Value-weighted Market Portfolio Research Articles

Riding the 1/N Premium

This article proposes a simple and effective rotation strategy. The strategy rotates between the value-weighted market portfolio (VW) and the equal-weighted counterpart (EW) based on an implicit market signal—the lagged one-month market return. The authors report a statistically significant relation between the lagged one-month market return and the future 1/N premium, which is the return difference between the EW and VW portfolios. Building on the predictive quality and exploiting the time-varying nature of the 1/N premium, they introduce a transparent and robust investment strategy that yields superior absolute and risk-adjusted returns.

Measuring the Carbon Exposure of Institutional Investors

The authors introduce a simple definition of carbon-intensive firms to measure institutional investors’ exposure to the emission intensities of portfolio companies. The definition is based on major emission industry sectors identified by the Intergovernmental Panel on Climate Change (IPCC). All firms in these industries are classified as carbon-intensive. The authors show that 13F institutions, on average, gradually reduce their carbon exposure relative to the US value-weighted market portfolio, from overweighting stocks of high-emission firms by 0.5% in 2001 to underweighting by 0.2%–0.7% since 2015, consistent with the view that investors have become more concerned about the financial implications of climate change. Compared with firm-level emission data obtained from vendors, the authors’ measure is free from selection issues and can be extended to early periods and to international markets, as it only depends on industry classifications and covers all stocks. The authors do not find the same divestment trend before 2000, when climate risks were less eminent. TOPIC:ESG investing Key Findings • We introduce a simple definition of carbon-intensive firms to measure institutional investors’ exposure to the emission intensities of portfolio companies. • 13F institutions have become more concerned about the financial implications of climate change. Relative to the US value-weighted market portfolio, they have gone from overweighting high-emission stocks by 0.5% in 2001 to underweighting by 0.2%–0.7%since 2015. • Compared with firm-level emission data obtained from vendors, our measure is free from selection issues and can be extended to early periods and to international markets.

In-Sample and Out-of-Sample Sharpe Ratios of Multi-Factor Asset Pricing Models

For many multi-factor asset pricing models proposed in the recent literature, their implied tang-ency portfolios have substantially higher sample Sharpe ratios than that of the value-weighted market portfolio. In contrast, such high sample Sharpe ratio is rarely delivered by professional fund managers. This makes it difficult for us to justify using these asset pricing models for performance evaluation. In this paper, we explore if estimation risk can explain why the high sample Sharpe ratios of asset pricing models are difficult to realize in reality. In particular, we provide finite sample and asymptotic analyses of the joint distribution of in-sample and out-of-sample Sharpe ratios of a multi-factor asset pricing model. For an investor who does not know the mean and co-variance matrix of the factors in a model, the out-of-sample Sharpe ratio of an asset pricing model is substantially worse than its in-sample Sharpe ratio. After taking into account of estimation risk, our analysis suggests that many of the newly proposed asset pricing models do not provide superior out-of-sample performance than the value-weighted market portfolio.

Mean-Variance Optimization Using Forward-Looking Return Estimates

Despite its theoretical appeal, Markowitz mean-variance portfolio optimization is plagued by practical issues. It is especially difficult to obtain reliable estimates of a stock's expected return. Recent research has therefore focused on minimum volatility portfolio optimization, which implicitly assumes that expected returns for all assets are equal. We argue that investors are better off using the implied cost of capital based on analysts' earnings forecasts as a forward-looking return estimate. Correcting for predictable analyst forecast errors, we demonstrate that mean-variance optimized portfolios based on these estimates outperform on both an absolute and a risk-adjusted basis the minimum volatility portfolio as well as naive benchmarks, such as the value-weighted and equally-weighted market portfolio. The results continue to hold when extending the sample to international markets, using different methods for estimating the forward-looking return, including transaction costs, and using different optimization constraints.

Testing for prospect and Markowitz stochastic dominance efficiency

We develop non-parametric tests for prospect stochastic dominance Efficiency (PSDE) and Markowitz stochastic dominance efficiency (MSDE) using block bootstrap resampling. Under the appropriate conditions we show that they are asymptotically conservative and consistent. We employ Monte Carlo experiments to assess the finite sample size and power of the tests. We use the tests to empirically establish whether the value-weighted market portfolio is the best choice of every individual with preferences exhibiting certain patterns of local attitudes towards risk. Our results indicate that we cannot reject the hypothesis of prospect stochastic dominance efficiency for the market portfolio. This is supportive of the claim that the particular portfolio can be rationalized as the optimal choice for any S-shaped utility function. Instead, we reject the hypothesis for Markowitz stochastic dominance, which could imply that there exist reverse S-shaped utility functions that do not rationalize the market portfolio.

An investor’s perspective on risk-models and characteristic-models

Purpose In capital markets, research risk factor loadings and characteristics are considered as opposing explanations for the cross-sectional dispersion in average stock returns. However, there is little known about the performance an investor would obtain who believes either in the characteristics explanation (CB-investor) or in the risk factor loadings explanation (RB-investor). The purpose of this paper is to compare the performance of CB- and RB-investors. Design/methodology/approach To compare the competing strategies, the authors propose a simple new approach to equity portfolio optimization in the style of Brandt et al. (2009) by modeling the portfolio weight in each asset as a function of the asset's risk factor loadings or characteristics. The authors perform an empirical analysis on the German stock market, exploiting the risk factor loadings from the Carhart (1997) four-factor model and the respective characteristics size, book-to-market equity ratio and momentum. Findings The results show that investment strategies relying on characteristics (particularly on momentum) outperform risk-based investment strategies in horse races. These findings hold in- and out-of-sample. Furthermore, the characteristics-based investment strategies outperform a value-weighted market portfolio strategy in- and out-of-sample. Originality/value The authors introduce a portfolio optimization approach that enables investors to directly link portfolio decisions to the firm’s characteristics or risk factor loadings.

The principal–agent problem, tracking error, and the optimal investment portfolio

The principal seeks a portfolio manager to manage funds. The principal uses a tracking-error constraint that restricts the portfolio’s volatility. Without the constraint, the manager may increase the portfolio return by adding high-variance assets. Tests assume that the benchmark is the value-weighted market portfolio less the risk-free rate (MKT). Tests then add the Fama and French four long–short portfolios to test if they can increase return without a large increase in the portfolio variance. SMB (Small Minus Big) is long in small stocks and short in big stocks and picks up the small-firm premium. HML (High Minus Low) is long in high book-to-market stocks and short in low book-to-market stocks and picks up the value premium. WML (Winners Minus Losers) is long in past winners and short in past losers and picks up the momentum premium. Tests find that SMB adds tracking error and small returns, HML adds tracking error and large returns, and WML adds modest tracking error and large returns. WML requires heavy trading. Net of trade costs, the WML gain disappears. However, the trade execution costs to hold HML are modest. Net of trade costs, we see a gain from adding HML to MKT.

A combined analytic hierarchy process and goal programming approach to international portfolio selection in the presence of investment barriers

The aim of this paper is to develop an integrated multiple criteria decision making approach combining the analytic hierarchy process (AHP) and the goal programming (GP) model to study the impact of a mixture of investment barriers on international portfolio selection, and therefore the home bias puzzle from the viewpoint of G-7 investors over the period 2001–2009. The AHP is used to determine the suitable international equity portfolios with respect to seven barriers to international investment. The GP model, incorporating the market weights of the maximum return, minimal variance, and AHP portfolios is formulated to determine the optimal international equity portfolios. The main results show that the AHP-GP optimal international portfolio weights are different from those predicted by the I-CAPM. Also, except for French and US investors, home bias values determined according to the AHP-GP portfolios are lower than those calculated on the basis of the value-weighted world market portfolio.

A Combined Analytic Hierarchy Process and Goal Programming Approach to International Portfolio Selection in the Presence of Investment Barriers

The aim of this paper is to develop an integrated multiple criteria decision making approach combining the analytic hierarchy process (AHP) and the goal programming (GP) model to study the impact of a mixture of investment barriers on international portfolio selection, and therefore the home bias puzzle from the view point of G-7 investors over the period 2001-2009. The AHP is used to determine the suitable international equity portfolios with respect to seven barriers to international investment: Information costs, investor behavior (optimism or pessimism toward financial market returns), geographical distance, transaction costs, expropriation risk, financial market size, and capital flow restrictions. The GP model, incorporating the market weights of the maximum return, minimal variance, and AHP portfolios is formulated to determine the optimal international equity portfolios. The main results show that the AHP-GP optimal international portfolio weights are different from those predicted by the I-CAPM. Also, except for French and US investors, home bias values determined according to the AHP-GP portfolios are lower than those calculated on the basis of the value weighted market portfolio. Hence, a combined AHP-GP model, as a more realistic approach to international portfolio selection, confirms the presence of the home bias and that this bias is generally less important than that obtained by applying a traditional approach.

Can Individual Investors Capture The Value Premium?

I test the performance of several simple one and two-factor mechanical GARP and value investment strategies against a value-weighted market portfolio for the period 1998-2006, focusing on the suitability of the strategies for individual investors. All of the GARP and value strategies produce substantially higher average returns than the market portfolio over the 97 rolling one-year holding periods included in the study. The strategies have a higher standard deviation of returns across the 97 start months, but are less likely than the market portfolio to experience negative returns over any three or five-year time period. Overall, the best performing strategies are EBIT to enterprise value and return on capital, EBIT to enterprise value alone, and earnings yield (the inverse of the P/E ratio). Adding a profitability factor to form a GARP strategy from a simple one-factor value strategy does not appear boost performance, though it does reduce risk in the EBIT to enterprise value and return on capital strategy. My results indicate that individual investors who are able to tolerate occasional underperformance should consider using a GARP or value strategy in at least a portion of the portion of their portfolio that is allocated to U.S. equities.

Liquidity Risk, Firm Risk, and Issue Risk Premium Effects on the Abnormal Returns to New Issues of Convertible Bonds

I. INTRODUCTION A large body of empirical evidence demonstrates that the issuance of new CB's is associated with negative abnormal returns of the underlying shares (1). This is puzzling since: (1) the market normally reacts positively to the straight corporate bond issuance; (2) CB's have payoff structures entail a straight bond component and an equity option component; and (3) in spite of the negative equity market reaction to CB issuance, the market for CB's has grown rapidly over time (2). One possible resolution of the puzzle that has been adduced is that due to dilution effects, investors perceive CB's as equity from the outset, rather than debt (3). In this vein, according to the pecking order theory, the market reaction should be negative. However, the evidence that we provide in this paper demonstrates that dilution effects are negligible. What then explains the abnormal equity performance of firms that issue CBs? This paper serves to provide new evidence on this score. In particular, we examine the underlying firm characteristics that serve as drivers of the abnormal returns when CB's are issued. The focus is on the relationship between the short-term wealth effect (4) around the issuance of CBs and the characteristics of issuer firms and the features embodied in the issues themselves. In particular, we examine the impact of three factors suggested in Liu and Switzer (2009): liquidity risk (logarithm of issue size), issue risk (Vega, which measures the sensitivity of CB value to the volatility of underlying stocks), and firm volatility risk (standard deviation of beta (5)) on the abnormal returns to convertible bond issues. We find that all of these three risk factors serve as significant drivers for the abnormal returns around the CB issue date. The market responds favourably to the issuance of convertible bonds by issuers with mild level of firm volatility risk. However, liquidity risk and issue risk are significantly negatively related to performance. The latter two risk components serve to offset the risk management benefits of convertibles for firms. These findings are robust to different grouping criteria and estimation methods. The remainder of this paper is organized as follows. Section II describes the data used in this paper. Section III explains the methodology and proxies employed in this study. Results are reported in Section IV. The paper concludes with a summary in Section V. II. DATA DESCRIPTION The sample consists of all CB offerings from January 1, 1986 to December 31, 2005 for which the underlying shares are traded on the New York Stock Exchange (NYSE), the American Exchange (AMEX), or the over-the-counter (NASDAQ) market from the SDC Platinum database. The basic CB data, including conversion price, coupon rate, expiry date, issue date, ratings, and issue size are obtained from SDC. Missing observations from SDC are replaced with data from the Convertible Bond Database that was provided to us from Morgan Stanley. The underlying stock prices of the above identified firms are from CRSP. During the observation period, stock prices are adjusted for stock dividends or splits. The market index returns are also from CRSP. We employ three market proxies in our tests: returns on the value-weighted market portfolio, returns on an equally-weighted market portfolio, and returns on the level of the Standard & Poor's 500 Composite Index. Company financial data are obtained from the Standard & Poor's Compustat database. Firms are included in the analyses if they have complete data on cash flow, working capital, investment in fixed assets, the real tax rate, growth rates in assets and sales, and various size and risk measures. Market expectations for CB issuers are proxied by analysts' opinions, as reported in IBES, which includes the estimation of earnings per share, cash flow per share, sales, or operating profit, and the Buy/Hold/Sell recommendations. …

Temporary Components of Stock Prices: New Univariate Results

While there is growing evidence that stock prices do not follow pure random walks, the degree of existence of temporary components in stock prices is not well known. Modelling stock prices as the sum of a random walk and a general stationary (predictable) component, we propose an estimable lower bound on the proportion of total stock return variance caused by the predictable component. Contrary to the absolute value of the first-order autocorrelation coefficient estimates of Fama and French (1988), this lower bound reasonably estimates the true variance roportion in finite samples also when the temporary component does not follow a first-order autoregressive process. The estimated mean values of the lower bound reach a maximum of 10% for the equal-weighted market portfolio of NYSE stocks over the post-war period 1947-1986, while the maximum is 25% for the pre-war period 1926-46. The value-weighted market portfolio exhibits generally smaller variance proportion estimates. We also reexamine the pure random walk hypothesis using our univariate variance proportion statistic.

Market portfolio efficiency and value stocks

In this journal, Best, Best, and Yoder (2000) recently demonstrated that portfolios of U.S. value stocks dominate portfolios of U.S. growth stocks in terms of second-order stochastic dominance (SSD). We cannot conclude from this finding that the market is SSD inefficient, however, because market portfolio efficiency generally does not require growth portfolios to be efficient. Furthermore, stochastic dominance results are very sensitive to sampling error. In fact, the value-weighted market portfolio is not significantly inefficient, and no significant value effects exist in the sample of Best, Best, and Yoder.

Assessing the Size of Asset Pricing Tests under Perfect Ex Ante Efficiency

The capital asset-pricing model (CAPM) asserts that in equilibrium the market portfolio is the tangency portfolio of the efficient frontier spanned by all risky assets. Conceptually, the tangency portfolio represents an ex ante efficient portfolio, while the market portfolio represents an ex post one. In practice, these two portfolios differ in that the value-weighted market portfolio corresponds to a buy-and-hold investment strategy, whereas the tangency portfolio relates to a constant-weight rebalanced strategy. Traditional tests of (ex ante) portfolio efficiency (e.g., the famous test of Gibbons, Ross, and Shanken (GRS, 1989)), however, have mostly focused on the efficiency of the market portfolio. Hence, the size of such tests are distorted because the tests are not correctly specified for the underlying null hypothesis of ex ante efficiency. Using simulations, we find that traditional tests, including the GRS and the GMM tests, seriously overreject the efficiency of the market portfolio under various ditributional specifications. The over-rejection problem is especially severe when the efficiency of the value-weighted market portfolio is tested with respect to equal-weighted portfolios.

Does Risk Seeking Drive Asset Prices? A Stochastic Dominance Analysis of Aggregate Investor Preferences

We investigate whether risk seeking or non-concave utility functions can help to explain the cross-sectional pattern of stock returns. For this purpose, we analyze the stochastic dominance efficiency classification of the value-weighted market portfolio relative to benchmark portfolios based on market capitalization, book-to-market equity ratio and momentum. We use various existing and novel stochastic dominance criteria that account for the possibility that investors exhibit local risk seeking behavior. Our results suggest that Markowitz type utility functions, with risk aversion for losses and risk seeking for gains, can capture the cross-sectional pattern of stock returns. The low average yield on big caps, growth stocks and past losers may reflect investors' twin desire for downside protection in bear markets and upside potential in bull markets.

Individual Decision Making and Investor Welfare

This article analyses and quantifies the costs of suboptimal decision making for an investor with a multi‐period horizon. In light of the empirical evidence that investors are too conservative and hold portfolios that are insufficiently diversified, we evaluate the costs of suboptimal equity participation both analytically and using simulation, and also estimate the costs of suboptimal diversification using simulation. We find that suboptimal leverage imposes only modest costs on the investor for reasonable parameter values. While the costs of inadequate diversification can be very high, we find that, because of the higher returns on small firms, an equally weighted portfolio of as few as five randomly chosen firms can provide the same level of expected utility as the value weighted market portfolio.(J.E.L.: G11, G18, G23).

Temporary Components of Stock Prices: New Univariate Results

While there is growing evidence that stock prices do not follow pure random walks, the degree of existence of temporary components in stock prices is not well known. Modeling stock prices as the sum of a random walk and a general stationary (predictable) component, the paper proposes an estimable lower bound on the proportion of total stock return variance caused by the predictable component. Contrary to the absolute value of the first-order autocorrelation coefficient estimates of Fama and French (1988a), this lower bound reasonably estimates the true variance proportion in finite samples also when the temporary compo? nent does not follow a first-order autoregressive process. The estimated mean values of the lower bound reach a maximum of 10 percent for the equal-weighted market portfolio of NYSE stocks over the post-war period 1947-1986, while the maximum is 25 percent for the pre-war period 1926-1946. The value-weighted market portfolio exhibits generally smaller variance proportion estimates. The pure random walk hypothesis is also reexamined using a standard variance ratio statistic extended to multiple return horizons.

A multi-dynamic-factor model for stock returns

In this paper, we define dynamic and static factors and distinguish between the dynamic and static structure of asset excess returns. We examine the value-weighted market portfolio as a dynamic factor and propose an intuitively appealing procedure to search for more dynamic factors. We find evidence that the market is a dynamic factor but a three-dynamic-factor model is superior in modelling the decile portfolios. The two additional factors are correlated with a January dummy and Bond risk premium and with production growth and a recession dummy, respectively. We found that small firms are more sensitive to the January/Bond risk factor, while large firms are more sensitive to the Production/Recession factor. We found that after accounting for the systematic risk corresponding to the three dynamic factors, there is not much of a static component of asset risk premium and there is no evidence for a higher ‘unexplained’ return on small firm portfolios.

A bilinear paradigm test of the Arbitrage Pricing Theory

The APT is estimated using a set of five economic variables: industrial production, the term structure of interest rates, default risk premium, inflation and the value-weighted market portfolio. Non-zero risk premia are found for all the factors. The bilinear paradigm test suggests that these variables are correctly hypothesized. From the perspective of domestic capital markets, there does not seem to be any difference between the sources of systematic risk for the small, open economy of Canada and the relatively large and less open economy of the U.S.