A trading strategy based on the lead–lag relationship between the spot index and futures contract for the FTSE 100

This paper examines the lead–lag relationship between the FTSE 100 index and index futures price employing a number of time series models. Using 10-min observations from June 1996–1997, it is found that lagged changes in the futures price can help to predict changes in the spot price. The best forecasting model is of the error correction type, allowing for the theoretical difference between spot and futures prices according to the cost of carry relationship. This predictive ability is in turn utilised to derive a trading strategy which is tested under real-world conditions to search for systematic profitable trading opportunities. It is revealed that although the model forecasts produce significantly higher returns than a passive benchmark, the model was unable to outperform the benchmark after allowing for transaction costs.

An alternative approach to investigating lead-lag relationships between stock and stock index futures markets

In the absence of market frictions, the cost-of-carry model of stock index futures pricing predicts that returns on the underlying stock index and the associated stock index futures contract will be perfectly contemporaneously correlated. Evidence suggests, however, that this prediction is violated with clear evidence that the stock index futures market leads the stock market. It is argued that traditional tests, which assume that the underlying data generating process is constant, might be prone to overstate the lead-lag relationship. Using a new test for lead-lag relationships based on cross correlations and cross bicorrelations it is found that, contrary to results from using the traditional methodology, periods where the futures market leads the cash market are few and far between and when any lead-lag relationship is detected, it does not last long. Overall, the results are consistent with the prediction of the standard cost-of-carry model and market efficiency.

Predicting stock index volatility: can market volume help?

This paper explores a number of statistical models for predicting the daily stock return volatility of an aggregate of all stocks traded on the NYSE. An application of linear and non-linear Granger causality tests highlights evidence of bidirectional causality, although the relationship is stronger from volatility to volume than the other way around. The out-of-sample forecasting performance of various linear, GARCH, EGARCH, GJR and neural network models of volatility are evaluated and compared. The models are also augmented by the addition of a measure of lagged volume to form more general ex-ante forecasting models. The results indicate that augmenting models of volatility with measures of lagged volume leads only to very modest improvements, if any, in forecasting performance.

The Corporate Gini Index (CGI) determinants and advantages: lessons from a multinational retail company case study

This article identifies and compares the determinants of CEO compensation to median employee earnings with those of the Corporate Gini Index (CGI). Using a multinational retail company, the article posits that the CGI is an advantageous corporate alternative pay inequality measure that concerns CEO pay multiples to median employee earnings, which regulators should consider using and disclosing in proxy statements. Although CGI and the official measure of multiples of CEO pay to median employee earnings share some of the challenges, the advantages of CGI as an alternative measure are greater. Our findings suggest that the CGI is a much better measure of corporate income inequality bringing clear benefits at both micro and macro levels of intervention.

Evidence for a second meal cognitive effect: glycaemic responses to high and low glycaemic index evening meals are associated with cognition the following morning

Low glycaemic index (GI) foods consumed at breakfast can enhance memory in comparison to high-GI foods; however, the impact of evening meal GI manipulations on cognition the following morning remains unexplored. Fourteen healthy males consumed a high-GI evening meal or a low-GI evening meal in a counterbalanced order on two separate evenings. Memory and attention were assessed before and after a high-GI breakfast the following morning. The high-GI evening meal elicited significantly higher evening glycaemic responses than the low-GI evening meal. Verbal recall was better the morning following the high-GI evening meal compared to after the low-GI evening meal. In summary, the GI of the evening meal was associated with memory performance the next day, suggesting a second meal cognitive effect. The present findings imply that an overnight fast may not be sufficient to control for previous nutritional consumption.

MODIS-based vegetation index has sufficient sensitivity to indicate stand-level intra-seasonal climatic stress in oak and beech forests

Context: Variation in photosynthetic activity of trees induced by climatic stress can be effectively evaluated using remote sensing data. Although adverse effects of climate on temperate forests have been subjected to increased scrutiny, the suitability of remote sensing imagery for identification of drought stress in such forests has not been explored fully. Aim: To evaluate the sensitivity of MODIS-based vegetation index to heat and drought stress in temperate forests, and explore the differences in stress response of oaks and beech. Methods: We identified 8 oak and 13 beech pure and mature stands, each covering between 4 and 13 MODIS pixels. For each pixel, we extracted a time series of MODIS NDVI from 2000 to 2010. We identified all sequences of continuous unseasonal NDVI decline to be used as the response variable indicative of environmental stress. Neural Networks-based regression modelling was then applied to identify the climatic variables that best explain observed NDVI declines. Results: Tested variables explained 84–97% of the variation in NDVI, whilst air temperature-related climate extremes were found to be the most influential. Beech showed a linear response to the most influential climatic predictors, while oak responded in a unimodal pattern suggesting a better coping mechanism. Conclusions: MODIS NDVI has proved sufficiently sensitive as a stand-level indicator of climatic stress acting upon temperate broadleaf forests, leading to its potential use in predicting drought stress from meteorological observations and improving parameterisation of forest stress indices.

Global forecasting of thermal health hazards: the skill of probabilistic predictions of the Universal Thermal Climate Index (UTCI)

Although over a hundred thermal indices can be used for assessing thermal health hazards, many ignore the human heat budget, physiology and clothing. The Universal Thermal Climate Index (UTCI) addresses these shortcomings by using an advanced thermo-physiological model. This paper assesses the potential of using the UTCI for forecasting thermal health hazards. Traditionally, such hazard forecasting has had two further limitations: it has been narrowly focused on a particular region or nation and has relied on the use of single ‘deterministic’ forecasts. Here, the UTCI is computed on a global scale,which is essential for international health-hazard warnings and disaster preparedness, and it is provided as a probabilistic forecast. It is shown that probabilistic UTCI forecasts are superior in skill to deterministic forecasts and that despite global variations, the UTCI forecast is skilful for lead times up to 10 days. The paper also demonstrates the utility of probabilistic UTCI forecasts on the example of the 2010 heat wave in Russia.

Solar causes of the long-term increase in geomagnetic activity

We analyze the causes of the century-long increase in geomagnetic activity, quantified by annual means of the aa index, using observations of interplanetary space, galactic cosmic rays, the ionosphere, and the auroral electrojet, made during the last three solar cycles. The effects of changes in ionospheric conductivity, the Earth's dipole tilt, and magnetic moment are shown to be small; only changes in near-Earth interplanetary space make a significant contribution to the long-term increase in activity. We study the effects of the interplanetary medium by applying dimensional analysis to generate the optimum solar wind-magnetosphere energy coupling function, having an unprecedentedly high correlation coefficient of 0.97. Analysis of the terms of the coupling function shows that the largest contributions to the drift in activity over solar cycles 20-22 originate from rises in the average interplanetary magnetic field (IMF) strength, solar wind concentration, and speed; average IMF orientation has grown somewhat less propitious for causing geomagnetic activity. The combination of these factors explains almost all of the 39% rise in aa observed over the last three solar cycles. Whereas the IMF strength varies approximately in phase with sunspot numbers, neither its orientation nor the solar wind density shows any coherent solar cycle variation. The solar wind speed peaks strongly in the declining phase of even-numbered cycles and can be identified as the chief cause of the phase shift between the sunspot numbers and the aa index. The rise in the IMF magnitude, the largest single contributor to the drift in geomagnetic activity, is shown to be caused by a rise in the solar coronal magnetic field, consistent with a rise in the coronal source field, modeled from photospheric observations, and an observed decay in cosmic ray fluxes.

Auroral bright spot sequence near 1400 MLT: Coordinated optical and ion drift observations

Optical observations of a dayside auroral brightening sequence, by means of all-sky TV cameras and meridian scanning photometers, have been combined with EISCAT ion drift observations within the same invariant latitude-MLT sector. The observations were made during a January 1989 campaign by utilizing the high F region ion densities during the maximum phase of the solar cycle. The characteristic intermittent optical events, covering ∼300 km in east-west extent, move eastward (antisunward) along the poleward boundary of the persistent background aurora at velocities of ∼1.5 km s−1 and are associated with ion flows which swing from eastward to westward, with a subsequent return to eastward, during the interval of a few minutes when there is enhanced auroral emission within the radar field of view. The breakup of discrete auroral forms occurs at the reversal (negative potential) that forms between eastward plasma flow, maximizing near the persistent arc poleward boundary, and strong transient westward flow to the south. The reported events, covering a 35 min interval around 1400 MLT, are embedded within a longer period of similar auroral activity between 0830 (1200 MLT) and 1300 UT (1600 MLT). These observations are discussed in relation to recent models of boundary layer plasma dynamics and the associated magnetosphere-ionosphere coupling. The ionospheric events may correspond to large-scale wave like motions of the low-latitude boundary layer (LLBL)/plasma sheet (PS) boundary. On the basis of this interpretation the observed spot size, speed and repetition period (∼10 min) give a wavelength (the distance between spots) of ∼900 km in the present case. The events can also be explained as ionospheric signatures of newly opened flux tubes associated with reconnection bursts at the magnetopause near 1400 MLT. We also discuss these data in relation to random, patchy reconnection (as has recently been invoked to explain the presence of the sheathlike plasma on closed field lines in the LLBL). In view of the lack of IMF data, and the existing uncertainty on the location of the open-closed field line boundary relative to the optical events, an unambiguous discrimination between the different alternatives is not easily obtained.

The impact of liquidity on senior credit index spreads during the subprime crisis

This paper examines the effects of liquidity during the 2007–09 crisis, focussing on the Senior Tranche of the CDX.NA.IG Index and on Moody's AAA Corporate Bond Index. It aims to understand whether the sharp increase in the credit spreads of these AAA-rated credit indices can be explained by worse credit fundamentals alone or whether it also reflects a lack of depth in the relevant markets, the scarcity of risk-capital, and the liquidity preference exhibited by investors. Using cointegration analysis and error correction models, the paper shows that during the crisis lower market and funding liquidity are important drivers of the increase in the credit spread of the AAA-rated structured product, whilst they are less significant in explaining credit spread changes for a portfolio of unstructured credit instruments. Looking at the experience of the subprime crisis, the study shows that when the conditions under which securitisation can work properly (liquidity, transparency and tradability) suddenly disappear, investors are left highly exposed to systemic risk.

Preliminary leaf area index estimates from airborne small footprint full-waveform LiDAR data

This study has compared preliminary estimates of effective leaf area index (LAI) derived from fish-eye lens photographs to those estimated from airborne full-waveform small-footprint LiDAR data for a forest dataset in Australia. The full-waveform data was decomposed and optimized using a trust-region-reflective algorithm to extract denser point clouds. LAI LiDAR estimates were derived in two ways (1) from the probability of discrete pulses reaching the ground without being intercepted (point method) and (2) from raw waveform canopy height profile processing adapted to small-footprint laser altimetry (waveform method) accounting for reflectance ratio between vegetation and ground. The best results, that matched hemispherical photography estimates, were achieved for the waveform method with a study area-adjusted reflectance ratio of 0.4 (RMSE of 0.15 and 0.03 at plot and site level, respectively). The point method generally overestimated, whereas the waveform method with an arbitrary reflectance ratio of 0.5 underestimated the fish-eye lens LAI estimates.

Bit-index sort: a fast non-comparison integer sorting algorithm for permutations

This paper describes a fast integer sorting algorithm, herein referred as Bit-index sort, which is a non-comparison sorting algorithm for partial per-mutations, with linear complexity order in execution time. Bit-index sort uses a bit-array to classify input sequences of distinct integers, and exploits built-in bit functions in C compilers supported by machine hardware to retrieve the ordered output sequence. Results show that Bit-index sort outperforms in execution time to quicksort and counting sort algorithms. A parallel approach for Bit-index sort using two simultaneous threads is included, which obtains speedups up to 1.6.

The role of index trading in price formation in the grains and oilseeds markets

We use both Granger-causality and instrumental variables (IV) methods to examine the impact of index fund positions on price returns for the main US grains and oilseed futures markets. Our analysis supports earlier conclusions that Granger-causal impacts are generally not discernible. However, market microstructure theory suggests trading impacts should be instantaneous. IV-based tests for contemporaneous causality provide stronger evidence of price impact. We find even stronger evidence that changes in index positions can help predict future changes in aggregate commodity price indices. This result suggests that changes in index investment are in part driven by information which predicts commodity price changes over the coming months.

Fast adaptive real-time classification for data streams with concept drift

An important application of Big Data Analytics is the real-time analysis of streaming data. Streaming data imposes unique challenges to data mining algorithms, such as concept drifts, the need to analyse the data on the fly due to unbounded data streams and scalable algorithms due to potentially high throughput of data. Real-time classification algorithms that are adaptive to concept drifts and fast exist, however, most approaches are not naturally parallel and are thus limited in their scalability. This paper presents work on the Micro-Cluster Nearest Neighbour (MC-NN) classifier. MC-NN is based on an adaptive statistical data summary based on Micro-Clusters. MC-NN is very fast and adaptive to concept drift whilst maintaining the parallel properties of the base KNN classifier. Also MC-NN is competitive compared with existing data stream classifiers in terms of accuracy and speed.