4,976 research outputs found
A Framework for Monitoring Capital Flows in Hong Kong
In this paper we attempt to delineate conceptual issues relating to the definition of capital flows, and introduce a framework that organises survey data and accounting information at different time horizons to form a judgment on the nature and scale of fund flows in Hong Kong. Given the complexity of international financial transactions in Hong Kong, cross-border capital flows may not correspond closely to fund flows into and out of the Hong Kong dollar. A comprehensive view on the scale and nature of capital flows in Hong Kong requires the joint analysis of both monetary and Balance of Payments statistics, in addition to information gathered through market intelligence. We then apply the monitoring framework to analyse four episodes of large fund flows between 2003 and mid-2009.Capital flows; Fund flows; Hong Kong; Balance of Payments; External claims and liabilities of banks; Monetary Survey
Multi-Source Multi-View Clustering via Discrepancy Penalty
With the advance of technology, entities can be observed in multiple views.
Multiple views containing different types of features can be used for
clustering. Although multi-view clustering has been successfully applied in
many applications, the previous methods usually assume the complete instance
mapping between different views. In many real-world applications, information
can be gathered from multiple sources, while each source can contain multiple
views, which are more cohesive for learning. The views under the same source
are usually fully mapped, but they can be very heterogeneous. Moreover, the
mappings between different sources are usually incomplete and partially
observed, which makes it more difficult to integrate all the views across
different sources. In this paper, we propose MMC (Multi-source Multi-view
Clustering), which is a framework based on collective spectral clustering with
a discrepancy penalty across sources, to tackle these challenges. MMC has
several advantages compared with other existing methods. First, MMC can deal
with incomplete mapping between sources. Second, it considers the disagreements
between sources while treating views in the same source as a cohesive set.
Third, MMC also tries to infer the instance similarities across sources to
enhance the clustering performance. Extensive experiments conducted on
real-world data demonstrate the effectiveness of the proposed approach
Triadophilia: A Special Corner in the Landscape
It is well known that there are a great many apparently consistent vacua of
string theory. We draw attention to the fact that there appear to be very few
Calabi--Yau manifolds with the Hodge numbers h^{11} and h^{21} both small. Of
these, the case (h^{11}, h^{21})=(3,3) corresponds to a manifold on which a
three generation heterotic model has recently been constructed. We point out
also that there is a very close relation between this manifold and several
familiar manifolds including the `three-generation' manifolds with \chi=-6 that
were found by Tian and Yau, and by Schimmrigk, during early investigations. It
is an intriguing possibility that we may live in a naturally defined corner of
the landscape. The location of these three generation models with respect to a
corner of the landscape is so striking that we are led to consider the
possibility of transitions between heterotic vacua. The possibility of these
transitions, that we here refer to as transgressions, is an old idea that goes
back to Witten. Here we apply this idea to connect three generation vacua on
different Calabi-Yau manifolds.Comment: 41 pages 4 pdf figures, one is large. Improved discussion of the
Gross-Popescu manifolds, Figure 4 added, additions to Table 1 and other minor
correction
Learning from Multi-View Multi-Way Data via Structural Factorization Machines
Real-world relations among entities can often be observed and determined by
different perspectives/views. For example, the decision made by a user on
whether to adopt an item relies on multiple aspects such as the contextual
information of the decision, the item's attributes, the user's profile and the
reviews given by other users. Different views may exhibit multi-way
interactions among entities and provide complementary information. In this
paper, we introduce a multi-tensor-based approach that can preserve the
underlying structure of multi-view data in a generic predictive model.
Specifically, we propose structural factorization machines (SFMs) that learn
the common latent spaces shared by multi-view tensors and automatically adjust
the importance of each view in the predictive model. Furthermore, the
complexity of SFMs is linear in the number of parameters, which make SFMs
suitable to large-scale problems. Extensive experiments on real-world datasets
demonstrate that the proposed SFMs outperform several state-of-the-art methods
in terms of prediction accuracy and computational cost.Comment: 10 page
Online Unsupervised Multi-view Feature Selection
In the era of big data, it is becoming common to have data with multiple
modalities or coming from multiple sources, known as "multi-view data".
Multi-view data are usually unlabeled and come from high-dimensional spaces
(such as language vocabularies), unsupervised multi-view feature selection is
crucial to many applications. However, it is nontrivial due to the following
challenges. First, there are too many instances or the feature dimensionality
is too large. Thus, the data may not fit in memory. How to select useful
features with limited memory space? Second, how to select features from
streaming data and handles the concept drift? Third, how to leverage the
consistent and complementary information from different views to improve the
feature selection in the situation when the data are too big or come in as
streams? To the best of our knowledge, none of the previous works can solve all
the challenges simultaneously. In this paper, we propose an Online unsupervised
Multi-View Feature Selection, OMVFS, which deals with large-scale/streaming
multi-view data in an online fashion. OMVFS embeds unsupervised feature
selection into a clustering algorithm via NMF with sparse learning. It further
incorporates the graph regularization to preserve the local structure
information and help select discriminative features. Instead of storing all the
historical data, OMVFS processes the multi-view data chunk by chunk and
aggregates all the necessary information into several small matrices. By using
the buffering technique, the proposed OMVFS can reduce the computational and
storage cost while taking advantage of the structure information. Furthermore,
OMVFS can capture the concept drifts in the data streams. Extensive experiments
on four real-world datasets show the effectiveness and efficiency of the
proposed OMVFS method. More importantly, OMVFS is about 100 times faster than
the off-line methods
Derivative Usage and Firm Value : Evidence for Norwegian Non-financial Firms
Using derivative usage data from 185 firms listed on the Oslo Exchange during the 2007
to 2021 time period, we find a positive correlation between derivative usage and firm
value. However, the significance varies across derivative types and firm value quantile
distributions. The derivative instruments exhibit varying associations with firm values
that are mostly positive, though interest rate cap derivatives generally show negative
associations. Also, there are dynamic associations between derivative usage and firm value
over different time intervals. These results are robust to dynamic difference-in-difference
estimations, an econometric framework that reduces potential endogeneity problems and
explains causality. We conclude that derivative usage has, in general, a positive lagged
impact on firm value for Norwegian-listed firms that are exposed to the relevant risks.nhhma
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