and FTSE100 indices were well explained by a specific group of realized measure estimators, and the returns negatively affected realized volatility. These results are highly recommended to financial analysts dealing with high frequency data and volatility modelling. Full article

(This article belongs to the Special Issue Modern Portfolio Theory)

► Show Figures

Figure 1

21 pages, 502 KiB

Open AccessArticle

Analyst Forecast Dispersion and Market Return Predictability: Does Conditional Equity Premium Play a Role?

by Shuang Liu, Juan Yao and Stephen Satchell

J. Risk Financial Manag. 2020, 13(5), 98; https://doi.org/10.3390/jrfm13050098 - 16 May 2020

Viewed by 3512

Abstract

Prior studies found that analyst forecast dispersion predicts future market returns. Some prior studies attribute this predictability to the short-sale constraints in the market according to the overpricing theory. Using the U.S. data from 1981 to 2014, we find that the return predictive [...] Read more.

Prior studies found that analyst forecast dispersion predicts future market returns. Some prior studies attribute this predictability to the short-sale constraints in the market according to the overpricing theory. Using the U.S. data from 1981 to 2014, we find that the return predictive power of aggregate dispersion only exists prior to 2005. The investor sentiment index, as a proxy of short-sale constraints used by many studies, can only explain the dispersion effect prior to 2005. The investor sentiment index and other proxies such as institutional ownership and put options cannot explain the significant weakening of the dispersion effect after the global financial crisis. We argue that the dispersion-return relation is partly driven by the correlation between dispersion and conditional equity premium. Our evidence suggests that the short-sale constrained stocks do not experience a higher dispersion effect, which is contrary to what the overpricing theory predicts. Full article

(This article belongs to the Special Issue Modern Portfolio Theory)

► Show Figures

Figure 1

21 pages, 4186 KiB

Open AccessArticle

A Wavelet-Based Analysis of the Co-Movement between Sukuk Bonds and Shariah Stock Indices in the GCC Region: Implications for Risk Diversification

by Samia Nasreen, Syed Asif Ali Naqvi, Aviral Kumar Tiwari, Shawkat Hammoudeh and Syed Ale Raza Shah

J. Risk Financial Manag. 2020, 13(4), 63; https://doi.org/10.3390/jrfm13040063 - 29 Mar 2020

Cited by 18 | Viewed by 4418

Abstract

Investors are interested in knowing whether sukuk bonds and shariah stock indices in the Gulf Corporation Council (GCC) region are related. This study examines the connectedness between the sukuk- and shariah-compliant stock indices in the GCC financial markets. Bivariate and multivariate wavelet approaches [...] Read more.

Investors are interested in knowing whether sukuk bonds and shariah stock indices in the Gulf Corporation Council (GCC) region are related. This study examines the connectedness between the sukuk- and shariah-compliant stock indices in the GCC financial markets. Bivariate and multivariate wavelet approaches are applied to the daily data covering the period 10 July 2008 to 15 May 2017. The empirical findings demonstrate a strong correlation between these GCC sukuk bond indices and shariah stock indices. The degree of connectedness between these sukuk and shariah stock indices varies across time and scale. A strong and positive association is observed in the short term and a negative association is evident in the long term. The same findings are observed, using the wavelet cohesion approach that also validates the existence of portfolio diversification opportunities at a short-time horizon. The multivariate cross-correlation analysis reveals that these sukuk and shariah stock markets are highly integrated across time and scale. Furthermore, the value at risk (VaR) for the sukuk bond–shariah stocks portfolio is performed to highlight the significance of the wavelet analysis. The outcomes show that portfolio stocks are variable with respect to time or scale (time diversification). Overall, analyzing the sukuk bond–shariah stock index returns in the GCC at a multiscale level makes it easier for financial agents dealing with heterogeneous trading horizons to assess the benefits of diversifications. Full article

(This article belongs to the Special Issue Modern Portfolio Theory)

► Show Figures

Figure 1

19 pages, 1384 KiB

Open AccessArticle

The Distribution of Cross Sectional Momentum Returns When Underlying Asset Returns Are Student’s t Distributed

by Oh Kang Kwon and Stephen Satchell

J. Risk Financial Manag. 2020, 13(2), 27; https://doi.org/10.3390/jrfm13020027 - 5 Feb 2020

Cited by 4 | Viewed by 2292

Abstract

In Kwon and Satchell (2018), a theoretical framework was introduced to investigate the distributional properties of the cross-sectional momentum returns under the assumption that the vector of asset returns over the ranking and holding periods were multivariate normal. In this paper, the framework [...] Read more.

In Kwon and Satchell (2018), a theoretical framework was introduced to investigate the distributional properties of the cross-sectional momentum returns under the assumption that the vector of asset returns over the ranking and holding periods were multivariate normal. In this paper, the framework is extended to derive the corresponding results when the asset returns are multivariate Student’s t. In particular, we derive the probability density function and the moments of the cross-sectional momentum returns and examine in detail the special case of two underlying assets to demonstrate that many of the salient features reported in the empirical literature are consistent with the theoretical implications. Full article

(This article belongs to the Special Issue Modern Portfolio Theory)

► Show Figures

Figure 1

16 pages, 874 KiB

Open AccessArticle

The Equity Curve and Its Relation to Future Stock Returns

by Olaf Stotz

J. Risk Financial Manag. 2020, 13(2), 19; https://doi.org/10.3390/jrfm13020019 - 21 Jan 2020

Cited by 3 | Viewed by 2974

Abstract

Using option prices, a new method for estimating the term structure of expected stock returns (equity curve) is proposed. We analyse how the equity curve relates to future stock returns and obtain three main results. First, a higher level of the equity curve [...] Read more.

Using option prices, a new method for estimating the term structure of expected stock returns (equity curve) is proposed. We analyse how the equity curve relates to future stock returns and obtain three main results. First, a higher level of the equity curve is associated with higher future stock returns. Second, a positive slope is followed by future realized returns which are lower in the short term (1 month) than in the long term (1 quarter or 1 year). Third, a steeper slope (either positive or negative) is associated with a larger absolute difference between short-term and long-term returns. Therefore, the equity curve is consistent with theoretical predictions. We also analyse an investment strategy that uses the slope of the equity curve to determine the allocation to stocks. This strategy earns an outperformance of up to 200 basis points per annum. Full article

(This article belongs to the Special Issue Modern Portfolio Theory)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Modern Portfolio Theory

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (9 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI