Serial correlation in the returns of UK capitalization based portfolios

This article examines whether UK portfolio returns are time varying so that expected returns follow an AR(1) process as proposed by Conrad and Kaul for the USA. It explores this hypothesis for four portfolios that have been formed on the basis of market capitalization. The portfolio returns are modelled using a kalman filter signal extraction model in which the unobservable expected return is the state variable and is allowed to evolve as a stationary first order autoregressive process. It finds that this model is a good representation of returns and can account for most of the autocorrelation present in observed portfolio returns. This study concludes that UK portfolio returns are time varying and the nature of the time variation appears to introduce a substantial amount of autocorrelation to portfolio returns. Like Conrad and Kaul if finds a link between the extent to which portfolio returns are time varying and the size of firms within a portfolio but not the monotonic one found for the USA.

Explaining volatility and serial correlation in opening and closing returns:a study of the FT-30 components

In this paper the performance of opening and closing returns, for the components of the FT-30 will be studied. It will be shown that for these stocks opening returns have higher volatility and a greater tendency towards negative serial correlation than closing returns. Unlike previous studies this contrasting performance cannot solely be attributed to differences in the trading mechanism across the trading day. All the stocks used in our sample trade thought the day using a uniform trading mechanism. In this paper, we suggest that it is differences in the speed that closing and opening returns adjust to new information that causes differences in return performance. By estimating the Amihud and Mendelson (1987) [Amihud, Yakov, & Mendelson, Haim (1987). Trading mechanisms and stock returns: An empirical investigation, Journal of Finance, 62 533-553.] partial adjustment model with noise, we show that opening returns have a tendency towards over-reaction, while closing returns have a tendency towards under-reaction. We suggest that it is these differences that cause a substantial proportion (although not all) of the asymmetric return patterns associated with opening and closing returns. © 2005 Elsevier Inc. All rights reserved.

Serial correlation in the returns of UK capitalization based portfolios

This article examines whether UK portfolio returns are time varying so that expected returns follow an AR(1) process as proposed by Conrad and Kaul for the USA. It explores this hypothesis for four portfolios that have been formed on the basis of market capitalization. The portfolio returns are modelled using a kalman filter signal extraction model in which the unobservable expected return is the state variable and is allowed to evolve as a stationary first order autoregressive process. It finds that this model is a good representation of returns and can account for most of the autocorrelation present in observed portfolio returns. This study concludes that UK portfolio returns are time varying and the nature of the time variation appears to introduce a substantial amount of autocorrelation to portfolio returns. Like Conrad and Kaul if finds a link between the extent to which portfolio returns are time varying and the size of firms within a portfolio but not the monotonic one found for the USA. © 2004 Taylor and Francis Ltd.

Opening returns, noise and overreaction

In this article a partial-adjustment model, which shows how equity prices fail to adjust instantaneously to new information, is estimated using a Kalman filter. For the components of the Dow Jones Industrial 30 index I aim to identify whether overreaction or noise is the cause of serial correlation and high volatility associated with opening returns. I find that the tendency for overreaction in opening prices is much stronger than for closing prices; therefore, overreaction rather than noise may account for differences in the return behavior of opening and closing returns.

Conditional volatility and firm size:an empirical analysis of UK equity portfolios

The techniques and insights from two distinct areas of financial economic modelling are combined to provide evidence of the influence of firm size on the volatility of stock portfolio returns. Portfolio returns are characterized by positive serial correlation induced by the varying levels of non-synchronous trading among the component stocks. This serial correlation is greatest for portfolios of small firms. The conditional volatility of stock returns has been shown to be well represented by the GARCH family of statistical processes. Using a GARCH model of the variance of capitalization-based portfolio returns, conditioned on the autocorrelation structure in the conditional mean, striking differences related to firm size are uncovered.

Data analysis methods in optometry. Part 5: correlation

1. Pearson's correlation coefficient only tests whether the data fit a linear model. With large numbers of observations, quite small values of r become significant and the X variable may only account for a minute proportion of the variance in Y. Hence, the value of r squared should always be calculated and included in a discussion of the significance of r. 2. The use of r assumes that a bivariate normal distribution is present and this assumption should be examined prior to the study. If Pearson's r is not appropriate, then a non-parametric correlation coefficient such as Spearman's rs may be used. 3. A significant correlation should not be interpreted as indicating causation especially in observational studies in which there is a high probability that the two variables are correlated because of their mutual correlations with other variables. 4. In studies of measurement error, there are problems in using r as a test of reliability and the ‘intra-class correlation coefficient’ should be used as an alternative. A correlation test provides only limited information as to the relationship between two variables. Fitting a regression line to the data using the method known as ‘least square’ provides much more information and the methods of regression and their application in optometry will be discussed in the next article.

Reduced attentional capacity, but normal processing speed and shifting of attention in developmental dyslexia: evidence from a serial task

We report the performance of a group of adult dyslexics and matched controls in an array-matching task where two strings of either consonants or symbols are presented side by side and have to be judged to be the same or different. The arrays may differ either in the order or identity of two adjacent characters. This task does not require naming – which has been argued to be the cause of dyslexics’ difficulty in processing visual arrays – but, instead, has a strong serial component as demonstrated by the fact that, in both groups, Reaction times (RTs) increase monotonically with position of a mismatch. The dyslexics are clearly impaired in all conditions and performance in the identity conditions predicts performance across orthographic tasks even after age, performance IQ and phonology are partialled out. Moreover, the shapes of serial position curves are revealing of the underlying impairment. In the dyslexics, RTs increase with position at the same rate as in the controls (lines are parallel) ruling out reduced processing speed or difficulties in shifting attention. Instead, error rates show a catastrophic increase for positions which are either searched later or more subject to interference. These results are consistent with a reduction in the attentional capacity needed in a serial task to bind together identity and positional information. This capacity is best seen as a reduction in the number of spotlights into which attention can be split to process information at different locations rather than as a more generic reduction of resources which would also affect processing the details of single objects.

Is pupil diameter influenced by refractive error?

Purpose: To investigate the relationship between pupil diameter and refractive error and how refractive correction, target luminance, and accommodation modulate this relationship. Methods: Sixty emmetropic, myopic, and hyperopic subjects (age range, 18 to 35 years) viewed an illuminated target (luminance: 10, 100, 200, 400, 1000, 2000, and 4100 cd/m2) within a Badal optical system, at 0 diopters (D) and −3 D vergence, with and without refractive correction. Refractive error was corrected using daily disposable contact lenses. Pupil diameter and accommodation were recorded continuously using a commercially available photorefractor. Results: No significant difference in pupil diameter was found between the refractive groups at 0 D or −3 D target vergence, in the corrected or uncorrected conditions. As expected, pupil diameter decreased with increasing luminance. Target vergence had no significant influence on pupil diameter. In the corrected condition, at 0 D target vergence, the accommodation response was similar in all refractive groups. At −3 D target vergence, the emmetropic and myopic groups accommodated significantly more than the hyperopic group at all luminance levels. There was no correlation between accommodation response and pupil diameter or refractive error in any refractive group. In the uncorrected condition, the accommodation response was significantly greater in the hyperopic group than in the myopic group at all luminance levels, particularly for near viewing. In the hyperopic group, the accommodation response was significantly correlated with refractive error but not pupil diameter. In the myopic group, accommodation response level was not correlated with refractive error or pupil diameter. Conclusions: Refractive error has no influence on pupil diameter, irrespective of refractive correction or accommodative demand. This suggests that the pupil is controlled by the pupillary light reflex and is not driven by retinal blur.

Intraoperative prediction of refractive error using a prototype surgical aberrometer

Purpose: To ascertain the agreement level between intra-operative refraction using a prototype surgical Hartmann-Shack aberrometer and subjective refraction a month later. Methods: Fifty-four consecutive patients had their pseudophakic refractive measured with the aberrometer intra-operatively at the end of their cataract surgery. A masked optometrist performed subjective refraction 4 weeks later. The two sets of data were then analysed for correlation. Results: The mean spherical equivalent was −0.14 ± 0.37 D (Range: −1.41 to +1.72 D) with the prototype aberrometer and −0.34 ± 0.32 (−1.64 to +1.88 D) with subjective refraction. The measurements positively correlated to a very high degree (r =+0.81, p < 0.01). In 84.3% of cases the two measurements were within 0.50D of each other. Conclusion: The aberrometer can verify the aimed refractive status of the eye intraoperatively to avoid a refractive surprise. The aberrometer is a useful tool for real time assessment of the ocular refractive status.