Discovering granger-causal features from deep learning networks

© Springer Nature Switzerland AG 2018. In this research, we propose deep networks that discover Granger causes from multivariate temporal data generated in financial markets. We introduce a Deep Neural Network (DNN) and a Recurrent Neural Network (RNN) that discover Granger-causal features for bivariate regression on bivariate time series data distributions. These features are subsequently used to discover Granger-causal graphs for multivariate regression on multivariate time series data distributions. Our supervised feature learning process in proposed deep regression networks has favourable F-tests for feature selection and t-tests for model comparisons. The experiments, minimizing root mean squared errors in the regression analysis on real stock market data obtained from Yahoo Finance, demonstrate that our causal features significantly improve the existing deep learning regression models

Volatility forecasting in the Chinese commodity futures market with intraday data

Given the unique institutional regulations in the Chinese commodity futures market as well as the characteristics of the data it generates, we utilize contracts with three months to delivery, the most liquid contract series, to systematically explore volatility forecasting for aluminum, copper, fuel oil, and sugar at the daily and three intraday sampling frequencies. We adopt popular volatility models in the literature and assess the forecasts obtained via these models against alternative proxies for the true volatility. Our results suggest that the long memory property is an essential feature in the commodity futures volatility dynamics and that the ARFIMA model consistently produces the best forecasts or forecasts not inferior to the best in statistical terms

Stock markets and effective exchange rates in European countries: threshold cointegration findings

© 2015, Eurasia Business and Economics Society. The nexus between stock markets and exchange rates is examined in the case of eight European countries. The sample consists of four economies with national currencies and four that have adopted the euro. Thus, if differences between the two groups in the relationship governing the two markets exist, they will be unveiled. To this effect, a threshold cointegration methodology is adopted that allows for more reliable inferences to be drawn for both the short and long run nexus between the two markets. Monthly data is used covering the period 01/2000–12/2014. The findings reported herein offer support in favor of the portfolio approach thesis over the recent economic crisis period, but this finding is not the case for the entire sample. Bidirectional causality is found for Norway and the UK, pointing to a currency effect on stock markets. In view of the findings reported herein, policies aiming at reducing uncertainty in the stock markets can exert beneficial effects on currency markets

Emerging interdependence between stock values during financial crashes

To identify emerging interdependencies between traded stocks we investigate the behavior of the stocks of FTSE 100 companies in the period 2000-2015, by looking at daily stock values. Exploiting the power of information theoretical measures to extract direct influences between multiple time series, we compute the information flow across stock values to identify several different regimes. While small information flows is detected in most of the period, a dramatically different situation occurs in the proximity of global financial crises, where stock values exhibit strong and substantial interdependence for a prolonged period. This behavior is consistent with what one would generally expect from a complex system near criticality in physical systems, showing the long lasting effects of crashes on stock markets

Frequency-specific hippocampal-prefrontal interactions during associative learning

Much of our knowledge of the world depends on learning associations (for example, face-name), for which the hippocampus (HPC) and prefrontal cortex (PFC) are critical. HPC-PFC interactions have rarely been studied in monkeys, whose cognitive and mnemonic abilities are akin to those of humans. We found functional differences and frequency-specific interactions between HPC and PFC of monkeys learning object pair associations, an animal model of human explicit memory. PFC spiking activity reflected learning in parallel with behavioral performance, whereas HPC neurons reflected feedback about whether trial-and-error guesses were correct or incorrect. Theta-band HPC-PFC synchrony was stronger after errors, was driven primarily by PFC to HPC directional influences and decreased with learning. In contrast, alpha/beta-band synchrony was stronger after correct trials, was driven more by HPC and increased with learning. Rapid object associative learning may occur in PFC, whereas HPC may guide neocortical plasticity by signaling success or failure via oscillatory synchrony in different frequency bands.National Institute of Mental Health (U.S.) (Conte Center Grant P50-MH094263-03)National Institute of Mental Health (U.S.) (Fellowship F32-MH081507)Picower Foundatio

Identifying the Rules of Engagement Enabling Leukocyte Rolling, Activation, and Adhesion

The LFA-1 integrin plays a pivotal role in sustained leukocyte adhesion to the endothelial surface, which is a precondition for leukocyte recruitment into inflammation sites. Strong correlative evidence implicates LFA-1 clustering as being essential for sustained adhesion, and it may also facilitate rebinding events with its ligand ICAM-1. We cannot challenge those hypotheses directly because it is infeasible to measure either process during leukocyte adhesion following rolling. The alternative approach undertaken was to challenge the hypothesized mechanisms by experimenting on validated, working counterparts: simulations in which diffusible, LFA1 objects on the surfaces of quasi-autonomous leukocytes interact with simulated, diffusible, ICAM1 objects on endothelial surfaces during simulated adhesion following rolling. We used object-oriented, agent-based methods to build and execute multi-level, multi-attribute analogues of leukocytes and endothelial surfaces. Validation was achieved across different experimental conditions, in vitro, ex vivo, and in vivo, at both the individual cell and population levels. Because those mechanisms exhibit all of the characteristics of biological mechanisms, they can stand as a concrete, working theory about detailed events occurring at the leukocyte–surface interface during leukocyte rolling and adhesion experiments. We challenged mechanistic hypotheses by conducting experiments in which the consequences of multiple mechanistic events were tracked. We quantified rebinding events between individual components under different conditions, and the role of LFA1 clustering in sustaining leukocyte–surface adhesion and in improving adhesion efficiency. Early during simulations ICAM1 rebinding (to LFA1) but not LFA1 rebinding (to ICAM1) was enhanced by clustering. Later, clustering caused both types of rebinding events to increase. We discovered that clustering was not necessary to achieve adhesion as long as LFA1 and ICAM1 object densities were above a critical level. Importantly, at low densities LFA1 clustering enabled improved efficiency: adhesion exhibited measurable, cell level positive cooperativity

Full-length human placental sFlt-1-e15a isoform induces distinct maternal phenotypes of preeclampsia in mice

<div><p>Objective</p><p>Most anti-angiogenic preeclampsia models in rodents utilized the overexpression of a truncated soluble fms-like tyrosine kinase-1 (sFlt-1) not expressed in any species. Other limitations of mouse preeclampsia models included stressful blood pressure measurements and the lack of postpartum monitoring. We aimed to 1) develop a mouse model of preeclampsia by administering the most abundant human placental sFlt-1 isoform (hsFlt-1-e15a) in preeclampsia; 2) determine blood pressures in non-stressed conditions; and 3) develop a survival surgery that enables the collection of fetuses and placentas and postpartum (PP) monitoring.</p><p>Methods</p><p>Pregnancy status of CD-1 mice was evaluated with high-frequency ultrasound on gestational days (GD) 6 and 7. Telemetry catheters were implanted in the carotid artery on GD7, and their positions were verified by ultrasound on GD13. Mice were injected through tail-vein with adenoviruses expressing hsFlt-1-e15a (n = 11) or green fluorescent protein (GFP; n = 9) on GD8/GD11. Placentas and pups were delivered by cesarean section on GD18 allowing PP monitoring. Urine samples were collected with cystocentesis on GD6/GD7, GD13, GD18, and PPD8, and albumin/creatinine ratios were determined. GFP and hsFlt-1-e15a expression profiles were determined by qRT-PCR. Aortic ring assays were performed to assess the effect of hsFlt-1-e15a on endothelia.</p><p>Results</p><p>Ultrasound predicted pregnancy on GD7 in 97% of cases. Cesarean section survival rate was 100%. Mean arterial blood pressure was higher in hsFlt-1-e15a-treated than in GFP-treated mice (∆MAP = 13.2 mmHg, p = 0.00107; GD18). Focal glomerular changes were found in hsFlt-1-e15a -treated mice, which had higher urine albumin/creatinine ratios than controls (109.3±51.7μg/mg vs. 19.3±5.6μg/mg, p = 4.4x10^-2; GD18). Aortic ring assays showed a 46% lesser microvessel outgrowth in hsFlt-1-e15a-treated than in GFP-treated mice (p = 1.2x10^-2). Placental and fetal weights did not differ between the groups. One mouse with liver disease developed early-onset preeclampsia-like symptoms with intrauterine growth restriction (IUGR).</p><p>Conclusions</p><p>A mouse model of late-onset preeclampsia was developed with the overexpression of hsFlt-1-e15a, verifying the <i>in vivo</i> pathologic effects of this primate-specific, predominant placental sFlt-1 isoform. HsFlt-1-e15a induced early-onset preeclampsia-like symptoms associated with IUGR in a mouse with a liver disease. Our findings support that hsFlt-1-e15a is central to the terminal pathway of preeclampsia, and it can induce the full spectrum of symptoms in this obstetrical syndrome.</p></div

The effects of crude oil price volatility, stock price, exchange rate and interest rate on Malaysia’s economic growth

This study examines the effects and relationships between Malaysia’s economic growth and selected variables which are oil price volatility, stock price, real exchange rate and real interest rate. Using time-series data methodology, the study employs unit root test using Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP), Auto-Regressive Distribution Lag (ARDL) model supplemented by Bounds F-Testing, Johansen-Julius Co-integration test and Granger causality test. The long�run equation derived from ARDL shows that there are positive relationships for stock price and real exchange rate whilst there are negative relationships between oil price volatility and real interest rate. Furthermore, Granger causality test shows that only stock price and real interest rates have an impact on Malaysia’s gross domestic product (GDP) in the short run. Finally, sound policy recommendations are suggested, in particular, to address oil price volatility in a forward looking manner as well as monetary-friendly measures to further support Malaysia’s economic growth