Portfolio Of Large Stocks Research Articles

Liquidation discount—a novel application of ARFIMA–GARCH

Urgent liquidation of a large stock portfolio entails a liquidity cost—i.e., a “liquidation discount”. This is the market impact discount in value yielded by the immediate sale of the portfolio relative to its in-hand market value calculated from prevailing market prices. For any portfolio, the day-to-day liquidation discount “at risk” (i.e., the liquidation discount that would be suffered if liquidation were undertaken) is variable. This liquidation discount risk is additional to price risk and will be of concern to portfolio managers that may, at short notice, wish to convert substantial stockholdings to cash. We obtain daily time series of instantaneous log liquidation discount for variously sized portfolios of Australian stocks and determine that these time series are best modeled with an Autoregressive Fractionally Integrated Moving Average-Generalized Autoregressive Conditional Heteroskedasticity (ARFIMA–GARCH) process. We then formulate a liquidation discount-at-risk measure with which portfolio managers can budget for the future cost of portfolio liquidity for a chosen liquidation horizon and confidence level.

Optimal Portfolio Allocation Strategies with Dynamic Factor Models

We present a framework for designing optimal allocation strategies for large stock portfolios using dynamic factor models and multivariate volatility parametrisations. We attempt to elaborate on the fundamental structure of the Fama and French (FF) factor model with a special focus on the time variation in risk and correlation between stocks returns and systematic factors. For this reason, variants of the multivariate GARCH models are employed to capture the dynamics in means, variances and covariances of the FF factors structure. Based on these models, we derive optimal capital allocation strategies in the framework of Markowitz’s mean-variance portfolio theory. We outline and compare the out-of-sample performance of these mean-variance allocations with those obtained using simpler techniques, such as sample historical and Exponentially Weighted Moving Average (EWMA) estimates.

Information Immobility and Foreign Portfolio Investment

We examine how residents of the United States allocate their stock portfolios internationally. We find that a large U.S. Foreign Direct Investment (FDI) position in a destination country in 1990 is associated with a relatively large stock portfolio position in that country in the 2001–2006 period. Moreover, a change in the U.S. FDI position from 1980 to 1990 helps predict the change in the U.S. Foreign Portfolio Investment position from 1994 to 2006. These results are rationalized by Van Nieuwerburgh and Veldkamp’s (2009) equilibrium model of learning and portfolio choice under an information processing constraint. FDI establishes marginal differences in the endowments of information about different countries, which later translate into differences in stock portfolio holdings. We control for cross-country differences in capital controls, proximity along different dimensions, corporate governance, and economic and capital market development. Our results also hold for the G6 countries collectively.

Optimal portfolio allocation strategies with dynamic factor models

We present a framework for designing optimal allocation strategies for large stock portfolios using dynamic factor models and multivariate volatility parametrisations. We attempt to elaborate on the fundamental structure of the Fama and French (FF) factor model with a special focus on the time variation in risk and correlation between stocks returns and systematic factors. For this reason, variants of the multivariate GARCH models are employed to capture the dynamics in means, variances and covariances of the FF factors structure. Based on these models, we derive optimal capital allocation strategies in the framework of Markowitz's mean-variance portfolio theory. We outline and compare the out-of-sample performance of these mean-variance allocations with those obtained using simpler techniques, such as sample historical and exponentially weighted moving average (EWMA) estimates.

Information transmission between small and large stocks in the National Stock Exchange in India: An empirical study

Abstract The study investigates return and volatility spillover effects between large and small stocks in the national stock exchange in India using daily index data on S&P CNX Nifty, CNX Nifty Junior and CNX Midcap. The VAR model together with the variance decomposition (VDC) and the impulse response function (IRF) analysis have been employed to uncover both casual and dynamic relationship between the large stocks and small stocks. The results show that there are very significant return spillovers from the market portfolio of large stocks to the portfolio of small stocks. To investigate the volatility spillover the study has used standard BEKK model and asymmetric BEKK model. Although, based on the standard BEKK model we have observed unidirectional volatility spillovers from the portfolio of large stocks to the portfolio of small stocks, the finding was less reliable. The more reliable finding, which is based on asymmetric BEKK model, is that there is bidirectional volatility spillover between the portfolio of large stocks and the portfolio of small stocks.

Yield Spreads as Alternative Risk Factors for Size and Book-to-Market

AbstractThis paper investigates whether the size and book-to-market factors of Fama and French (1993) proxy for the risks associated with business cycle fluctuations. We find that changes in default spread (Δdef) and changes in term spread (Δterm) capture the systematic differences in average returns along the size and book-to-market dimensions in the way that the Fama-French factors do: small stock portfolios have higher loadings on Δdef than large stock portfolios, while high book-to-market portfolios have higher loadings on Δterm than low book-to-market portfolios. Furthermore, in the presence of Δdef and Δterm, the Fama-French factors are superfluous in explaining the size and book-to-market effects. The results suggest that the size and value premiums are compensation for higher exposure to the risks related to changing credit market conditions and interest rates proxied by Δdef and Δterm.

The Persistence and Predictability of Closed-End Fund Discounts

It is well-known that the level of closed-end fund discounts appears to predict the corresponding fund's future returns. We further document that such predictability decays slowly. The popular explanations, including the tax effect, investor sentiment risk, and the funds's dividend yield, do not fully account for the observed predictability. At the same time, discounts are very persistent especially on an aggregate level. Using an AR(1) model for discounts, we demonstrate that such predictability is largely due to persistence in discounts. Our calibration exercise can produce most characteristics of an aggregate equity close-end fund index over the ten year period from 1993 to 2001. A cross-sectional study links discount persistence to rational factors such as dividend yield, unrealized capital gains, and turnover. In addition, we document a second independent source for predicting fund returns from large stock portfolio returns. This suggests that the well-known lead lag relationship between large stocks and small stocks also exists between NAV returns and fund returns. Finally, we find no evidence for excess volatility on the aggregate level both for conditional and unconditional volatility.

THE CROSS‐AUTOCORRELATION OF SIZE‐BASED PORTFOLIO RETURNS IN EUROPE

This study examines the cross‐autocorrelation of size‐based portfolio returns in a sample of 15 major European markets using daily data from January 1990 through December 1999. Previous studies have primarily used U.S. data. This study extends previous research by considering results in multiple European exchanges. We examine whether a difference in size‐based portfolios exists by testing cross‐autocorrelation, granger‐causality, and asymmetric responses in the European markets. The results confirm that large stock portfolio returns lead small stock portfolio returns in most European countries, and that cross‐autocorrelation is present both within and between European financial markets.

Cross-autocorrelation in Asian stock markets

We evaluate six Asian stock markets (Hong Kong, Japan, Singapore, South Korea, Taiwan, and Thailand) and the US stock market for evidence of cross-autocorrelation. We find some evidence of Lo and MacKinlay's [Lo, A., MacKinlay, C., 1990a. When are contrarian profits due to stock market overreaction? Review of Financial Studies 3, 175-205] cross-autocorrelation in all six of the Asian markets. Specifically, within each country, monthly returns on a portfolio of small stocks are correlated with the lagged returns on a portfolio of large stocks. Cross-autocorrelation is strongest in the US and weakest in Singapore and Taiwan, where the lagged response is evident only after conditioning on the direction (up or down) of the market movement. After documenting the international evidence of cross-autocorrelation, we report the results of five additional tests designed to shed light on existing explanations of the cross-autocorrelation puzzle and give direction for further research. First, we show that cross-autocorrelation exists after controlling for non-synchronous trading. Second, we do not find much evidence of across-ocean or inter-Asian cross-autocorrelation; monthly returns on portfolios of Asian stocks are generally not correlated with the lagged return on a portfolio of large US stocks or with large stocks in other Asian countries. Third, we confirm McQueen et al.'s [McQueen, G., Pinegar, J.M., Thorley, S., 1996. Delayed reaction to good news and the cross-autocorrelation of portfolio returns. Journal of Finance 51, 839-920] finding of directional asymmetry in the US, with small stocks responding to lagged good, but not bad, news. However, the directional asymmetry is not universal and is significant only in the US and Taiwan. Fourth, we show that large stocks retain their predictive power in some, but not all, of the markets after correcting for small stock autocorrelation. Fifth, we find that the degree of cross-autocorrelation in monthly returns has not weakened since the market correction of 1987.

Firm size and the Italian Stock Exchange

The presence of a relation between firm size and asset returns is investigated by referring to the Italian Stock Exchange. In order to explain asset return variability, the excess return on a market portfolio as well as the difference between the return on a portfolio of small stocks and the return on a portfolio of large stocks are considered. The resultant two-factor model seems to improve the explanation of the returns of the portfolios formed on size.

Cross-Autocorrelation in Asian Stock Markets

Five Asian stock markets (Hong Kong, Japan, South Korea, Taiwan, and Thailand) and the U.S. stock market are evaluated for evidence of cross-autocorrelation. We find evidence of Lo and MacKinlay's (1990a) cross-autocorrelation in each of the five Asian markets. Specifically, within each country, monthly returns on a portfolio of small stocks are correlated with the lagged returns on a portfolio of large stocks. We also find weak evidence of across-ocean and inter-Asian cross-autocorrelation; in several countries, monthly returns on portfolios of small Asian stocks are correlated with the lagged return on a portfolio of large U.S. stocks and large stocks in other Asian countries. We also confirm McQueen, Pinegar, and Thorley's (1996) finding of directional asymmetry in the U.S., with small stocks responding to lagged good, but not bad, news. However, the directional asymmetry is not universal and is significant only in the U.S. and Taiwan. Similarly, after correcting for small stock autocorrelation, large stocks retain their predictive power in some, but not all, of the markets. We also find that the degree of cross-autocorrelation has not weakened since the market correction of 1987. Our findings help shed light on existing explanations of the cross-autocorrelation puzzle and give direction for further research.

Further Evidence on Short-Run Results for New Issue Investors

In a recent article, Professors Stoll and Curley (hereinafter referred to as S–C) examined results for new issues during a short-run period and over long-run periods. The authors concluded that, “investors in new small issues floated under Regulation A in 1957, 1959, and 1963 experienced lower long-run rates of return than if they had invested in a portfolio of large stocks represented by the Standard and Poor's Industrial Average.” It was pointed out that these long-run results were consistent with the results of similar studies. Alternatively, regarding short-run results it was concluded that “ in the short run , the stocks in the sample showed a remarkable price appreciation.” In fact, “short-run price appreciation was, however, considerably greater than the index appreciation.” They refer to this short-run performance as “.… perhaps the most interesting and certainly the most puzzling phenomenon encountered in the study.”