The Empirical Distribution of UK and US Stock Returns

There is now substantial evidence that daily equity returns are not normally distributed but instead display significant leptokurtosis and, in many cases, skewness. Considerable effort has been made in order to capture these empirical characteristics using a range of ad hoc statistical distributions. In this paper, we investigate the distribution of daily, weekly and monthly equity returns in the UK and US using two very flexible families of distributions that have been recently introduced: the exponential generalised beta (EGB) and the skewed generalised- t (SGT). These distributions permit very diverse levels of skewness and kurtosis and, between them, nest many of the distributions previously considered in the literature. Both the EGB and the SGT provide a very substantial improvement over the normal distribution in both markets. Moreover, for daily returns, we strongly reject the restrictions on the EGB and SGT implied by most of the distributions that are commonly used for modelling equity returns, including the student- t , the power exponential and the logistic distributions. Instead, our preferred distributions for daily returns are the generalised- t for the US and the skewed- t for the UK, both of which are members of the SGT family. For weekly returns, our preferred distributions are the student- t for the UK and the skewed- t for the US, while for monthly returns, our preferred distributions are the EBR12 for the UK and the logistic for the US. We consider the implications of our findings for the implementation of value-at-risk, a risk management methodology that rests heavily on the distributional characteristics of returns.     

Integration of emerging stock markets with global stock markets

Financial integration for emerging economies should be seen as a long-term objective. In this paper, we examine stock market integration among five selected emerging stock markets (Brazil, China, Mexico, Russia and Turkey) and developed markets of the US, UK and Germany. The bounds testing approach to cointegration and error-correction modeling are used on monthly data from January 2001 to December 2014 to determine the short-run and long-run relationship between emerging stock market returns and the returns of the developed stock markets. The results show evidence of the existence of short-run integration among stock markets in emerging countries and the developed markets. However, the long-run coefficients for stock market returns in all emerging countries show a significant relationship only with Germany stock market return. The empirical findings in this study have important implications for academicians, international investors, and policymakers in emerging markets.     

Managerial sentiment,consumer confidence and sector returns

This paper investigates the relationship between managerial sentiment and sector returns. Using UK monthly data from January 1985 to December 2014 and a sample of consumer and business confidence indicators provided by the European Commission, we provide novel evidence on how managerial and consumer sentiment indicators affect stock returns. We find no support for consumer confidence as a predictor of stock returns. However, managerial sentiment shows a significant impact on aggregate market and sector return indices. Furthermore, we find that parameter estimates for sector groupings are not consistent, implying that the sentiment-return relationship differs across sectors. We also find parameters are sensitive to industry characteristics. Importantly, the overall sentiment-return relationship is dominated by sentiment associated with manufacturing firms.     

Portfolio Returns,Market Volatility,and Seasonality

This paper examines three important issues related to the relationship between stock returns and volatility. First, are Duffee's (1995) findings of the relationship between individual stock returns and volatility valid at the portfolio level? Second, is there a seasonality of the market return volatility? Lastly, do size portfolio returns react symmetrically to the market volatility during business cycles? We find that the market volatility exhibits strong autocorrelation and small size portfolio returns exhibit seasonality. However, this phenomenon is not present in large size portfolios. For the entire sample period of 1962–1995, the highest average monthly volatility occurred in October, followed by November, and then January. Examining the two sub-sample periods, we find that the average market volatility increases by 15.4% in the second sample period of 1980–1995 compared to the first sample period of 1962–1979. During the contraction period, the average market volatility is 60.9% higher than that during the expansion period. Using a binary regression model, we find that size portfolio returns react asymmetrically with the market volatility during business cycles. This paper documents a strongly negative contemporaneous relationship between the size portfolio returns and the market volatility that is consistent with the previous findings at the aggregate level, but is inconsistent with the findings at the individual firm level. In contrast with the previous findings, however, we find an ambiguous relationship between the percentage change in the market volatility and the contemporaneous stock portfolio returns. This ambiguity is attributed to strongly negative contemporaneous and one-month ahead relationships between the market volatility and portfolio returns.

     

The Gilt-Equity Yield Ratio and the Predictability of UK and US Equity Returns

A number of financial variables have been shown to be effective in explaining the time-series of aggregate equity returns in both the UK and the US. These include, inter alia , the equity dividend yield, the spread between the yields on long and short government bonds, and the lagged equity return. Recently, however, the ratio between the long government bond yield and the equity dividend yield – the gilt-equity yield ratio – has emerged as a variable that has considerable explanatory power for UK equity returns. This paper compares the predictive ability of the gilt-equity yield ratio with these other variables for UK and US equity returns, providing evidence on both in-sample and out-of-sample performance. For UK monthly returns, it is shown that while the dividend yield has substantial in-sample explanatory power, this is not matched by out-of sample forecast accuracy. The gilt-equity yield ratio, in contrast, performs well both in-sample and out-of-sample. Although the predictability of US monthly equity returns is much lower than for the UK, a similar result emerges, with the gilt-equity yield ratio dominating the other variables in terms of both in-sample explanatory power and out-of-sample forecast performance. The gilt-equity yield ratio is also shown to have substantial predictive ability for long horizon returns.     

The Gilt-Equity Yield Ratio and the Predictability of UK and US Equity Returns

A number of financial variables have been shown to be effective in explaining the time-series of aggregate equity returns in both the UK and the US. These include, inter alia , the equity dividend yield, the spread between the yields on long and short government bonds, and the lagged equity return. Recently, however, the ratio between the long government bond yield and the equity dividend yield – the gilt-equity yield ratio – has emerged as a variable that has considerable explanatory power for UK equity returns. This paper compares the predictive ability of the gilt-equity yield ratio with these other variables for UK and US equity returns, providing evidence on both in-sample and out-of-sample performance. For UK monthly returns, it is shown that while the dividend yield has substantial in-sample explanatory power, this is not matched by out-of sample forecast accuracy. The gilt-equity yield ratio, in contrast, performs well both in-sample and out-of-sample. Although the predictability of US monthly equity returns is much lower than for the UK, a similar result emerges, with the gilt-equity yield ratio dominating the other variables in terms of both in-sample explanatory power and out-of-sample forecast performance. The gilt-equity yield ratio is also shown to have substantial predictive ability for long horizon returns.     

Empirical Analysis of Stock Returns and Volatility: Evidence from Seven Asian Stock Markets Based on TAR-GARCH Model

This paper investigates the time-series behavior of stock returns for seven Asian stock markets. In most cases, higher average returns appear to be associated with a higher level of volatility. Testing the relationship between stock returns and unexpected volatility, the evidence shows that four out of seven Asian stock markets have significant results. Further analyzing the relationship between stock returns and time-varying volatility by using Threshold Autoregressive GARCH(1,1)-in-mean specification indicates that the null hypothesis of no asymmetric effect on the conditional volatility is rejected for the daily data. However, the null cannot be rejected for the monthly data.     

Fluctuations in the UK equity market: what drives stock returns?

Present value parameters from a state-space model are estimated for the UK FT All-Share Index. The estimated parameters are used to construct a time series of expected future returns and expected future values of dividend growth, both of which are found to be time-varying with persistent components. Variations in the price-dividend ratio appear to be driven primarily by the variance in expected returns. A comparison with the findings from a present value-constrained vector autoregression model indicates that the latter forecasts future realized returns and dividend growth better than the series constructed using a state-space approach. Furthermore, when the model is estimated for monthly and quarterly data, expected dividend growth is found to be more persistent.     

Momentum strategies and stock returns: Chinese evidence

This paper investigates the profitability of momentum investment strategies for equities listed in the Shanghai Stock Exchange. We also investigate the role of trading volume to examine whether there is any relationship between stock returns and past trading volume for Chinese equities. We find evidence of substantial momentum profits during the period 1995 to 2005 and that momentum is a pervasive feature of stock returns for the market investigated in this paper.Our findings suggest that investors can generate superior returns by investing in strategies unrelated to market movements. We also investigate the potential of past volume to explain momentum profits, and find no strong link between past volume and momentum profits. Our findings also show a strong momentum effect around earnings announcements but the magnitude of these returns is small in relation to the average monthly returns earned in the early months following portfolio formation.     

Real estate returns,money and fiscal deficits: Is the real estate market efficient?

This research examines the causal relationship between several financial variables and a portfolio of real estate returns using monthly data from January 1965 to December 1986. The empirical analysis is based on multivariate Granger-causality tests in conjunction with Akaike's final prediction error criterion. The results indicate that measures approximating monetary policy and market returns play an important role in causing changes in real estate returns. In particular, our findings suggest that base money and market returns have had significant lagged effects on current real estate returns.     

Mean Reversion in the Short Horizon Returns of UK Portfolios

This paper will show that short horizon stock returns for UK portfolios are more predictable than suggested by sample autocorrelation co-efficients. Four capitalisation based portfolios are constructed for the period 1976–1991. It is shown that the first order autocorrelation coefficient of monthly returns can explain no more than 10% of the variation in monthly portfolio returns. Monthly autocorrelation coefficients assume that each weekly return of the previous month contains the same amount of information. However, this will not be the case if short horizon returns contain predictable components which dissipate rapidly. In this case, the return of the most recent week would say a lot more about the future monthly portfolio return than other weeks. This suggests that when predicting future monthly portfolio returns more weight should be given to the most recent weeks of the previous month, because, the most recent weekly returns provide the most information about the subsequent months' performance. We construct a model which exploits the mean reverting characteristics of monthly portfolio returns. Using this model we forecast future monthly portfolio returns. When compared to forecasts that utilise the autocorrelation statistic the model which exploits the mean reverting characteristics of monthlyportfolio returns can forecast future returns better than the autocorrelation statistic, both in and out of sample.     

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.     

ROBUSTNESS OF THE HEADQUARTERS‐CITY EFFECT ON STOCK RETURNS

Recent studies report that U.S. firms headquartered near each other experience positive comovement in their stock returns, a finding suggestive of local biases in equity trading activity. We investigate the robustness of these findings and find that including additional pricing factors in models for monthly stock returns materially reduces the magnitude of the headquarters‐city effect in stock returns. Additionally, we find that an implicit null hypothesis of zero local return comovement is inappropriate as there is positive comovement between a stock's return and returns on portfolios of stocks from nonheadquarters cities, on average. Nevertheless, results benchmarked against estimates based on resampling methods indicate a significant and robust headquarters‐city effect in stock returns.     

The empirical relationship between risk and return: evidence from the UK stock market

Previous studies reach no consensus on the relationship between risk and return using data from one market. We argue that the world market factor should not be ignored in assessing the risk-return relationship in a partially integrated market. Applying a bivariate generalized autoregressive conditional heteroscedasticity in mean (GARCH-M) model to the weekly stock index returns from the UK and the world market, we document a significant positive relationship between stock returns and the variance of returns in the UK stock market after controlling for the covariance of the UK and the world market return. In contrast, conventional univariate GARCH-M models typically fail to detect this relationship. Nonnested hypothesis tests supplemented with other commonly used model selection criteria unambiguously demonstrate that our bivariate GARCH-M model is more likely to be the true model for UK stock market returns than univariate GARCH-M models. Our results have implications for empirical assessments of the risk-return relationship, expected return estimation, and international diversification.     

A Dividend Payment Effect in Stock Returns

This paper presents evidence for the period 7/62-12/89 that individual NYSE and AMEX stocks provide relatively high average excess returns on the payment dates of quarterly cash dividends and several subsequent trading days. Additional results indicate that returns during the payment period: (a) are not a manifestation of the January, monthly or dividend yield anomalies; (b) are positively related to the stock's dividend yield; and (c) are higher for firms that have dividend reinvestment plans. These findings are consistent with a tendency by stock-holders to reinvest dividend income into the stock of the paying firm, thereby increasing demand for the stock and raising its price. Additional evidence links the returns on these days with (previously-documented) excess returns around the ex-dividend date.     

Long-Term Market Overreaction: The Effect of Low-Priced Stocks

Conrad and Kaul (1993) report that most of De Bondt and Thaler's (1985) long-term overreaction findings can be attributed to a combination of bid-ask effects when monthly cumulative average returns (CARs) are used, and price, rather than prior returns. In direct tests, we find little difference in test-period returns whether CARs or buy-and-hold returns are used, and that price has little predictive ability in cross-sectional regressions. The difference in findings between this study and Conrad and Kaul's is primarily due to their statistical methodology. They confound cross-sectional patterns and aggregate time-series mean reversion, and introduce a survivor bias. Their procedures increase the influence of price at the expense of prior returns.     

Dynamics of Asset Prices and Transaction Activity in Illiquid Markets: the Case of Private Commercial Real Estate

This paper investigates the relationship between capital flows, turnover and returns for the UK private real estate market. We examine a number of possible implications of capital flows and turnover on capital returns testing for evidence of a price pressure effect, ‘return chasing’ behaviour and information revelation. The main tool of analysis is a panel vector autoregressive (VAR) regression model in which institutional capital flows, turnover and returns are specified as endogenous variables in a two equation system in which we also control for macro-economic variables. Data on flows, turnover and returns are obtained for the ten market segments covering the main UK commercial real estate sectors. Our results do not support the widely-held belief among practitioners that capital flows have a ‘price pressure’ effect on property prices. However, we do find a significant positive relationship between lagged turnover and contemporaneous capital returns, suggesting that asset turnover provides increased price revelation which, in turn, reduces investment risk and increases property values.     

Stock returns,inflation and interest rates in the United Kingdom

The Fisherian theory of interest asserts that a fully perceived change in inflation would be reflected in nominal interest rates and stock returns in the same direction in the long run. This paper examines the Fisherian hypothesis of asset returns using alternative techniques of linear regression, and vector error correction models to examine the nature of the relationship between stock returns and inflation in the UK. Consistent with the Fisherian hypothesis, empirical evidence in the linear regression model suggests a positive and statistically significant relationship between stock returns and inflation, which regards common stock as a good hedge against inflation. The results based on the unit root and cointegration tests indicate a long-run reliable relationship between price levels, share prices, and interest rates which could be interpreted as the long-run determinants of stock returns. The findings also suggest a bidirectional relationship between stock returns and inflation. The evidence of a significant Fisher effect is robust across model specifications.     

On the Predictability of Japanese Stock Returns Using Dividend Yield

The aim of this paper is to provide a critical and comprehensive reexamination of empirical evidence on the ability of the dividend yield to predict Japanese stock returns. Our empirical results suggest that in general, the predictability is weak. However, (1) if the bubble economy period (1986–1998), during which dividend yields were persistently lower than the historical average, is excluded from the sample, and (2) if positive autocorrelation in monthly aggregate returns is taken into account, there is some evidence that the log dividend yield is indeed useful in forecasting future stock returns. More specifically, the log dividend yield contributes to predicting monthly stock returns in the sample after 1990 and when lagged stock returns are included simultaneously.     

Non-linear dynamics of hot and cold cycles in Indian IPO markets: evidence from Markov regime-switching vector autoregressive model

The study aims to examine non-linear relationship between initial public offering (IPO) volume and average monthly initial returns for ‘hot’ and ‘cold’ issuing cycles in the Indian IPO markets using a two-state Markov regime-switching vector autoregressive model. The sample considers 557 IPOs during the period 2004–2014. The study establishes the presence of hot and cold states in Indian IPO markets. It finds bidirectional causality between IPO volume and initial returns for ‘hot’ issuing periods. The empirical findings suggest that the market possesses valuable information content in terms of the past issuing activity which has the potential to increase the predictability of future market behaviour.