Equal-weighted Index Research Articles

Facts and Fantasies about Commodity Futures

We construct an equally-weighted index of commodity futures monthly returns over the period between July of 1959 and December of 2004 in order to study simple properties of commodity futures as an asset class. Fully-collateralized commodity futures have historically offered the same return and Sharpe ratio as equities. While the risk premium on commodity futures is essentially the same as equities, commodity futures returns are negatively correlated with equity returns and bond returns. The negative correlation between commodity futures and the other asset classes is due, in significant part, to different behavior over the business cycle. In addition, commodity futures are positively correlated with inflation, unexpected inflation, and changes in expected inflation.

International equity portfolios: Selecting the right benchmark for emerging markets

We examine two alternatives to the cap-weighted index for emerging markets: the GDP-weighted and the equally weighted (EW) indices. Over the 1990–2004 period, the GDP-weighted index dominates the cap-weighted index, and represents a promising alternative benchmark for the investment policy. The EW index dominates all indices even after reasonable transaction costs are included. Three factors seem to play a role in explaining the EW index dominance: low concentration, more frequent rebalancing and larger allocations to small countries. Managing a fraction of an equity portfolio according to an EW scheme stands for a promising avenue to add value to the policy benchmark.

Estimation of expected return: CAPM vs. Fama and French

Most practitioners favour a one-factor model (CAPM) when estimating expected return for an individual stock. For estimation of portfolio returns, academics recommend the Fama and French three-factor model. The main objective of this paper is to compare the performance of these two models for individual stocks. First, estimates for individual stock returns based on CAPM are obtained using different time frames, data frequencies, and indexes. It is found that 5 years of monthly data and an equal-weighted index, as opposed to the commonly recommended value-weighted index, provide the best estimate. However, performance of the model is very poor; it explains on average 3% of differences in returns. Then, estimates for individual stock returns are obtained based on the Fama and French model using 5 years of monthly data. This model, however, does not do much better; independent of the index used, it explains on average 5% of differences in returns. These results therefore bring into question the use of either model for estimation of individual expected stock returns.

The characterestics of the equally weighted market index and the value weighted market index and their implications for market based accounting research: The case of ase

This study examines empirically the characteristics of the two market indices developed by Amman Stock Exchange (ASE) and their related returns: the equally weighted index (EWI) and the value weighted index (VWI). The monthly‐prices for each index are obtained from ASE for the entire nine‐year period (1992‐2000) to compute market returns for both indices. The study period is divided into seven intervals of different lengths. The results of the test and the nonparametric (Wilcoxon) test indicate that the value weighted market return (VWR) tends to be significantly higher than the equally‐weighted market return (EWR). The variances of the two market returns tend to decrease as the length of the time intervals increases. Inconsistent with prior research findings, the differences between the two market returns variances failed to be significant at the 0.05 level. The study findings may have implications for capital market research that apply the market model in emerging markets, in general, and in ASE in particular.

The Predictive Value of the Signs of January Returns: Evidence of a New January Effect

Many investment practitioners rely on the 'January Barometer', namely, they believe in the theory of 'as goes January, so goes the year', which is fundamentally related to the well-known January effect. Unlike many prior studies that examined the well-known January effect per se, we investigate the issue of whether or not the signs of January returns have superior predictive value for future stock returns vis-a-vis the predictive value of the signs of returns in any other calendar month. Using NYSE equal-weighted stock index, we confirm the existence of the January effect in the sample period of 1941-2002 with both unconditional and conditional evidence, and we document two additional manifestations of the well-known January effect. We also introduce a new type of January effect, namely the signs of January returns have superior predictive value vis-a-vis the signs of any other calendar month's returns for the purpose of predicting the next 12-months' returns.

Estimation of Expected Return: CAPM vs Fama and French

Most practitioners favour a one factor model (CAPM) when estimating expected return for an individual stock. For estimation of portfolio returns academics recommend the Fama and French three factor model. The main objective of this paper is to compare the performance of these two models for individual stocks. First, estimates for individual stock returns based on CAPM are obtained using different time frames, data frequencies, and indexes. It is found that five years of monthly data and an equal-weighted index, as opposed to the commonly recommended value-weighted index, provide the best estimate. However performance of the model is very poor; it explains on average three percent of differences in returns. Then, estimates for individual stock returns are obtained based on the Fama and French model using five years of monthly data. This model, however, does not do much better; independent of the index used it explains on average five percent of differences in returns. These results provide a possible explanation for why CAPM is used so extensively by practitioners; the additional cost associated with Fama and French is not justified. However, they also bring into question the use of either model for estimation of individual stock returns.

Calendar Effects on the Amsterdam Stock Exchange

For daily data on a value-weighted index of all shares in the Netherlands (1981-1998), we find abnormally high returns in the pre-Christmas period of the second half of December and around the turn of the month, whereas returns are negative and volatility is relatively high on the Mondays where the previous week's return is below zero. Furthermore, our evidence indicates the presence of an ARCH(1) effect. Our intraday results based on an equal-weighted index of the leading shares in the Netherlands (1986-1993) reveal a U-shaped return pattern over each trading day, Monday morning excluded. Moreover, our tests reveal repeated price adjustments mostly in the same direction around and over non-trading periods. We argue that the arrival of private information is affected by strategic and behavioral factors incompatible with the risk-return paradigm.

Efficiently ARMA–GARCH estimated trading volume characteristics in thinly traded markets

ARMA-GARCH lag specification is employed to fit a model exhibiting nonsynchronous trading and volatility clustering for the Norwegian thinly traded equity market. In particular, characteristics of the conditional mean and conditional volatility inhibited in thinly traded equity markets are investigated. Trading volume is employed as a proxy measure for trading frequency. Low to no trading volume induces thin trading and non-trading effects while a relative higher trading frequency induces continuous trading. The main objective is to investigate trading frequency differences in serial correlation and cross-autocorrelation in the mean equation and volatility clustering in the volatility equation as well as any symptoms of data dependencies in the model residuals, which imply ARMA-GARCH model misspecification. BIC efficient ARMA-GARCH lag specifications are employed for the conditional mean and volatility and relevant mean and volatility parameter measures introduced that are well known from the changing volatility literature. The empirical results report consistent mean and volatility patterns over the increasing trading frequency series. Nonsynchronous trading and non-trading effects show a consistent pattern in serial correlation and cross-autocorrelation for the conditional mean and the latent volatility exhibits a consistent pattern in past shocks, past conditional volatility, persistence and weight to long-run average volatility. In contrast to the more relatively frequently traded asset series the most thinly traded series report insignificant asymmetric volatility. Moreover, for the most thinly traded series, specification tests suggest data dependence, which seems to be prolonged into the equal-weighted index series. Hence, due to serial correlation and data dependence in the model residuals the ARMA–GARCH lag specifications seem only appropriate for relatively frequently traded return series.

The Relative Efficiency of Beta Estimates

When estimation of beta is based on the Capital Asset Pricing Model the standard recommendation is to use five years of monthly data and a value-weighted index. Given the importance of the beta estimate obtained for financial decisions, such as those involved in portfolio management, capital budgeting, and performance evaluation, there is surprisingly little research evidence in support of this recommendation. The objective of this paper is to address this shortcoming. For this purpose the relative efficiency of beta estimates which result from using different data frequencies, time periods, and indexes is examined. It is found that five years of monthly data and an equal-weighted index, as opposed to the commonly recommended value-weighted index, provides the most efficient estimate.

Testing multi-beta asset pricing models

This paper presents a complete solution to the estimation and testing of multi-beta models by providing a small sample likelihood ratio test when the usual normality assumption is imposed and an almost analytical GMM test when the normality assumption is relaxed. Using 10 size portfolios from January 1926 to December 1994, we reject the joint efficiency of the CRSP value-weighted and equal-weighted indices. We also apply the tests to analyze a new version of Fama and French's [Fama, E.F., French, K.R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics. 33, 3–56.] three-factor model in addition to two standard ones, and find that the new version performs the best.

BIAS DARI PENGGUNAAN MODEL DI MBAR

This paper discusses biases that occurred on level, return and event study models. In a prices lead earnings condition, the coefficient in the level model is unbiased, while that in the return model is biased. In a prices do not lead earnings condition, both level and earnings models yield unbiased coefficients economically. In general, the level model suffers more serious bias econometrically than does the return model. In an event study, daily returns with equally-weighted index are able to detect abnormal returns better than are monthly returns with value-weighted index. When announcements are clustered in calender events, the market model or the marketadjustement model is less biased than the mean-adjusted model. But, when event dates are not clustered, a simple model such as the mean-adjusted model is not worse than other models.

Book-to-market, dividend yield, and expected market returns: A time-series analysis

We find reliable evidence that both book-to-market (B M) and dividend yield track time-series variation in expected real stock returns over the period 1926 91 (in which B M is stronger) and the subperiod 1941–1991 (in which dividend yield is stronger). A Bayesian bootstrap procedure implies that an investor with prior belief 0.5 that expected returns on the equal-weighted index are never negative comes away from the full-period B/M evidence with posterior probability 0.08 for the hypothesis (0.14 with the impact of the 1933 outlier tempered). Although this raises doubts about market efficiency, the post-1940 evidence is consistent with expected returns always being positive.

On the long-term stability and cross-country invariance of financial ratio patterns

The purpose of this study is to develop empirically-based classification patterns for twelve commonly used financial ratios and to measure the long-term stability and structural invariance of these patterns. The data are based on annual reports of U.S. and Finnish industrial firms for the periods 1947–1975 and 1974–1984, respectively. The selected financial ratios are, according to a priori classification, the measures of short-term solvency, long term-solvency, profitability and efficiency. Classification patterns of the financial ratios are developed using factor analysis and the stability and invariance analyses are carried out using transformation analysis. The results show that empirically-based classifications are not fully equivalent to the a priori classification. The following factors are found: solvency, profitability, efficiency and dynamic liquidity. The empirical results are based both on the value- and equal-weighted indices of the ratios. Classification patterns are developed using ratio indices in the first-difference form. The use of the first differences becomes necessary because of the clear trend in the time series. Further, empirical results show that different aggregation methods lead to different results. The theoretically better value-weighted indices (in the first-difference form) give more accurate and easy-to-interpret empirical results. Factor patterns based on these indices display also very clear time-series stability and cross-sectional invariance. This result confirms the great importance of aggregation method in ratio analysis.

Multivariate tests of the zero-beta CAPM

A ‘cross-sectional regression test’ (CSRT) of the CAPM is developed and its connection to the Hotelling T2 test of multivariate statistical analysis is explored. Algebraic relations between the CSRT, the likehood ratio test and the Langrange multiplier test are derived and a useful small-sample bound on the distribution function of the CSRT is obtained. An application of the CSRT suggests that the CRSP equally-weighted index is inefficient, but that the inefficiency is not explained by a firm size-effect from February to December.