Browsing by Author "Zhu J"

Now showing 1 - 4 of 4
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    Broker and institutional investor short selling
    (John Wiley and Sons Australia, Ltd on behalf of Accounting and Finance Association of Australia and New Zealand, 2024-09-20) Marshall BR; Nguyen NH; Visaltanachoti N; Zhu J
    Brokers have access to order-flow data, which they can use to enhance their short-selling returns. However, New Zealand brokers also have a fiduciary duty to place their clients' interests before their own. We compare the short-selling returns and trading behaviours of brokers and institutional investors who predominantly focus on profit-making. Our results show no significant return difference between broker and institutional short sales and indicate that broker short sales are apparently to stabilise the market. Short selling is associated with improved market quality, and this improvement is more pronounced when brokers short sell more than institutional investors.
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    Initialization-similarity clustering algorithm
    (Springer Science+Business Media, LLC, 2019-12) Liu T; Zhu J; Zhou J; Zhu Y; Zhu X
    Classic k-means clustering algorithm randomly selects centroids for initialization to possibly output unstable clustering results. Moreover, random initialization makes the clustering result hard to reproduce. Spectral clustering algorithm is a two-step strategy, which first generates a similarity matrix and then conducts eigenvalue decomposition on the Laplacian matrix of the similarity matrix to obtain the spectral representation. However, the goal of the first step in the spectral clustering algorithm does not guarantee the best clustering result. To address the above issues, this paper proposes an Initialization-Similarity (IS) algorithm which learns the similarity matrix and the new representation in a unified way and fixes initialization using the sum-of-norms regularization to make the clustering more robust. The experimental results on ten real-world benchmark datasets demonstrate that our IS clustering algorithm outperforms the comparison clustering algorithms in terms of three evaluation metrics for clustering algorithm including accuracy (ACC), normalized mutual information (NMI), and Purity.
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    Joint Spectral Clustering based on Optimal Graph and Feature Selection
    (Springer Nature Switzerland AG, 2021-02) Zhu J; Jang-Jaccard J; Liu T; Zhou J
    Redundant features and outliers (noise) included in the data points for a machine learning clustering model heavily influences the discovery of more distinguished features for clustering. To solve this issue, we propose a spectral new clustering method to consider the feature selection with the L2 , 1-norm regularization as well as simultaneously learns orthogonal representations for each sample to preserve the local structures of data points. Our model also solves the issue of out-of-sample, where the training process does not output an explicit model to predict unseen data points, along with providing an efficient optimization method for the proposed objective function. Experimental results showed that our method on twelve data sets achieves the best performance compared with other similar models.
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    Weighted adjacent matrix for K-means clustering
    (Springer Science+Business Media, LLC, 2019-12) Zhou J; Liu T; Zhu J
    K-means clustering is one of the most popular clustering algorithms and has been embedded in other clustering algorithms, e.g. the last step of spectral clustering. In this paper, we propose two techniques to improve previous k-means clustering algorithm by designing two different adjacent matrices. Extensive experiments on public UCI datasets showed the clustering results of our proposed algorithms significantly outperform three classical clustering algorithms in terms of different evaluation metrics.