30,580 research outputs found
Deep Learning for Forecasting Stock Returns in the Cross-Section
Many studies have been undertaken by using machine learning techniques,
including neural networks, to predict stock returns. Recently, a method known
as deep learning, which achieves high performance mainly in image recognition
and speech recognition, has attracted attention in the machine learning field.
This paper implements deep learning to predict one-month-ahead stock returns in
the cross-section in the Japanese stock market and investigates the performance
of the method. Our results show that deep neural networks generally outperform
shallow neural networks, and the best networks also outperform representative
machine learning models. These results indicate that deep learning shows
promise as a skillful machine learning method to predict stock returns in the
cross-section.Comment: 12 pages, 2 figures, 8 tables, accepted at PAKDD 201
Economic Integration in East Asia: Trends, Prospects, and a Possible Roadmap
This paper, which is a revised version of the ADB Working Paper on Regional Economic Integration No. 2, reviews trends in East Asian regionalism in the areas of trade and investment, money and finance, and infrastructure. It finds that trade and, to a lesser extent, financial integration is starting to increase in the region. It also finds that business cycles are starting to be more synchronized, enhancing the case for further monetary integration among these countries. The paper also outlines a roadmap for East Asian integration.
Economic Integration in East Asia: Trends, Prospects, and a Possible Roadmap
This paper reviews trends in East Asian regionalism in the areas of trade and investment, money and finance, and infrastructure. It presents various measures of trade and financial integration. An important finding of the paper is that increasing trade and financial integration in the region is now starting to lead to a synchronization of business cycles in a selected group of countries, further enhancing the case for monetary integration among these countries. The paper also outlines a roadmap for East Asian integration.ASEAN/East Asian economic cooperation and integration; business cycle synchronization; free trade agreements; policy coordination
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PERSIANN-MSA: A precipitation estimation method from satellite-based multispectral analysis
Visible and infrared data obtained from instruments onboard geostationary satellites have been extensively used for monitoring clouds and their evolution. The Advanced Baseline Imager (ABI) that will be launched onboard the Geostationary Operational Environmental Satellite-R (GOES-R) series in the near future will offer a larger range of spectral bands; hence, it will provide observations of cloud and rain systems at even finer spatial, temporal, and spectral resolutions than are possible with the current GOES. In this paper, a new method called Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks-Multispectral Analysis (PERSIANN-MSA) is proposed to evaluate the effect of using multispectral imagery on precipitation estimation. The proposed approach uses a self-organizing feature map (SOFM) to classify multidimensional input information, extracted from each grid box and corresponding textural features of multispectral bands. In addition, principal component analysis (PCA) is used to reduce the dimensionality to a few independent input features while preserving most of the variations of all input information. The above method is applied to estimate rainfall using multiple channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. In comparison to the use of a single thermal infrared channel, the analysis shows that using multispectral data has the potential to improve rain detection and estimation skills with an average of more than 50% gain in equitable threat score for rain/no-rain detection, and more than 20% gain in correlation coefficient associated with rain-rate estimation. © 2009 American Meteorological Society
Neural Natural Language Inference Models Enhanced with External Knowledge
Modeling natural language inference is a very challenging task. With the
availability of large annotated data, it has recently become feasible to train
complex models such as neural-network-based inference models, which have shown
to achieve the state-of-the-art performance. Although there exist relatively
large annotated data, can machines learn all knowledge needed to perform
natural language inference (NLI) from these data? If not, how can
neural-network-based NLI models benefit from external knowledge and how to
build NLI models to leverage it? In this paper, we enrich the state-of-the-art
neural natural language inference models with external knowledge. We
demonstrate that the proposed models improve neural NLI models to achieve the
state-of-the-art performance on the SNLI and MultiNLI datasets.Comment: Accepted by ACL 201
Observations of the Sunyaev-Zel'dovich effect at high angular resolution towards the galaxy clusters A665, A2163 and CL0016+16
We report on the first observation of the Sunyaev-Zel'dovich effect with the
Diabolo experiment at the IRAM 30 metre telescope. A significant brightness
decrement is detected in the direction of three clusters (Abell 665, Abell 2163
and CL0016+16). With a 30 arcsecond beam and 3 arcminute beamthrow, this is the
highest angular resolution observation to date of the SZ effect.Comment: 23 pages, 8 figures, 6 tables, accepted to New Astronom
The Effects of the Minimum Wage in the Private and Public Sectors in Brazil
The wage and employment effects of the minimum wage predicted by the standard neoclassical theory rely on a profit maximizing firm, not on a Government employer that can cover the higher wage bill by raising taxes, reducing expenditure, or simply printing money. If the public sector has an inelastic labour demand, the associated non-negative employment effect might offset some of the negative employment effect observed in the private sector and the overall employment effect might be less adverse. This is particularly so if the public sector is overpopulated by minimum wage workers, as in Brazil. There is very limited evidence on the minimum wage effects in developing countries, and none whatsoever on the minimum wage effects across the private and public sectors. This paper estimates the effects of the minimum wage on wages and employment in both the private and public sectors. The data used is an under-explored monthly Brazilian household survey from 1982 to 2000 at individual and regional levels. Robust results suggest that the minimum wage compresses the distribution of both sectors, but in line with a stronger effect in the private sector, more adverse employment effects in the long run are also observed in that sector. In the public sector, no evidence of adverse employment effects was uncovered.minimum wage; wage effect; employment effect; private sector; public sector; Brazil
Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation
Image correlation remote sensing monitoring techniques are becoming key tools for
providing effective qualitative and quantitative information suitable for natural hazard assessments,
specifically for landslide investigation and monitoring. In recent years, these techniques have
been successfully integrated and shown to be complementary and competitive with more standard
remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry.
The objective of this article is to apply the proposed in-depth calibration and validation analysis,
referred to as the Digital Image Correlation technique, to measure landslide displacement.
The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized
by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS
(Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models
and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide
displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the
landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive
sensitivity analyses and statistics-based processing approaches are used to identify the role of the
background noise that affects the whole dataset. This noise has a directly proportional relationship to
the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy
of the environmental-instrumental background noise evaluation allowed the actual displacement
measurements to be correctly calibrated and validated, thereby leading to a better definition of
the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability
(ranging from 1/10 to 8/10 pixel) for each processed dataset
The Distribution of Exchange Rate Volatility
Using high-frequency data on Deutschemark and Yen returns against the dollar, we construct model-free estimates of daily exchange rate volatility and correlation, covering an entire decade. In addition to being model-free, our estimates are also approximately free of measurement error under general conditions, which we delineate. Hence, for all practical purposes, we can treat the exchange rate volatilities and correlations as observed rather than latent. We do so, and we characterize their joint distribution, both unconditionally and conditionally. Noteworthy results include a simple normality-inducing volatility transformation, high contemporaneous correlation across volatilities, high correlation between correlation and volatilities, pronounced and highly persistent temporal variation in both volatilities and correlation, clear evidence of long-memory dynamics in both volatilities and correlation, and remarkably precise scaling laws under temporal aggregation.
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