Intraday Level Research Articles

ASSESSMENT OF IN VITRO NARINGENIN RELEASE FROM SOLID LIPID NANOPARTICLES AND KINETIC MODEL PROFILING: APPLIED ULTRAVIOLET-VISIBLE SPECTROPHOTOMETER

A new, simple, specific, rapid, precise, highly accurate, reproducible and cost effective Ultraviolet-Visible spectrophotometric method was developed and validated, according to the International Harmonization Guidelines, for the determination of naringenin from solid lipid nanoparticles. Absorption maximum of Naringenin was found to be at 287.49nm in methanol. The linearity range was found to be 5-25μg/mL with high correlation coefficient value of 0.999. The detection and quantification limits were found to be 0.1879μg/mL and 0.5694μg/mL, respectively. This method was shown to be specific, selective, precise at the intra-day (relative standard deviation less than 0.7046%) and inter-day (relative standard deviation less than 1.5424%) level and accurate with recoveries between 98.77-100.43% (relative standard deviation less than 0.3924%). Method robustness observation indicates that method was robust. The suitability of the method for naringenin quantifications was assessed by the determination of entrapment parameters and by studying the naringenin release profile from SLNs. High entrapment efficiency (91.922 ± 0.717%) and drug loading (3.506 ± 0.027%) were observed. Kinetic models (zero order, first order, Higuchi, Hixson-Crowell, Korsmeyer-Peppas and Baker-Lonsdale) were used to fit the obtained release profile and to predict the in vivo performance of naringenin-loaded SLNs. An anomalous non-Fickian transport was found, which indicate a controlled drug release system.

Intraday herding on a cross-border exchange

This study investigates intraday herding on the Euronext, the world's first cross-border consolidated exchange. Intraday herding is significant in the Euronext as a group and presents us with size, industry and country effects. Importantly, the trading dynamics of the group's member markets significantly affect each other and can, in the case of the Netherlands, promote herding formation. Intraday herding is found to be significant before, during and after the 2007–09 financial crisis period, with its presence appearing the least strong during the crisis. Overall, we demonstrate for the first time in the literature that cross-border exchanges harbour versatile herding dynamics at intraday level, a finding which reflects recent advances in financial technology and the ongoing financial integration in Europe.

Intraday online investor sentiment and return patterns in the U.S. stock market

Abstract We implement a novel approach to derive investor sentiment from messages posted on social media before we explore the relation between online investor sentiment and intraday stock returns. Using an extensive dataset of messages posted on the microblogging platform StockTwits, we construct a lexicon of words used by online investors when they share opinions and ideas about the bullishness or the bearishness of the stock market. We demonstrate that a transparent and replicable approach significantly outperforms standard dictionary-based methods used in the literature while remaining competitive with more complex machine learning algorithms. Aggregating individual message sentiment at half-hour intervals, we provide empirical evidence that online investor sentiment helps forecast intraday stock index returns. After controlling for past market returns, we find that the first half-hour change in investor sentiment predicts the last half-hour S&P 500 index ETF return. Examining users’ self-reported investment approach, holding period and experience level, we find that the intraday sentiment effect is driven by the shift in the sentiment of novice traders. Overall, our results provide direct empirical evidence of sentiment-driven noise trading at the intraday level.

A new statistic to capture the level dependence in stock price volatility

Abstract In this paper, we propose a new covariance estimator based on daily opening, high, low and closing prices. We prove theoretically that the new estimator is unbiased for a pure random walk and further validate it with simulation studies. However, upon examining empirically four indices namely: NIFTY, S&P500, FTSE100 and DAX over the sample period from January 1996 to March 2015, we find that the estimator is upward biased for all the indices under study. This overreaction in stock indices can be attributed to the level dependence in stock indices, something that is not captured by the random walk model. So we explore an alternative to random walk, namely: Constant Elasticity of Variance (CEV) specification. Simulation studies provide supporting evidence that the CEV specification can capture the level dependence that makes the estimator upward biased as seen in the data. Therefore, through this specification exercise, we can see that it is possible to isolate the effect of intraday level dependence in stock prices using our estimator.

Asymmetries of the intraday return-volatility relation

This study investigates the asymmetry of the intraday return-volatility relation at different return horizons ranging from 1, 5, 10, 15, up to 60min and compares the empirical results with results for the daily return horizon. Using data on the S&P 500 (SPX) and the VIX from September 25, 2003 to December 30, 2011 and a Quantile-Regression approach, we observe strong negative return-volatility relation over all return horizons. However, this negative relation is asymmetric in three different aspects. First, the effects of positive and negative returns on volatility are different and more pronounced for negative returns. Second, for both positive and negative returns, the effect is conditional on the distribution of volatility changes. The absolute effect is up to five times larger in the extreme tails of the distribution. Third, at the intraday level, there is evidence of both autocorrelation in volatility changes and cross-autocorrelation with returns. This lead-lag relation with returns is also very asymmetric and more pronounced in the tails of the distribution. These effects are, however, not observed at the daily return horizon.

Development and Validation of RP-HPLC Method for Determination of Doxorubicin Hydrochloride from Vacuum Foam Dried Formulation

A rapid reversed phase (RP) high performance liquid chromatography (HPLC) method was developed and validated for quantitative estimation of Doxorubicin hydrochloride (Dox) from the prepared vacuum foam dried formulation. Chromatographic runs were performed on C18 column with mobile phase comprising of acetonitrile: 0.01M o-phosphoric acid (40: 60) pH 2 at a flow rate of 0.75mL.min−1. Dox was detected at a wavelength of 234nm. The method was shown to be specific, linear in the range of 0–10μg.mL−1 with correlation coefficient of 0.9999, precise at the intra-day level as reflected by relative standard deviation, accurate at recovery rate 99.60±0.13 and robust to change mobile phase and column brand. The detection and quantitation limits were 0.091μg.mL−1 and 0.2758μg.mL−1, respectively. The proposed method could be advantageous in estimation of Dox quantitation in pharmaceutical dosage form in the presence of excipients.

Dark Trading and Intraday Market Quality

This paper uses regulator-provided transaction data to investigate how trading in dark pools affects intraday market quality on the limit order book of the primary exchange for members of the FTSE 100 index. Using trading patterns from execution algorithms as instrumental variables, I show that dark trading leads to improved liquidity on the primary exchange, both in absolute terms and relative to trading on the limit order book. Although these relationships differ across stocks of different sizes, dark trading does not lead to worse market quality at the intraday level for either small or large stocks during the sample period.

Inter-Individual Differences in the Oral Bacteriome Are Greater than Intra-Day Fluctuations in Individuals

Given the advent of massively parallel DNA sequencing, human microbiome is analyzed comprehensively by metagenomic approaches. However, the inter- and intra-individual variability and stability of the human microbiome remain poorly characterized, particularly at the intra-day level. This issue is of crucial importance for studies examining the effects of microbiome on human health. Here, we focused on bacteriome of oral plaques, for which repeated, time-controlled sampling is feasible. Eighty-one supragingival plaque subjects were collected from healthy individuals, examining multiple sites within the mouth at three time points (forenoon, evening, and night) over the course of 3 days. Bacterial composition was estimated by 16S rRNA sequencing and species-level profiling, resulting in identification of a total of 162 known bacterial species. We found that species compositions and their relative abundances were similar within individuals, and not between sampling time or tooth type. This suggests that species-level oral bacterial composition differs significantly between individuals, although the number of subjects is limited and the intra-individual variation also occurs. The majority of detected bacterial species (98.2%; 159/162), however, did not fluctuate over the course of the day, implying a largely stable oral microbiome on an intra-day time scale. In fact, the stability of this data set enabled us to estimate potential interactions between rare bacteria, with 40 co-occurrences supported by the existing literature. In summary, the present study provides a valuable basis for studies of the human microbiome, with significant implications in terms of biological and clinical outcomes.

Into the Light: Dark Pool Trading and Intraday Market Quality on the Primary Exchange

This paper uses regulator-provided transaction data to investigate how trading in dark pools affects intraday market quality on the limit order book of the primary exchange for members of the FTSE 100 index. Using trading patterns from execution algorithms as instrumental variables, I show that dark trading leads to improved liquidity on the primary exchange, both in absolute terms and relative to trading on the limit order book. Although these relationships differ across stocks of different sizes, dark trading does not lead to worse market quality at the intraday level for either small or large stocks during the sample period.

A Dynamic Fuzzy Money Management Approach for Controlling the Intraday Risk‐Adjusted Performance of AI Trading Algorithms

SummaryThe majority of existing artificial intelligence (AI) studies in computational finance literature are devoted solely to predicting market movements. In this paper we shift the attention to how AI can be applied to control risk‐based money management decisions. We propose an innovative fuzzy logic approach which identifies and categorizes technical rules performance across different regions in the trend and volatility space. The model dynamically prioritizes higher performing regions at an intraday level and adapts money management policies with the objective to maximize global risk‐adjusted performance. By adopting a hybrid method in conjunction with a popular neural network (NN) trend prediction model, our results show significant performance improvements compared with both standard NN and buy‐and‐hold approaches. Copyright © 2014 John Wiley & Sons, Ltd.

The determinants and market reaction to Open Briefings: an investor relations option and evidence on the effectiveness of disclosure

AbstractOpen Briefings are market announcements styled as question and answer transcripts from a mock analyst interview and run by Orient Capital, an investor relations consultant. We found that Open Briefings are used by both growth and mature firms, and that Open Briefings are significant market events at both the daily and intraday level. In addition, the positive abnormal return does not soon reverse, suggesting Open Briefings are used by investors. We contribute to the existing literature by finding a stronger market reaction for firms with greater incentives to increase market awareness.

Information-theoretic approach to lead-lag effect on financial markets

Recently the interest of researchers has shifted from the analysis of synchronous relationships of financial instruments to the analysis of more meaningful asynchronous relationships. Both of those analyses are concentrated only on Pearson's correlation coefficient and thus intraday lead-lag relationships associated with such. Under Efficient Market Hypothesis such relationships are not possible as all information is embedded in the prices. In this paper we analyse lead-lag relationships of financial instruments and extend known methodology by using mutual information instead of Pearson's correlation coefficient, which not only is a more general measure, sensitive to non-linear dependencies, but also can lead to a simpler procedure of statistical validation of links between financial instruments. We analyse lagged relationships using NYSE 100 data not only on intraday level but also for daily stock returns, which has usually been ignored.

System-wide tail comovements: A bootstrap test for cojump identification on the S&P 500, US bonds and currencies

This paper studies bivariate tail comovements on financial markets that are of crucial importance for the world economy: the S&P 500, US bonds, and currencies. We propose to study that form of dependence under the lens of cojump identification in a bivariate Brownian semimartingale with idiosyncratic jumps, as well as cojumps. Whereas univariate jump identification has been widely studied in the high-frequency data literature, the multivariate literature on cojump identification is more recent and scarcer. Cojump identification is of interest, as it may identify comovements which are not trivially visible in a univariate setting. That is, price changes can be small relative to local variation, but still abnormal relative to local covariation. This paper investigates how simple parametric bootstrapping of the product of assets' intraday returns can help detect cojumps in a multivariate Brownian semi-martingale with both idiosyncratic jumps and cojumps. In particular, we investigate how to disentangle idiosyncratic jumps from common jumps at an intraday level for pairs of assets. The approach is flexible, trivial to implement, and yields good power properties. It allows to shed new light on extreme dependence at the world economy level. We detect cojumps of heterogeneous size which are partly undetected with a univariate approach. We find an increased cojump intensity after the crisis on the S&P 500-US bonds pair before a return to normal.

Enhancing risk-adjusted performance of stock market intraday trading with Neuro-Fuzzy systems

Whilst the interest of many former studies on the application of AI in finance is solely on predicting market movements, trading practitioners are predominantly concerned about risk-adjusted performance. This paper provides new insights into improving the time-varying risk-adjusted performance of trading systems controlled by Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Systems (ANFIS) or Dynamic Evolving Neuro Fuzzy Systems (DENFIS). Contrary to most former studies which focus on daily predictions, we compare these models in an intraday stock trading scenario using high-frequency data. Firstly, we propose a dynamic extension of the popular moving average rule and enhance it with a model validation methodology using heat maps to analyse favourable profitability in specific holding time and signal regions. Secondly, we study the effect of realistic constraints such as transaction costs and intraday trading hours, which many existing approaches in the literature ignore. Thirdly, unlike most former studies that only aim to minimise statistical error measures, we compare this approach with financially more relevant risk-adjusted objective functions. To this end, we also consider an innovative ANFIS ensemble architecture which on an intraday level dynamically selects between different risk-adjusted models. Our study shows that accounting for transaction costs and the use of risk-return objective functions provide better results in out-of-sample tests. Overall, the ANN model is identified as a viable model, however ANFIS shows more stable time-varying performance across multiple market regimes. Moreover, we find that combining multiple risk-adjusted objective functions using an ANFIS ensemble yields promising results.

Long Memory and Periodicity in Intraday Volatility

Intraday return volatility is characterized by the contemporaneous presence of periodicity and long memory. This article proposes two new parameterizations of the intraday volatility process that account for both features: the Fractionally Integrated Periodic EGARCH and the Seasonal Fractional Integrated Periodic EGARCH. The analysis of hourly E-mini S&P 500 futures returns shows that the volatility is characterized by a statistically significant long-range dependence coupled with a periodic leverage effect, with negative return shocks having a larger effect on volatility during the US trading period. Long memory estimates obtained with nonperiodic long memory models are greater than those obtained with FI-PEGARCH and SFI-PEGARCH models. A simulation experiment shows that the long memory component can be strongly biased when periodic patterns are not properly modelled at the intraday level. An out-of-sample forecasting comparison with alternative models shows that a constrained version of the FI-PEGARCH provides superior forecasts.

The hydrological cycle in the ravine oak forests of steppe Prydniprovia

The characteristic of the hydrological cycle in the ravine oak forest is given in the work. In its preparation, we proceeded from assumption about the participation of groundwater in water feeding of wood vegetation. To confirm or refute this the term average groundwater depth, the changes in its long-term, seasonal and intraday level dynamics were analyzed. The intra-annual structure of the groundwater regime was established, while in the structure the three specific periods were identified – winter-spring recovery, spring-autumn recession and autumn recovery. It was established that the duration of the hydrological year between the beginning of winter-spring recovery and the ending of autumn recovery was about 11.5 months. Most often, the hydrological year flows between January and December of neighboring hydrological years. The groundwater depth during the observation period ranged from 7.65 to 10.08 m from the surface. The amplitude of level changes between seasons was 0.57–1.59 m. The speed of rise and fall level was set, while the instability of characterized indicators was observed, that is closely related to the temperature of atmospheric air of the previous period. The annual dynamics of groundwater level is compensatory in nature, in which the spring-autumn recession is compensated autumn and winter-spring recovery. From the position of winter minimum level and the deviation of the average annual rainfall norms, it was found that the course of the annual changes level has a close correlation with the amount of precipitation. Intraday dynamics of level conditions during the vegetation period has stepwise, descending character, which means that there is no direct connection of groundwater with woody vegetation. It was found that at the absence of available groundwater, the incoming part of the water balance was determined by the vegetation period precipitation and the moisture contained in the soil at the beginning of the vegetation period. The amount of precipitation of the vegetation period, excluding precipitation detained by the cover and litter, during the observation period was over 292 mm. Dynamics of soil moisture in 1.5 meters layer showed that the most dynamic changes in moisture took place in the upper meter of soil. During the non-growing period in the 1.5 meters layer up to 252 mm of moisture is accumulated. The total amount of rainfall and soil moisture in the 1.5 meters layer of soil at the beginning of vegetative period averages 542 mm, 46 % of which is soil moisture, and the remaining 54 % is the precipitation moisture. The average annual evapotranspiration coefficient is calculated from the ratio of the amount of soil moisture at the beginning of the vegetative period and all vegetative period precipitation to evapotranspiration of appropriate period of time. In this approach the figure for ravined oak forests is 0.78, 0.63 – for steppe plots of virgin land. The general moisture conditions prevailing here are local, are formed under the influence of geomorphological features on the background of climatic conditions and are responsible to the intrazonal type.

System-Wide Tail Comovements: A Bootstrap Test for Cojump Identification on the S&P 500, US Bonds and Exchange Rates

This paper studies bivariate tail comovements on financial markets that are of crucial importance for the world economy: The S&P 500, US bonds, and currencies. We propose to study that form of dependence under the lens of cojump identification in a bivariate Brownian semimartingale with idiosyncratic jumps, as well as cojumps. Whereas univariate jump identification has been widely studied in the high-frequency data literature, the multivariate literature on cojump identification is more recent and scarcer. Cojump identification is of interest, as it may identify comovements which are not trivially visible in a univariate setting. That is, price changes can be small relative to local variation, but still abnormal relative to local covariation. This paper investigates how simple parametric bootstrapping of the product of assets' intraday returns can help detect cojumps in a multivariate Brownian semi-martingale with both idiosyncratic jumps and cojumps. In particular, we investigate how to disentangle idiosyncratic jumps from common jumps at an intraday level for pairs of assets. The approach is flexible, trivial to implement, and yields good power properties. It allows to shed new light on extreme dependence at the world economy level. We detect cojumps of heterogeneous size which are partly undetected with a univariate approach. We find an increased cojump intensity after the crisis on the S&P 500-US bonds pair before a return to normal.

Dark Trading on Public Exchanges

Over the last decade, market participants increasingly use trading tools that allow them to hide their trading intentions. We study how “dark trading” in the form of fully hidden, or dark, orders posted on a visible exchange affects the quality of the visible market. Dark orders were introduced on the Toronto Stock Exchange in 2011 in two stages, allowing us to employ a difference-in-differences approach to isolate the causal effect of the availability of dark trading. Using order level data, we observe that the introduction of dark orders led to a widening of displayed quoted spreads and an increase in trading costs, while leaving depth, overall volume, and volatility unaffected. At the intra-day level, dark trading leads to decreased displayed quoted spreads, increased depth, increased volume and to reduced trading costs and volatility. These findings may be interpreted as two sides of the same coin: the possible presence of dark liquidity causes market participants to post visible quotes more carefully. Upon detecting dark executions, however, traders infer that dark liquidity is diminished and thus may post quotes more aggressively.

A re-examination of the “zero is enough” hypothesis in the emergence of financial stylized facts

In recent years, a growing literature has claimed that the market microstructure is sufficient to generate the so-called stylized facts without any reference to the behaviour of market players. Indeed, qualitative stylized-facts can be generated with zero-intelligence traders (ZITs) but we stress that they are without any quantitative predictive power. In this paper we show that in most of the cases, such qualitative stylized facts hide unrealistic price motions at the intraday level and ill-calibrated return processes as well. To generate realistic price motions and return series with adequate quantitative values is out-of-reach using pure ZIT populations. To do so, one must increasingly constrain agents’ choices to a point where it is hard to claim that their behaviour is completely random. In addition we show that even with highly constrained ZIT agents, one cannot reproduce real time series from these. Except in a few cases, first order moments of ZITs never equal real data ones. We therefore claim that stylized facts produced by means of ZIT agents are useless for financial engineering.

Does herding affect volatility? Implications for the Spanish stock market

According to rational expectation models, uninformed or liquidity trading make market price volatility rise. This paper sets out to analyse the impact of herding, which may be interpreted as one of the components of uninformed trading, on the volatility of the Spanish stock market. Herding is examined at the intraday level, considered the most reliable sampling frequency for detecting this type of investor behavior, and measured using the Patterson and Sharma (Working Paper, University of Michigan–Dearborn, 2006) herding intensity measure. Different volatility measures (historical, realized and implied) are employed. The results confirm that herding has a direct linear impact on volatility for all of the volatility measures considered, although the corresponding intensity is not always the same. In fact, herding variables seem to be useful in volatility forecasting and therefore in decision making when volatility is considered a key factor.