8,924 research outputs found
Chandra survey in the AKARI North Ecliptic Pole Deep Field. I. X-ray data, point-like source catalog, sensitivity maps, and number counts
We present data products from the 300 ks Chandra survey in the AKARI North
Ecliptic Pole (NEP) deep field. This field has a unique set of 9-band infrared
photometry covering 2-24 micron from the AKARI Infrared Camera, including
mid-infrared (MIR) bands not covered by Spitzer. The survey is one of the
deepest ever achieved at ~15 micron, and is by far the widest among those with
similar depths in the MIR. This makes this field unique for the MIR-selection
of AGN at z~1. We design a source detection procedure, which performs joint
Maximum Likelihood PSF fits on all of our 15 mosaicked Chandra pointings
covering an area of 0.34 square degree. The procedure has been highly optimized
and tested by simulations. We provide a point source catalog with photometry
and Bayesian-based 90 per cent confidence upper limits in the 0.5-7, 0.5-2,
2-7, 2-4, and 4-7 keV bands. The catalog contains 457 X-ray sources and the
spurious fraction is estimated to be ~1.7 per cent. Sensitivity and 90 per cent
confidence upper flux limits maps in all bands are provided as well. We search
for optical MIR counterparts in the central 0.25 square degree, where deep
Subaru Suprime-Cam multiband images exist. Among the 377 X-ray sources detected
there, ~80 per cent have optical counterparts and ~60 per cent also have AKARI
mid-IR counterparts. We cross-match our X-ray sources with MIR-selected AGN
from Hanami et al. (2012). Around 30 per cent of all AGN that have MID-IR SEDs
purely explainable by AGN activity are strong Compton-thick AGN candidates.Comment: 23 pages, 20 figures; catalogs, sensitivity maps, and upper limit
flux maps are available from the VizieR Servic
Adaptive Investment Strategies For Periodic Environments
In this paper, we present an adaptive investment strategy for environments
with periodic returns on investment. In our approach, we consider an investment
model where the agent decides at every time step the proportion of wealth to
invest in a risky asset, keeping the rest of the budget in a risk-free asset.
Every investment is evaluated in the market via a stylized return on investment
function (RoI), which is modeled by a stochastic process with unknown
periodicities and levels of noise. For comparison reasons, we present two
reference strategies which represent the case of agents with zero-knowledge and
complete-knowledge of the dynamics of the returns. We consider also an
investment strategy based on technical analysis to forecast the next return by
fitting a trend line to previous received returns. To account for the
performance of the different strategies, we perform some computer experiments
to calculate the average budget that can be obtained with them over a certain
number of time steps. To assure for fair comparisons, we first tune the
parameters of each strategy. Afterwards, we compare the performance of these
strategies for RoIs with different periodicities and levels of noise.Comment: Paper submitted to Advances in Complex Systems (November, 2007) 22
pages, 9 figure
Collective behavior of stock price movements in an emerging market
To investigate the universality of the structure of interactions in different
markets, we analyze the cross-correlation matrix C of stock price fluctuations
in the National Stock Exchange (NSE) of India. We find that this emerging
market exhibits strong correlations in the movement of stock prices compared to
developed markets, such as the New York Stock Exchange (NYSE). This is shown to
be due to the dominant influence of a common market mode on the stock prices.
By comparison, interactions between related stocks, e.g., those belonging to
the same business sector, are much weaker. This lack of distinct sector
identity in emerging markets is explicitly shown by reconstructing the network
of mutually interacting stocks. Spectral analysis of C for NSE reveals that,
the few largest eigenvalues deviate from the bulk of the spectrum predicted by
random matrix theory, but they are far fewer in number compared to, e.g., NYSE.
We show this to be due to the relative weakness of intra-sector interactions
between stocks, compared to the market mode, by modeling stock price dynamics
with a two-factor model. Our results suggest that the emergence of an internal
structure comprising multiple groups of strongly coupled components is a
signature of market development.Comment: 10 pages, 10 figure
Portfolio Optimization and the Random Magnet Problem
Diversification of an investment into independently fluctuating assets
reduces its risk. In reality, movement of assets are are mutually correlated
and therefore knowledge of cross--correlations among asset price movements are
of great importance. Our results support the possibility that the problem of
finding an investment in stocks which exposes invested funds to a minimum level
of risk is analogous to the problem of finding the magnetization of a random
magnet. The interactions for this ``random magnet problem'' are given by the
cross-correlation matrix {\bf \sf C} of stock returns. We find that random
matrix theory allows us to make an estimate for {\bf \sf C} which outperforms
the standard estimate in terms of constructing an investment which carries a
minimum level of risk.Comment: 12 pages, 4 figures, revte
Noise Dressing of Financial Correlation Matrices
We show that results from the theory of random matrices are potentially of
great interest to understand the statistical structure of the empirical
correlation matrices appearing in the study of price fluctuations. The central
result of the present study is the remarkable agreement between the theoretical
prediction (based on the assumption that the correlation matrix is random) and
empirical data concerning the density of eigenvalues associated to the time
series of the different stocks of the S&P500 (or other major markets). In
particular the present study raises serious doubts on the blind use of
empirical correlation matrices for risk management.Comment: Latex (Revtex) 3 pp + 2 postscript figures (in-text
Managing Risk of Bidding in Display Advertising
In this paper, we deal with the uncertainty of bidding for display
advertising. Similar to the financial market trading, real-time bidding (RTB)
based display advertising employs an auction mechanism to automate the
impression level media buying; and running a campaign is no different than an
investment of acquiring new customers in return for obtaining additional
converted sales. Thus, how to optimally bid on an ad impression to drive the
profit and return-on-investment becomes essential. However, the large
randomness of the user behaviors and the cost uncertainty caused by the auction
competition may result in a significant risk from the campaign performance
estimation. In this paper, we explicitly model the uncertainty of user
click-through rate estimation and auction competition to capture the risk. We
borrow an idea from finance and derive the value at risk for each ad display
opportunity. Our formulation results in two risk-aware bidding strategies that
penalize risky ad impressions and focus more on the ones with higher expected
return and lower risk. The empirical study on real-world data demonstrates the
effectiveness of our proposed risk-aware bidding strategies: yielding profit
gains of 15.4% in offline experiments and up to 17.5% in an online A/B test on
a commercial RTB platform over the widely applied bidding strategies
Random Matrix Theory Analysis of Cross Correlations in Financial Markets
We confirm universal behaviors such as eigenvalue distribution and spacings
predicted by Random Matrix Theory (RMT) for the cross correlation matrix of the
daily stock prices of Tokyo Stock Exchange from 1993 to 2001, which have been
reported for New York Stock Exchange in previous studies. It is shown that the
random part of the eigenvalue distribution of the cross correlation matrix is
stable even when deterministic correlations are present. Some deviations in the
small eigenvalue statistics outside the bounds of the universality class of RMT
are not completely explained with the deterministic correlations as proposed in
previous studies. We study the effect of randomness on deterministic
correlations and find that randomness causes a repulsion between deterministic
eigenvalues and the random eigenvalues. This is interpreted as a reminiscent of
``level repulsion'' in RMT and explains some deviations from the previous
studies observed in the market data. We also study correlated groups of issues
in these markets and propose a refined method to identify correlated groups
based on RMT. Some characteristic differences between properties of Tokyo Stock
Exchange and New York Stock Exchange are found.Comment: RevTex, 17 pages, 8 figure
Learning a Factor Model via Regularized PCA
We consider the problem of learning a linear factor model. We propose a
regularized form of principal component analysis (PCA) and demonstrate through
experiments with synthetic and real data the superiority of resulting estimates
to those produced by pre-existing factor analysis approaches. We also establish
theoretical results that explain how our algorithm corrects the biases induced
by conventional approaches. An important feature of our algorithm is that its
computational requirements are similar to those of PCA, which enjoys wide use
in large part due to its efficiency
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