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