An Empirical Investigation of Eastern European Bond Markets

We examine the value of Eastern European emerging bond markets to global fixed income managers. In an environment where bonds from traditional developed markets are offering modest yields, emerging market bonds with attractive yields are becoming more popular with institutional managers. Furthermore, the returns on these bonds exhibit low correlations with traditional fixed income investments and thus offer opportunities for portfolio diversification. We develop a multifactor forecasting model and estimate its parameters using a dynamic Kalman filter procedure. The forecasts are then used to construct optimal mean–variance portfolios with and without emerging market bonds. We find that the portfolios that include emerging market bonds have significantly higher Sharpe ratios.     

IPO market timing. The evidence of the disposition effect among corporate managers

Until now, IPO market timing has been mostly associated with a varying number of IPOs in certain periods of “hot” and “cold” issue markets. We would like to offer a different perspective. We focus on a speed of the IPO process, after the decision to go public was actually made. Our hypothesis is that in “hot market” managers will tend to minimize the time necessary to go public in order to take advantage of high valuations as quickly as possible. On the contrary, if the firm is not ready with the IPO on time and in the meantime the market falls during the going-public process, managers will tend to delay the IPO hoping that the good market conditions will come back soon. We argue that such a behavior might be attributed to the disposition effect among firms' managers.We find a statistically significant negative correlation between the market return and the speed of the IPO process. The absolute correlation coefficient is higher when the market return is calculated 90 days prior to the Approval Date of the prospectus than when it is calculated 90 days after the Approval Date. Hence, a vast part of the market influence on the speed of the offering process has its origin at the time when offering is formally not possible yet. External factors occurring after the Approval Date seem to be less important than the managerial decision influenced by observation of the market situation prior to the Approval Date.We also find that for firms débuting faster than the median of our sample, the average market return in the period between the IPO date and the median is positive. On the other hand, in the group of slower firms, the average market return in the period between the median and the IPO date is negative. There is an analogy between firms – débuting too fast in bullish market and too slow in bearish market, and investors – selling winning stocks too quickly and keeping falling stocks for too long in their portfolios. Both managers and investors seem to be biased by the S-shape utility function, as predicted by the prospect theory of Kahnemann and Tversky (1979).     

Coskewness timing ability in the mutual fund industry

The ability of mutual fund managers to time coskewness successfully can help them manage their portfolio’s exposure to potential losses and improve their fund’s performance. This study assesses whether mutual fund managers are able to manage the market exposure of their investment portfolios given a change in coskewness. We demonstrate that fund managers investing in Small Blend and Small Growth stocks possess the ability to time coskewness. On average, the fund managers of these two investment objectives increase the market exposures of their portfolios about 2.749 % and 1.340 %, respectively, based on their anticipations on future coskewness. Superiority is driven from the fact that the fund managers in small capitalization stocks are successfully able to manage the tail risk of their funds’ portfolios. The fund-by-fund results confirm that the number of individual funds succeeding in timing market skewness of the Small Blend and Small Growth investment objectives is larger than the remaining types. The main findings are robust when controlling for other types of timing ability, the periods of financial turbulence, and the construction of coskewness.     

Measuring Mutual Fund Performance with Characteristic-Based Benchmarks

This article develops and applies new measures of portfolio performance which use benchmarks based on the characteristics of stocks held by the portfolios that are evaluated. Specifically, the benchmarks are constructed from the returns of 125 passive portfolios that are matched with stocks held in the evaluated portfolio on the basis of the market capitalization, book-to-market, and prior-year return characteristics of those stocks. Based on these benchmarks, “Characteristic Timing” and “Characteristic Selectivity” measures are developed that detect, respectively, whether portfolio managers successfully time their portfolio weightings on these characteristics and whether managers can select stocks that outperform the average stock having the same characteristics. We apply these measures to a new database of mutual fund holdings covering over 2500 equity funds from 1975 to 1994. Our results show that mutual funds, particularly aggressive-growth funds, exhibit some selectivity ability, but that funds exhibit no characteristic timing ability.     

Is there a real estate factor premium?

In this paper, we study the variation of expected returns on five different asset portfolios in a multi-factor model. We found the presence of a real estate factor, in addition to both a stock factor and a bond factor in asset pricing. This suggests that mutual fund managers should seriously consider including real estate assets in their portfolios, since one cannot capture the real estate factor premium without having some kind of real estate exposure. Another result is that the market segmentation found in previous studies disappears in a more general model of asset pricing in which we allow for multi-factors other than the market factor to affect asset returns. This implies that real estate assets can be treated just like other assets as far as mean-variance efficient asset allocations are concerned. We also have some preliminary evidence that equity REITs and the Russell-NCREIF index are driven by the same underlying real estate factor.     

Do funds selected by managers’ skills perform better?

We construct a simple intuitive rating mechanism to evaluate stock picking and market timing skills of equity and hybrid equity fund managers in China. We find that both our skill-rated 5-star (SR-5S) fund and the Morningstar 5-star (MS-5S) fund portfolios outperform the market. The SR-5S fund portfolio outperforms its counterpart MS-5S portfolio in most situations, depending on whether portfolio performance is measured by the abnormal returns of the CAPM model, the Fama-French three-factor (FF3) model, the Carhart four-factor (CH4) model and the Fama-French five-factor (FF5) model. Both market timing skill and stock picking skill affect the performance difference between the SR-5S fund and MS-5S fund portfolios. Additionally, the departure of a SR-5S or MS-5S fund manager is associated with fund performance declines, and the declines in performance for SR-5S funds are generally larger than the declines for the MS-5S funds.     

Investing in equity mutual funds

We construct optimal portfolios of equity funds by combining historical returns on funds and passive indexes with prior views about asset pricing and skill. By including both benchmark and nonbenchmark indexes, we distinguish pricing-model inaccuracy from managerial skill. Modest confidence in a pricing model helps construct portfolios with high Sharpe ratios. Investing in active mutual funds can be optimal even for investors who believe managers cannot outperform passive indexes. Optimal portfolios exclude hot-hand funds even for investors who believe momentum is priced. Our large universe of funds offers no close substitutes for the Fama-French and momentum benchmarks.     

Government Bond Returns,Measurement of Interest Rate Risk,and the Arbitrage Pricing Theory

Empirical tests are reported for Ross' arbitrage pricing theory using monthly data for U.S. Treasury securities during the 1960–1979 period. We find that mean returns on bond portfolios are linearly related to at least two factor loadings. Multivariate test results, however, are not consistent with the APT. Our sample data in the U.S. Treasury securities market are also not consistent with either version of the CAPM. One-month-ahead forecasts of excess returns using factor-generating models are compared with corresponding naive predictions or predictions using the “market model” with various market portfolios.     

The cross-section of industry equity returns and global tactical asset allocation across regions and industries

This study investigates which index characteristics predict returns in the cross-section of local industry indexes in six regions. The results show that geographical origin and market capitalization of indexes critically determine the predictive ability of characteristics. We find that industry indexes of any market capitalization with high earnings-to-price ratio yield higher expected returns in the US, Europe, and Asia-Pacific. Recent winner (loser) portfolios in Europe have a tendency to outperform (underperform) recent loser (winner) portfolios in the near future for all groups of market capitalization. Small portfolios with high idiosyncratic volatility in Asia-Pacific earn an idiosyncratic volatility premium. Dividend yield is positively related to future returns of small European portfolios. These results are robust to the inclusion of transaction costs and control variables and have implications for portfolio managers following a global tactical asset allocation policy.     

Capital market equilibrium with heterogeneous investors

As a two-parameter model that satisfies stochastic dominance, the mean-extended Gini model is used to build efficient portfolios. The model quantifies risk aversion heterogeneity in capital markets. In a simple Edgeworth box framework, we show how capital market equilibrium is achieved for risky assets. This approach provides a richer basis for analysing the pricing of risky assets under heterogeneous preferences. Our main results are: (1) identical investors, who use the same statistic to represent risk, hold identical portfolios of risky assets equal to the market portfolio; and (2) heterogeneous investors as expressed by the variance or the extended Gini hold different risky assets in portfolios, and therefore no one holds the market portfolio.     

Leveraging a call-put ratio as a trading signal

We examine whether a put-call ratio, derived from a unique set of market data, can be used to predict directional moves in asset prices during various market conditions between March 2005 and December 2012. Our findings show: (1) specific market participant's options trading volume is a predecessor to asset price movements, and (2) portfolios based on the put-call ratio adjusted for four factors Carhart model and transaction costs exhibit abnormal excess returns.     

Portfolio Selection and Optimization with Higher Moments: Evidence from the Indian Stock Market

The Markowitz portfolio optimization model, popularly known as the Mean-Variance model, assumes that stockreturns follow normal distribution. But when stock returns do not follow normal distribution, this model wouldbe inadequate as it would prescribe sub-optimal portfolios. Stock market literature often deliberates that stock returns are non-normal. In such context the Markowitz model would not be sufficient to estimate the portfolio risks. The purpose of this paper is to expand the original Markowitz portfolio theory (mean-variance) via adding the higher order moments like skewness (third moment about the mean) and kurtosis (fourth moment about the mean) in the return characteristics. The research paper investigates the impact of including higher moments using multi-objective programming model for portfolio stock selection and optimization. The empirical results indicate that the inclusion of higher moments had a considerable impact in estimating the returns behavior of portfolios. The portfolios optimized using all the four moments, generated higher returns for the given level of risk in comparison to the returns of the Markowitz model during the study period 2000–2011. The results of this study would be immensely useful to fund managers, portfolio managers and investors as it would help them in understanding the Indian stock market behavior better and also in selecting alternative portfolio selection models.     

Life Settlement Funds: Current Valuation Practices and Areas for Improvement

We analyze the prevailing valuation practices in the life settlement industry based on a sample of 11 funds that cover a large portion of the current market. The most striking result is that a majority of asset managers seem to substantially overvalue their portfolios relative to the prices of comparable transactions that have recently been closed. Drawing on market‐consistent estimates with regard to medical underwriting, it is possible to trace back the observed discrepancies to inadequately low model inputs for life expectancies and discount rates. The main consequences are a dissimilar treatment of investor groups in open‐end funds structures as well as an unduly high compensation for managers and third parties. To address this predicament, we suggest defining life settlements as level 2 assets in the fair value hierarchy of IFRS 13, improving transparency and disclosure requirements, and developing new incentive‐compatible fee schedules.     

Do Publicly Traded Property–Casualty Insurers Cater to the Stock Market?

This article examines the catering theory in the insurance industry. We investigate whether managers of publicly traded insurers pursue a growth strategy catering to the stock market's preference. Two hypotheses are tested in this study: (1) an insurer will devote more efforts to increasing premium growth when the stock market places greater values on growth, and (2) this catering effect will be more pronounced at firms where managers have greater incentives to maximize short‐term stock prices. We find evidence supporting both hypotheses. Our study discovers a new channel through which the stock market and executive compensation affect insurance companies’ business strategies and the insurance market. The implication of the interplay between insurers and the stock market is significant and deserves future research.     

Active portfolio management with benchmarking: A frontier based on alpha

Active portfolio management often involves the objective of selecting a portfolio with minimum tracking error variance (TEV) for some expected gain in return over a benchmark. However, Roll (1992) shows that such portfolios are generally suboptimal because they do not belong to the mean-variance frontier and are thus overly risky. Our paper proposes an appealing method to lessen this suboptimality that involves the objective of selecting a portfolio from the set of portfolios that have minimum TEV for various levels of ex-ante alpha, which we refer to as the alpha-TEV frontier. Since practitioners commonly use ex-post alpha to assess the performance of managers, the use of this frontier aligns the objectives of managers with how their performance is evaluated. Furthermore, sensible choices of ex-ante alpha lead to the selection of portfolios that are less risky (in variance terms) than the portfolios that active managers would otherwise select.     

Equilibrium "Anomalies"

Many empirical “anomalies” are actually consistent with the single beta capital asset pricing model if the empiricist utilizes an equity‐only proxy for the true market portfolio. Equity betas estimated against this particular inefficient proxy will be understated, with the error increasing with the firm's leverage. Thus, firm‐specific variables that correlate with leverage (such as book‐to‐market and size) will appear to explain returns after controlling for proxy beta simply because they capture the missing beta risk. Loadings on portfolios formed on relative leverage and relative distress completely subsume the powers of the Fama and French (1993) returns to small minus big market capitalization (SMB) portfolios and returns to high minus low book‐to‐market (HML) portfolios factors in explaining cross‐sectional returns.     

Portfolio selection: An extreme value approach

We show theoretically that lower tail dependence (χ), a measure of the probability that a portfolio will suffer large losses given that the market does, contains important information for risk-averse investors. We then estimate χ for a sample of DJIA stocks and show that it differs systematically from other risk measures including variance, semi-variance, skewness, kurtosis, beta, and coskewness. In out-of-sample tests, portfolios constructed to have low values of χ outperform the market index, the mean return of the stocks in our sample, and portfolios with high values of χ. Our results indicate that χ is conceptually important for risk-averse investors, differs substantially from other risk measures, and provides useful information for portfolio selection.     

Firm size,information acquisition and price efficiency

We present a model in a competitive market where traders choose between a small and a large firm to acquire costly private information, but they also obtain free public information by observing equilibrium share prices. Our major finding is the existence of a noisy rational expectation competitive equilibrium, in which there are more informed traders of the large firm than those of the small firm. As a result, share prices of the large firm are more informative than those of the small firm. Our empirical study supports the analytical results. By using a bivariate vector autoregressive regression, we are able to conduct a variance decomposition of share prices for different size portfolios. We find that prices of large-size portfolios are more informative because non-value-related price shocks are less important in driving price changes of large-size portfolios than in the case of small-size portfolios.     

A dynamic model of active portfolio management with benchmark orientation

This paper studies optimal dynamic portfolios for investors concerned with the performance of their portfolios relative to a benchmark. Assuming that asset returns follow a multi-linear factor model similar to the structure of Ross (1976) [Ross, S., 1976. The arbitrage theory of the capital asset pricing model. Journal of Economic Theory, 13, 342–360] and that portfolio managers adopt a mean tracking error analysis similar to that of Roll (1992) [Roll, R., 1992. A mean/variance analysis of tracking error. Journal of Portfolio Management, 18, 13–22], we develop a dynamic model of active portfolio management maximizing risk adjusted excess return over a selected benchmark. Unlike the case of constant proportional portfolios for standard utility maximization, our optimal portfolio policy is state dependent, being a function of time to investment horizon, the return on the benchmark portfolio, and the return on the investment portfolio. We define a dynamic performance measure which relates portfolio’s return to its risk sensitivity. Abnormal returns at each point in time are quantified as the difference between the realized and the model-fitted returns. Risk sensitivity is estimated through a dynamic matching that minimizes the total fitted error of portfolio returns. For illustration, we analyze eight representative mutual funds in the U.S. market and show how this model can be used in practice.     

Volatility Risk Premium,Risk Aversion,and the Cross‐Section of Stock Returns

We test if innovations in investor risk aversion are a priced factor in the stock market. Using 25 portfolios sorted on book‐to‐market and size as test assets, our new factor together with the market factor explains 64% of the variation in average returns compared to 60% for the Fama‐French model. The new factor is generally significant with an estimated risk premium close to its time series mean also when industry portfolios and portfolios sorted on previous returns are augmented to the test assets.