Intraday Measures Research Articles

Public attention, sentiment and the default of Silicon Valley Bank

We assess the interplay between public attention and trading of the Silicon Valley Bank (SVB) stock before its default on March 10, 2023. Based on intra-day data in 15-min intervals, we estimate SVB market excess returns and match these with intra-day measures of investor attention based on the relative number of tweets and Google searches. Wavelet analysis reveals bilateral lead–lag patterns between both series and demonstrates that a higher level of attention led to a significant decrease in SVB returns. Thereby, the results provide evidence that Twitter sentiment and media attention ultimately fueled and accelerated the crash dynamics of Silicon Valley Bank. Economically, Twitter provides information at lower costs and higher effectiveness than newspapers and allows direct communication without potential distortions from media bias or timing lags in reporting. Hence, individuals can coordinate and communicate their run beliefs at a much faster pace, emphasizing the importance for financial stability.

Associations between short-term temperature exposure and kidney-related conditions in New York State: The influence of temperature metrics across four dimensions

BackgroundEvidence describing the relationship between short-term temperature exposure and kidney-related conditions is insufficient. It remains unclear how temperature specification affects estimation of these associations. This study aimed to assess associations between short-term temperature exposure and seven kidney-related conditions and to evaluate the influence of temperature specification. MethodsWe obtained data on hospital encounters in New York State (2007–2016). We assessed associations with a case-crossover design using conditional logistic regression with distributed lag non-linear models. We compared model performance (i.e., AIC) and association curves using 1) five temperature spatial resolutions; 2) temperature on an absolute versus relative scale; 3) seven temperature metrics incorporating humidity, wind speed, and/or solar radiation; and 4) five intraday temperature measures (e.g., daily minimum and daytime mean). ResultsWe included 1,209,934 unplanned adult encounters. Temperature metric and intraday measure had considerably greater influence than spatial resolution and temperature scale. For outcomes not associated with temperature exposure, almost all metrics or intraday measures showed good model performance; for outcomes associated with temperature, there were meaningful differences in performance across metrics or intraday measures. For parsimony, we modelled daytime mean outdoor wet-bulb globe temperature, which showed good performance for all outcomes. At lag 0–6 days, we observed increased risk at the 95th percentile of temperature versus the minimum morbidity temperature for acute kidney failure (odds ratio [OR] = 1.36, 95% confidence interval [CI]: 1.09, 1.69), urolithiasis (OR = 1.41, 95% CI: 1.16, 1.70), dysnatremia (OR = 1.26, 95% CI: 1.01, 1.59), and volume depletion (OR = 1.88, 95% CI: 1.41, 2.51), but not for glomerular diseases, renal tubulo-interstitial diseases, and chronic kidney disease. ConclusionsHigh-temperature exposure over one week is a risk factor for acute kidney failure, urolithiasis, dysnatremia, and volume depletion. The differential model performance across temperature metrics and intraday measures indicates the importance of careful selection of exposure metrics when estimating temperature-related health burden.

Development of a Novel Molecularly Imprinted Polyvinylimidazole/Functionalized Carbon Black Nanocomposite‐based Paste Electrode for Electrochemical Sensing of Imazethapyr in Rice Samples

AbstractThis paper reports the development of a simple electroanalytical method for imazethapyr (IMT) determination in rice samples based on molecularly imprinted polymer and functionalized carbon black paste electrode (MIP‐fCBPE). Carbon black (CB) was functionalized by the insertion of oxygenated functional groups upon acid treatment with HNO3 and H2SO4. The functionalized carbon black (fCB) presented higher performance for IMT determination than the CB without functionalization. The insertion of molecularly imprinted polyvinylimidazole (MIP‐VN) in the fCBPE promoted a significant increase in the cathodic peak current even at low proportions (7.5 % w/w) due to the specific binding sites for IMT recognition. For IMT determination, DPV parameters were optimized by the Doehlert matrix applying 0.1 V for 60 s as pre‐treatment in acetate buffer solution (pH 3.0) as supporting electrolyte. The proposed method showed low limit of detection (0.03 μmol L−1), a wide linear range (0.10–70.00 μmol L−1), and good precision in terms of repeatability of intraday measures (RSD%=3.6). The method was applied in rice samples after microwave‐assisted extraction of IMT and the accuracy of the method was evaluated by addition/recovery assays (96.3–105.7 %), being statistically attested using HPLC‐DAD as reference technique.

Forecasting risk measures using intraday data in a generalized autoregressive score framework

A new framework for the joint estimation and forecasting of dynamic value at risk (VaR) and expected shortfall (ES) is proposed by our incorporating intraday information into a generalized autoregressive score (GAS) model introduced by Patton et al., 2019 to estimate risk measures in a quantile regression set-up. We consider four intraday measures: the realized volatility at 5-min and 10-min sampling frequencies, and the overnight return incorporated into these two realized volatilities. In a forecasting study, the set of newly proposed semiparametric models are applied to four international stock market indices (S&P 500, Dow Jones Industrial Average, Nikkei 225 and FTSE 100) and are compared with a range of parametric, nonparametric and semiparametric models, including historical simulations, generalized autoregressive conditional heteroscedasticity (GARCH) models and the original GAS models. VaR and ES forecasts are backtested individually, and the joint loss function is used for comparisons. Our results show that GAS models, enhanced with the realized volatility measures, outperform the benchmark models consistently across all indices and various probability levels.

Forecasting Risk Measures Using Intraday Data in a Generalized Autoregressive Score (GAS) Framework

A new framework for the joint estimation and forecasting of dynamic Value-at-Risk (VaR) and Expected Shortfall (ES) is proposed by incorporating intraday information into a generalized autoregressive score (GAS) model, introduced by Patton, Ziegel, and Chen (2019) to estimate risk measures in a quantile regression setup. We consider four intraday measures: the realized variance at 5-min and 10-min sampling frequencies, and the overnight return incorporated into these two realized variances. In a forecasting study, the set of newly proposed semiparametric models is applied to 4 international stock market indices: the S&P 500, the Dow Jones Industrial Average, the NIKKEI 225 and the FTSE 100, and is compared with a range of parametric, nonparametric and semiparametric models including historical simulations, GARCH and the original GAS models. VaR and ES forecasts are backtested individually, and the joint loss function is used for comparisons. Our results show that GAS models, enhanced with the realized variance measures (especially at 5 minutes frequency), outperform the benchmark models consistently across all indices and various probability levels.

Repeatability of Arterial Spin Labeling MRI in Measuring Blood Perfusion in the Human Eye.

Quantifying blood perfusion in ocular tissues is challenging, partly because the majority of the blood is carried by the choroid, which is difficult to visualize because it is located between the retina and sclera. To evaluate the intra- and interday repeatability of MRI measures of chorio-retinal blood perfusion. Prospective, cross-sectional, observational study. Twenty young healthy adults (six male, age: 25 ± 5 years) scanned twice within a single session repeated at the same time of day on 2 days. Arterial spin labeling (ASL) MRI at 3.0T using pseudocontinuous ASL (PCASL) labeling scheme and a 3D turbo-gradient-spin-echo (TGSE) acquisition, including axial T2 -weighted structural images using a 2D turbo-spin-echo (TSE) sequence. Region-of-interest analysis for assessment of chorio-retinal blood perfusion. Intra- and interday repeatability of measures analyzed using intraclass correlation coefficients (ICC), Pearson's correlation analysis, paired t-tests, and Bland-Altman plots. The mean chorio-retinal perfusion was 77.86 (standard deviation [SD] = 29.80) ml/100ml/min. Perfusion measurements correlated strongly within a single session (r = 0.95, 95% confidence interval [CI] [0.880-0.980], P < 0.001) and between the two sessions based on a single run (r = 0.80 [0.582-0.913], P < 0.001), and two runs (r = 0.80 [0.479-0.918], P < 0.001). There were mean differences of 2.69 [16.85 to -22.23] ml/100ml/min for intraday measures, -7.44 [27.45 to -42.32] ml/100ml/min for single-run interday measures, and 5.73 [28.71 to -40.17] ml/100ml/min for two-run interday measures, but none were significant (all P > 0.05). Quantitative ASL-MRI measurements of chorio-retinal blood perfusion showed high intra- and interday repeatability. The ASL-MRI technique provides reliable measures of chorio-retinal perfusion in vivo. 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;49:966-974.

Information Shocks and Liquidity Innovations

We examine the intraday impact of predictable and measurable public information shocks on various dimensions of liquidity in a very fast and liquid market. These information shocks resolve information asymmetry, move prices in the direction of the information, and lead investors to increase liquidity as measured by quoted spreads, volume, depth, and absolute order imbalance. Moreover, price impact and effective spread are higher as trading moves prices to reflect the new information. Our findings complement recent work of Collin-Dufresne and Fos (2015) and Foucault, Hombert, and Rosu (2016) by showing how intraday liquidity measures respond to information shocks in the presence of high frequency traders.

A High-Frequency Analysis of Trading Activity in the Corporate Bond Market: Do Macro Announcements Drive Activity?

Herein we explore whether macroeconomic announcements are a driving factor in the trading activity of US corporate bond market. Prior studies have documented a strong response of bond returns and interest rates to surprises in macroeconomic data in the US Treasury and Treasury futures markets. Likewise, studies have also documented that trading activity changes sharply, based on informational impulses provided by the release of economic data. We contribute to the existing literature by examining how both daily and intraday measures of trading activity are impacted by surprises in macro data. Our main findings are that the thinly-traded market for corporate bonds is largely unaffected by surprises in individual economic reports and that the market is dominated by day-of-week and time-of-day affects. The results stand in contrast to prior studies documenting increased financial market activity, such as Andersen, Bollerslev, Diebold and Vega (2003). The existence of intraday or “diurnal” trading patterns is consistent with earlier findings in Anderson and Bollerslev (1997,1998) studies with respect to intraday asset returns and volatility. Engle and Ng (1993) and Engle and Solkasa (2012)

Sentiment Effects in the European Emissions Market

The pricing of European emissions allowances (EUAs) is complicated by the market being driven by not just financial and economic factors, but also the harder-to-gauge uncertain influences of politics and policy. Drawing on a well-established literature showing sentiment to play an expanded role in investment decision-making in the face of uncertainty, we develop a social media sentiment measure through aggregating pertinent Twitter posts about European emissions. Our sentiment measure is significantly predictive of EUA price and price volatility movements. This relationship is shown using hourly intra-day sentiment and price measures for the final year of the December 2013 EUA futures contract. For prices and returns we demonstrate that there is bi-directional Granger causality between changes in sentiment and changes in EUA prices, and we establish that periods of strong (weak) sentiment correspond with periods of high (low) EUA return volatility. These findings demonstrate that sentiment matters in EUA pricing, that it can be measured using social media, and more broadly suggest that sentiment should matter in other young asset markets with scope for uncertain political interference and policy developments.

A High-Frequency Analysis of Trading Activity in the Corporate Bond Market: Do Macro Announcements Drive Activity?

Herein we explore whether macroeconomic announcements are a driving factor in the trading activity of US corporate bond market. Prior studies have documented a strong response of bond returns and interest rates to surprises in macroeconomic data in the US Treasury and Treasury futures markets. Likewise, studies have also documented that trading activity changes sharply, based on informational impulses provided by the release of economic data. We contribute to the existing literature by examining how both daily and intraday measures of trading activity are impacted by surprises in macro data. Our main findings are that the thinly-traded market for corporate bonds is largely unaffected by surprises in individual economic reports and that the market is dominated by day-of-week and time-of-day affects. The results stand in contrast to prior studies documenting increased financial market activity, such as Andersen, Bollerslev, Diebold and Vega (2003). The existence of intraday or “diurnal” trading patterns is consistent with earlier findings in Anderson and Bollerslev (1997,1998) studies with respect to intraday asset returns and volatility. Engle and Ng (1993) and Engle and Solkasa (2012)

Intraday LeBaron effects

We study the relation at intraday level between serial correlation and volatility of the Standard and Poor (S&amp;P) 500 stock index futures returns. At daily and weekly levels, serial correlation and volatility forecasts have been found to be negatively correlated (LeBaron effect). After finding a significant attenuation of the original effect over time, we show that a similar but more pronounced effect holds by using intraday measures, by such as realized volatility and variance ratio. We also test the impact of unexpected volatility, defined as the part of volatility which cannot be forecasted, on the presence of intraday serial correlation in the time series by employing a model for realized volatility based on the heterogeneous market hypothesis. We find that intraday serial correlation is negatively correlated to volatility forecasts, whereas it is positively correlated to unexpected volatility.

On forecasting daily stock volatility: The role of intraday information and market conditions

Several recent studies advocate the use of nonparametric estimators of daily price variability that exploit intraday information. This paper compares four such estimators, realised volatility, realised range, realised power variation and realised bipower variation, by examining their in-sample distributional properties and out-of-sample forecast ranking when the object of interest is the conventional conditional variance. The analysis is based on a 7-year sample of transaction prices for 14 NYSE stocks. The forecast race is conducted in a GARCH framework and relies on several loss functions. The realized range fares relatively well in the in-sample fit analysis, for instance, regarding the extent to which it brings normality in returns. However, overall the realised power variation provides the most accurate 1-day-ahead forecasts. Forecast combination of all four intraday measures produces the smallest forecast errors in about half of the sampled stocks. A market conditions analysis reveals that the additional use of intraday data on day t−1 to forecast volatility on day t is most advantageous when day t is a low volume or an up-market day. These results have implications for option pricing, asset allocation and value-at-risk.

The Rise and Fall of Portfolio Pumping Among U.S. Mutual Funds

Fund managers have incentives to inflate their quarterly performance by aggressively buying stocks they already own. Using intraday measures of pumping we document that sharply increased regulatory attention in late 2000 and improved market liquidity following the implementation of Regulation NMS in 2007 are associated with fewer last-minute purchases by institutions, lower price spikes in mutual fund NAVs, and a weakened link between fund holdings and stock return reversals at period ends. Our findings suggest that portfolio pumping among U.S. mutual funds was widespread until regulatory scrutiny and heightened competition drastically diminished its prevalence.

What is a Good Information Proxy?

This article compares the qualities of several intraday activity measures as information proxy. These information proxies are: - the number of trades; - the volume of trades, - the number of incoming orders; - the volume of incomings orders. Relying on the MDH hypothesis, a good information proxy should induce a reduction of the well known deviations from the Markov-Gaussian hypothesis. Although, this approach has rarely been used at an intraday level. This articles uses several time deformations: the time is not measured in number of minutes and seconds but according to a measure of the activity. All the time deformations respect the daily structure of the data: we only distort the time measurement between the first and the last trade on each day. We then analyze the returns properties in these time measurements. The distance between the empirical facts we observe and the theoretical properties we expect enables us to rank the activity measures. Two different ways are used to compare the activity measures. First, we evaluate the reduction of the well known intraday patterns. Second, we compare the statistical properties of the returns. The conclusion of these two criteria are broadly consistent: None of the activity measure satisfy the validation criteria (disappearance of the intraday pattern and normality of the returns). Although, this study leads to several interesting conclusions. First, activity measures from the trades are better than from those from the incomings orders. Second, a number of events is preferable to a volume of events. Hence, the ranking among the four activity measures is: the number of trades, the volume of trades, the number of orders introduced, the volume of orders. This study also underlines that the results are influenced by the interval length. For instance, according to the length, the volume may or may not have an information content beyond the number of trades. This fact suggests that a better understanding of the micro-phenomena (trade-by-trade study) is required to get a firm conclusion about the informational relationships at an intraday level.

On Forecasting Daily Stock Volatility: The Role of Intraday Information and Market Conditions

Several recent studies advocate the use of nonparametric estimators of daily price variability that exploit intraday information. This paper compares four such estimators, realised volatility, realised range, realised power variation and realised bipower variation, by examining their in-sample distributional properties and out-of-sample forecast ranking when the object of interest is the conventional conditional variance. The analysis is based on a 7-year sample of transaction prices for 14 NYSE stocks. The forecast race is conducted in a GARCH framework and relies on several loss functions. The realized range fares relatively well in the in-sample fit analysis, for instance, regarding the extent to which it brings normality in returns. However, overall the realised power variation provides the most accurate 1-day-ahead forecasts. Forecast combination of all four intraday measures produces the smallest forecast errors in about half of the sampled stocks. A market conditions analysis reveals that the additional use of intraday data on day t-1 to forecast volatility on day t is most advantageous when day t is a low volume or an up-market day. The results have implications for value-at-risk analysis.

Quantitative analysis of malic and citric acids in fruit juices using proton nuclear magnetic resonance spectroscopy

1H NMR spectroscopy was applied to the quantitative determination of malic and citric acids in apple, apricot, pear, kiwi, orange, strawberry and pineapple juices. Aspartic acid was studied as a potential interference. The effect of the sample pH on the chemical shifts of signals from malic, citric and aspartic acids was examined and a value of 1.0 was selected to carry out the determination. Integration of NMR signals at 2.89–2.95 and 3.00–3.04 ppm were used for calculating the concentration of malic and citric acids, respectively. At this pH the integrated signals were not overlapped. Sodium 3-(trimethylsilyl)tetradeuteropropionate (TSP) was used as an internal reference. The obtained results applying NMR procedures to analyze the juices from different fruits were compared to those obtained using enzymatic methods and both were in close agreement. The intra- and inter-day repeatability was tested for apple juice (7.86 g l −1 malic acid, 0.32 g l −1 citric acid) and apricot juice (5.06 g l −1 malic acid, 4.79 g l −1 citric acid) obtaining coefficients of variation lower than 3.4% for intra-day measures ( n = 10) and lower than 3.8% for inter-day measures ( n = 20).

Variability of nitric oxide metabolites in exhaled breath condensate

The collection of exhaled breath condensate (EBC) is simple and non-invasive, however, there are few data on the methodological aspects affecting concentrations of compounds in EBC. The aim of this study was to investigate methodological issues for measuring nitric oxide metabolites (NO(x)) in EBC. Twenty-five healthy adults (12 females, age range 23-55 years) and 22 children (11 females, age range 7-6 years) were recruited for studies investigating inter- and intra-day repeatability, repeatability with controlled expiratory flows and temperature, flow dependence, and analytical variability of EBC NO(x). Both intra- and inter-day repeatability was poor with a coefficient of repeatability of 103.4% of the mean difference between intra-day (15 min) measures and 118.6% of inter-day (24 h) differences. Repeatability was not improved when expiratory flow and temperature of the collection device were controlled. However, some of the variability (approximately 50%) may be accounted for by variability in the analytical technique (analytical variability) and this may result from difficulties in controlling for contamination. NO(x) levels were not affected by different expiratory flows in either adults or children but there was still significant variation within individuals. Levels of NO(x) in EBC seem to be highly variable and this needs to be considered if EBC NO(x) is to be used in clinical studies.

Liquidity costs: Screen-based trading versus open outcry

The results reported in this paper challenge the popular belief that screen-based trading offered lower liquidity costs than the open-outcry approach during its first year of side-by-side operation in the U.S. financial derivatives market. Using time and sales data from the Chicago Board of Trade (CBOT) market profile data series, effective bid–ask spreads are estimated on the basis of daily and intraday measures of the Thompson–Waller and Smith–Whaley estimators. We find liquidity costs on the screen-based system vary with time and the level of floor trading activity. In particular, a one-tick market is observed just before the opening of the Chicago trading floor (6:30 to 7:30 am). However, subsequent intraday spreads exhibit the familiar “reverse J-shaped pattern”—highest following the opening of floor trading, declining until afternoon, and then increasing until close. Meanwhile, daily spread estimates average almost a quarter-tick higher on the screen-based market relative to the one-tick spread commonly associated with open outcry. This relationship remained robust across sample time-series and conservative price-change specifications. Since the study was conducted, electronic trading has become the predominant exchange medium for financial derivatives at the CBOT, following the example set in Europe's traditional futures exchanges, e.g. France's Matif, Germany's Deutsche Bourse and the U.K.'s Liffe.