The impact of liquidity on the financial growth of industrial companies listed on the Amman Stock Exchange

This research reveals the cash liquidity’s impact on the sustainability of the financial growth of the industrial public shareholding companies listed on the Amman Stock Exchange (ASE) between (2015) and 2021. The research population consists of (53) ASE-listed public shareholding industrial companies. A purposeful sample of (28) companies whose financial statements have provided all the data is selected as they meet the research variables during the research years. Using (SPSS), the appropriate statistical methods are utilized for data analysis. The findings indicate that cash flow with all its indicators; net cash flow from all activities to total assets, net cash flow from all activities to total equity, net cash flow from all activities to total profit, and net operating cash flow to total assets statistically and significantly impact the sustainability of profit growth, sales and assets of these companies. However, the net cash flow from all activities to total equity does not impact sales growth. Of the recommendations of this research is that the industrial companies incorporated in this research must pay more attention to the cash flow statement and rely on the data contained therein when making investment and financing decisions by raising efficiency in managing cash liquidity, and working to determine the suitable combination of equity and debt sources as the ideal use of this mixture may lead to a reduction in the cost of capital in these companies, which maximizes the elements of sustainable financial growth and paves the way for achieving more profitable investment opportunities available

Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity

While self-supervised learning techniques are often used to mining implicit knowledge from unlabeled data via modeling multiple views, it is unclear how to perform effective representation learning in a complex and inconsistent context. To this end, we propose a methodology, specifically consistency and complementarity network (CoCoNet), which avails of strict global inter-view consistency and local cross-view complementarity preserving regularization to comprehensively learn representations from multiple views. On the global stage, we reckon that the crucial knowledge is implicitly shared among views, and enhancing the encoder to capture such knowledge from data can improve the discriminability of the learned representations. Hence, preserving the global consistency of multiple views ensures the acquisition of common knowledge. CoCoNet aligns the probabilistic distribution of views by utilizing an efficient discrepancy metric measurement based on the generalized sliced Wasserstein distance. Lastly on the local stage, we propose a heuristic complementarity-factor, which joints cross-view discriminative knowledge, and it guides the encoders to learn not only view-wise discriminability but also cross-view complementary information. Theoretically, we provide the information-theoretical-based analyses of our proposed CoCoNet. Empirically, to investigate the improvement gains of our approach, we conduct adequate experimental validations, which demonstrate that CoCoNet outperforms the state-of-the-art self-supervised methods by a significant margin proves that such implicit consistency and complementarity preserving regularization can enhance the discriminability of latent representations.Comment: Accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE) 2022; Refer to https://ieeexplore.ieee.org/document/985763

Deep Learning for Financial Banking Stress Test Analytics

Since the recent financial crisis of late 2008, several global regulatory authorities have collaboratively mandated stress-testing exercises. These exercises evaluate the potential capital shortfalls & systemic impacts on large banks in hypothetical adverse economic scenarios, which try to simulate the macro-economic conditions similar to recent crisis'. The ability to relate dynamic economic conditions with banking performance profiles to identify meaningful relationships could provide significant insights for bank capital & loss projections. In this dissertation, the practical challenges that face bank stress-test analytics are examined and approached using advanced analytical techniques. Initially, (1) through a rigorous examination of an economic condition estimator (ECE), which learns joint approximation representations among exogenous factors by analyzing the complex non-linear relational combinations among the real-world economic indicators using a multi-modal conditioned variational auto-encoder (MCVAE). Experimentation on real-world economic conditions from the U.S. regulatory stress test exercise (CCAR) over the last three decades demonstrates the model's effectiveness. Additionally, (2) a focused study on bank capital & loss prediction (BCLP) methodology that can incorporate economic conditions as an estimated variable while also considering dynamic variability of potential crisis profiles that better provide a robust prediction of capital & loss. Demonstrations through experiments show that the BCLP model outperforms baseline & state-of-the-art methods from literature when evaluated on a sample of 1000 U.S. bank holding companies' historical consolidated financial statements (FR-9YC) from the past three decades. Both the ECE & BCLP model frameworks together form the Integrated Multi-modal Bank Stress Test Predictor (IMBSTP) framework to provide a data-driven end to end bank stress testing analytical tool. Lastly, (3) a preliminary overview of the Transferable Knowledge for the Bank Capital Components (TKBCC) model framework is discussed. The framework assumes that banks inherently share hidden intrinsic qualities and leverages inductive transfer learning techniques to improve bank capital-components predictions for domain tasks with limited training data. The performance of preliminary experiments on the proposed model framework through consolidated financial statements from the China Stock Market Accounting Research Database (CSMAR), and the Wharton Research and Data Service's (WRDS) repositories from the last two decades demonstrate the utility of the TKBCC model framework

Information Theory-Guided Heuristic Progressive Multi-View Coding

Multi-view representation learning captures comprehensive information from multiple views of a shared context. Recent works intuitively apply contrastive learning (CL) to learn representations, regarded as a pairwise manner, which is still scalable: view-specific noise is not filtered in learning view-shared representations; the fake negative pairs, where the negative terms are actually within the same class as the positive, and the real negative pairs are coequally treated; and evenly measuring the similarities between terms might interfere with optimization. Importantly, few works research the theoretical framework of generalized self-supervised multi-view learning, especially for more than two views. To this end, we rethink the existing multi-view learning paradigm from the information theoretical perspective and then propose a novel information theoretical framework for generalized multi-view learning. Guided by it, we build a multi-view coding method with a three-tier progressive architecture, namely Information theory-guided heuristic Progressive Multi-view Coding (IPMC). In the distribution-tier, IPMC aligns the distribution between views to reduce view-specific noise. In the set-tier, IPMC builds self-adjusted pools for contrasting, which utilizes a view filter to adaptively modify the pools. Lastly, in the instance-tier, we adopt a designed unified loss to learn discriminative representations and reduce the gradient interference. Theoretically and empirically, we demonstrate the superiority of IPMC over state-of-the-art methods.Comment: We have uploaded a new version of this paper in arXiv:2308.10522, so that we have to withdrawal this pape