Large Number Of Assets Research Articles

The Effect of Liquidity Ratios, Profitability Ratios, and Solvency to Value Ratio Company Studies on Sector Companies Consumer Goods Industry Listed on IDX Period 2018-2021

This study examines the effect of the company's liquidity, profitability, and solvency ratios on company value and company size is used as a control variable. Using the Purposive Sampling method with a total sample of 184 business entities in the consumer goods industry sector in Indonesia for the 2018-2021 period. The data analysis technique uses multiple linear regression analysis with the help of the SPSS 26 program. The findings showed that the liquidity ratio had no impact on the value of the company, while the ratio of profitability and solvency had a significant influence on the value of the company according to the hypothesis built in the study. The size of the company proved to significantly affect the value of the company. The implication of this research is that investors can use financial ratio analysis to make decisions before investing. Companies must also pay attention to their financial performance if they want to get investment funds. Large-scale companies usually have a large number of assets so that they can generate large profits, this is what can increase the value of the company.

VaR Estimation with Quantum Computing Noise Correction Using Neural Networks

In this paper, we present the development of a quantum computing method for calculating the value at risk (VaR) for a portfolio of assets managed by a finance institution. We extend the conventional Monte Carlo algorithm to calculate the VaR of an arbitrary number of assets by employing random variable algebra and Taylor series approximation. The resulting algorithm is suitable to be executed in real quantum computers. However, the noise affecting current quantum computers renders them almost useless for the task. We present a methodology to mitigate the noise impact by using neural networks to compensate for the noise effects. The system combines the output from a real quantum computer with the neural network processing. The feedback is used to fine tune the quantum circuits. The results show that this approach is useful for estimating the VaR in finance institutions, particularly when dealing with a large number of assets. We demonstrate the validity of the proposed method with up to 139 assets. The accuracy of the method is also proven. We achieved an error of less than 1% in the empirical measurements with respect to the parametric model.

Monitoring of Critical Undersea Infrastructures: The Nord Stream and Other Recent Case Studies

The explosions on September 26th, 2022, which damaged the gas pipelines of Nord Stream 1 and Nord Stream 2, have highlighted the need and urgency of improving the re- silience of Critical Undersea Infrastructures (CUIs). Comprising gas pipelines and power and communication cables, these con- nect countries worldwide and are critical for the global economy and stability. An attack targeting multiple of such infrastruc- tures simultaneously could potentially cause significant damage and greatly affect various aspects of daily life. Due to the in- creasing number and continuous deployment of CUIs, existing underwater surveillance solutions, such as Autonomous Under- water Vehicles (AUVs) or Remotely Operated Vehicles (ROVs), are not adequate enough to ensure thorough monitoring.We show that the combination of information from both un- derwater and above-water surveillance sensors enables achieving Seabed-to-Space Situational Awareness (S3A), mainly thanks to Artificial Intelligence (AI) and Information Fusion (IF) method- ologies. These are designed to process immense volumes of infor- mation, fused from a variety of sources and generated from mon- itoring a very large number of assets on a daily basis. The learned knowledge can be used to anticipate future behaviors, identify threats, and determine critical situations concerning CUIs.To illustrate the capabilities and importance of S3A, we con- sider three events that occurred in the second half of 2022: the aforementioned Nord Stream explosions, the cutoff of the under- water communication cable SHEFA-2 connecting the Shetland Islands and the UK mainland, and the suspicious activity of a large vessel in the Adriatic Sea. Specifically, we provide analy- ses of the available data, from Automatic Identification System (AIS) and satellite data, integrated with possible contextual in- formation, e.g., bathymetry, Patterns Of Life (POLs), weather conditions, and human intelligence (HUMINT).

Adaptive Testing for Alphas in High-Dimensional Factor Pricing Models

This article proposes a new procedure to validate the multi-factor pricing theory by testing the presence of alpha in linear factor pricing models with a large number of assets. Because the market’s inefficient pricing is likely to occur to a small fraction of exceptional assets, we develop a testing procedure that is particularly powerful against sparse signals. Based on the high-dimensional Gaussian approximation theory, we propose a simulation-based approach to approximate the limiting null distribution of the test. Our numerical studies show that the new procedure can deliver a reasonable size and achieve substantial power improvement compared to the existing tests under sparse alternatives, and especially for weak signals.

Evaluation of the Implementation of the Fixed Asset Accounting Security Procedure System in the Regional Government of Bogor Regency

The Bogor Regency regional government must present detailed financial reports describing asset values, regulated in Government Accounting Standard Regulation No. 7 of 2010. On the other hand, the problem in regional asset management is the lack of orderly administration and legal order which has resulted in regional assets, especially regional fixed assets, not being able to be properly and accurately inventoried, the lack of previous technology, which is still limited makes some of the data owned by the Government Regions are not valid, coupled with Human Resources (HR) which are still very limited. The research aims to examine the implementation of the Fixed Assets Accounting Systems and Procedures and the safeguarding of fixed assets that the Government of Bogor Regency has implemented. The research method used is descriptive qualitative, which is a method that describes the problems or cases raised based on existing facts based on specific facts and then examines them to solve problems and draw general conclusions. The Bogor Regency Government 2013 launched the Regional Goods Cycle Information Technology Application (ATISISBADA), which implements Fixed Assets Accounting Systems and Procedures and safeguards fixed assets. The technology-based fixed asset information system assists administration or online administrative/inventory recording that coordinates the Management of Regional Property in Bogor Regency, which has 40 Districts and 38 SKPDs or Regional Apparatuses with a large number of assets in the Bogor Regency Government with a considerable value. The conclusion of the research on securing regional property on fixed assets in Bogor Regency is good regarding procedures or implementation of regional property management, especially on physical, administrative, and legal security.

A novel approach to denoising correlation matrices with applications to global portfolio management with a large number of assets

We introduce a new approach to denoising correlation matrices that imposes a block structure with a fixed block-dependent pair-wise correlation within each block and a constant correlation specified for each pair of blocks. We characterize the eigenvalue spectrum and modify the Marchenko-Pastur distribution of eigenvalues. We present approximate analytic solutions for the inverse problem of determining the block sizes and the correlation parameters under a broad set of assumptions. Our solution is based on a novel unsupervised approach for improving correlation matrix estimation by a functional transformation of the original data rather than popular shrinkage estimation or standard random-matrix-theory-based techniques. However, a correlation matrix produced by our method can serve as an attractive target correlation matrix within shrinkage frameworks. Our correlation matrix denoising method has broad applications for global portfolio management with a large number of assets from many diverse asset classes.

High-Dimensional Distributionally Robust Mean-Variance Efficient Portfolio Selection

This paper introduces a novel distributionally robust mean-variance portfolio estimator based on the projection robust Wasserstein (PRW) distance. This approach addresses the issue of increasing conservatism of portfolio allocation strategies due to high-dimensional data. Our simulation results show the robustness of the PRW-based estimator in the presence of noisy data and its ability to achieve a higher Sharpe ratio than regular Wasserstein distances when dealing with a large number of assets. Our empirical study also demonstrates that the proposed portfolio estimator outperforms classic “plug-in” methods using various covariance estimators in terms of risk when evaluated out of sample.

Robust sparse portfolios for index tracking based on M-estimation

In order to save transaction costs, a sparse portfolio that consists of a small subset of the constituent stocks is commonly requested especially when the target index has a large number of assets. To the best of our knowledge, a large body of literature on sparse index tracking are mainly focus on the penalized least squares estimation under the no-short selling constraint. Accordingly, these procedures are highly sensitive to outliers and their efficiency may be significantly decreased when the noise distribution is unknown and possibly heavy-tailed. As promising alternatives, robust loss functions such as L 1 loss and Huber’s loss have been well characterized in penalized M-estimation techniques. This paper considers robust sparse portfolios to simultaneously perform stock selection and capital allocation for high-dimensional index tracking. Through the simulation, we show that newly proposed approach is resistant to heavy-tailed errors or outliers in the response. Finally, we illustrate the superior performance of the proposed method through tracking the Standard & Poor’s 500 Index.

A legal approach to the national emergency management of space weather: China as a case study

The attention of the international space community has been drawn to space weather over the last two decades, not only because of its uncertainty and its adverse effects on the safety of space operations and the long-term sustainability of outer space activities (LTSOSA), but also because of its negative societal and economic impacts on the ground. The national emergency management of space weather is indispensable for China because China has both a large number of assets in outer space and infrastructure on the ground that are vulnerable to space weather. While China gives weight to the monitoring of space weather events by deploying a series of space- and ground-based systems, its national-level management of space weather events is inadequate. China, compared to the USA, is still a long way from having scientific and effective emergency management of severe space weather in the areas of space weather science research, response, and rulemaking. Current international hard laws that address the space weather issue play a limited role because these rules are all general. The Guidelines for the Long-term Sustainability of Outer Space Activities of the Committee on the Peaceful Uses of Outer Space (the UNCOPUOS Guidelines for the LTSOSA), although they provide soft-law guidance addressing the harmful impacts of space weather on space activities from the perspectives of mitigation, preparedness, monitoring, and early warning, neither address the response to or recovery from extreme space weather events in outer space nor provide for space weather mitigation, preparedness, response, and recovery regimes on the ground. Apart from the international law and the soft-law guidance, Chinese national laws only provide general guidelines for the emergency management of space weather. To enhance its national capacity to address space weather and minimize the harmful societal and economic impacts of space weather, a comprehensive legal regime for the emergency management of space weather should be in place in China; this should primarily revolve around mitigation, preparedness, monitoring, early warning, response, recovery, capacity-building, and international cooperation.

Pengaruh Ratio Keuangan Terhadap Kinerja Keuangan Pada Perusahaan Sub Sektor Makanan

The purpose of this study is to see the effect of financial ratios on financial performance in food sub-sector companies listed on the Indonesia Stock Exchange both partially and simultaneously. Earnings growth is a measure that compares how much increase in income receipts in the current period with earnings in the current period. then. The greater the opportunity to grow the company, the higher the opportunity for the company to increase its profits. Companies that experience an increase in profits can strengthen the relationship between company size and the level of profit earned where companies that experience an increase in profits can have a larger number of assets so that they have more opportunities to generate profits in the future. The profit growth experienced by a company does not always increase every year, sometimes it will decrease in the following year. The population in this study has all the food sub-sector companies listed on the Indonesia Stock Exchange for the 2016-2020 period, as many as 26 companies and the sample with a total of 65 units of analysis. The research method applies multiple linear regression analysis techniques. The result of this research is that liquidity and activity variables partially have no effect on profit growth in food sub-sector companies listed on the Indonesia Stock Exchange. Leverage and profitability variables partially affect profit growth in food sub-sector companies listed on the Indonesia Stock Exchange. The variables of liquidity, leverage, profitability and activity together affect profit growth in food sub-sector companies listed on the Indonesia Stock Exchange.

Sparsity and stability for minimum-variance portfolios

The popularity of modern portfolio theory has decreased among practitioners because of its unfavorable out-of-sample performance. Estimation risk tends to affect the optimal weight calculation noticeably, especially when a large number of assets are considered. To overcome these issues, many methods have been proposed in recent years, but only a few address practically relevant questions related to portfolio allocation. This study therefore uses different covariance estimation techniques, combines them with sparse model approaches, and includes a turnover constraint that induces stability. We use two datasets of the S&P 500 to create a realistic data foundation for our empirical study. We discover that it is possible to maintain the low-risk profile of efficient estimation methods while automatically selecting only a subset of assets and further inducing low portfolio turnover. Moreover, we find that simply using LASSO is insufficient to lower turnover when the model’s tuning parameter can change over time.

Next generation models for portfolio risk management: An approach using financial big data

AbstractThis paper proposes a dynamic process of portfolio risk measurement to address potential information loss. The proposed model takes advantage of financial big data to incorporate out‐of‐target‐portfolio information that may be missed when one considers the value at risk (VaR) measures only from certain assets of the portfolio. We investigate how the curse of dimensionality can be overcome in the use of financial big data and discuss where and when benefits occur from a large number of assets. In this regard, the proposed approach is the first to suggest the use of financial big data to improve the accuracy of risk analysis. We compare the proposed model with benchmark approaches and empirically show that the use of financial big data improves small portfolio risk analysis. Our findings are useful for portfolio managers and financial regulators, who may seek for an innovation to improve the accuracy of portfolio risk estimation.

A Novel Approach to Denoising Correlation Matrices With Applications to Global Portfolio Management With a Large Number of Assets

A Novel Approach to Denoising Correlation Matrices With Applications to Global Portfolio Management With a Large Number of Assets

Asset Allocation via Machine Learning

In this paper, we document a novel machine learning-based numerical framework to solve static and dynamic portfolio optimization problems, with, potentially, an extremely large number of assets. The framework proposed applies to general constrained optimization problems and overcomes many major difficulties arising in current literature. We not only empirically test our methods in U.S. and China A-share equity markets, but also run a horse-race comparison of some optimization schemes documented in (Homescu, 2014). We record significant excess returns, relative to the selected benchmarks, in both U.S. and China equity markets using popular schemes solved by our framework, where the conditional expected returns are obtained via machine learning regression, inspired by (Gu, Kelly & Xiu, 2020) and (Leippold, Wang & Zhou, 2021), of future returns on pricing factors carefully chosen.

Testing for Regime Changes in Portfolios with a Large Number of Assets: A Robust Approach to Factor Heteroskedasticity

abstractWe develop a new test for threshold-type regime changes in the risk exposures in portfolios with a large number of financial assets whose returns exhibit an approximate factor structure. Unlike existing procedures to detect discrete shifts in factor models, our test is robust to regime-specific second moment of the common factors. We rely on an auxiliary threshold regression: we take a weighted cross-sectional average of the cross-sectional units; we estimate the factors from the original model under the null hypothesis of no regime changes; we construct a Lagrange multiplier statistic to test for threshold effect in the auxiliary regression. Numerical results show the good finite sample properties of our procedure. The empirical analysis uncovers the dynamics of portfolio weights and diversification benefits in factor mimicking portfolios across different regimes.

Artificial intelligence in portfolio formation and forecast: Using different variance-covariance matrices

Portfolio optimization relies in three main elements: the utility function, the use of multivariate dependence measure between assets and the returns of the assets. The first problem is well known in finance since one can use several different types of portfolio utility functions depending on her objective function and constraints. The two remaining problems are relevant topics of research: the measure and forecast of the dependence between assets and the forecast of the returns of assets. In this paper, we present a new method with artificial intelligence to estimate the variance and covariance matrices of financial time series data accounting for small sample size, large number of assets and presences of outliers. We compare results using different approaches, e.g., Minimum covariance determinant (MCD), Minimum Volume Ellipsoid (MVE), Minimum Regularized Covariance Determinant (MRCD) and Orthogonalized Gnanadesikan-Kettenring (OGK). The proposed approach is robust under several financial stylized facts and presents good performance with respect to the cumulative returns in the US markets (NASDAQ Stock Exchange) for 95 assets during 2015 to the beginning of 2019. These study contributes to fill in the literature gap in covariance estimation for small sample size, large number of assets and presences of outliers and contributes to investors to making better portfolio management.

Optimal portfolio deleveraging under market impact and margin restrictions

We consider the problem of optimally deleveraging a high net-worth long-short portfolio in a short time period to position the fund favorably with respect to leverage and margin risks, in the face of an adverse outlook on future uncertainty. We develop a generalized mean-variance deleveraging optimization model that accounts for market impact costs in portfolio trading under market illiquidity. Due to asset price impact stemming from both volume and intensity of trading, the model has significant non-convexities. For portfolios with a large number of assets, the model is not solvable using standard software, and thus, we employ an efficient solution scheme based on dual optimization, along with a sequence of progressively-improving feasible portfolios computed under convex approximation. Our computational analysis using real data on ETF assets provides new insights on performance sensitivity. In particular, ignoring market impact severely downgrades portfolio performance depending on leverage and margin policies, as well as market liquidity conditions. Such insights can guide portfolio managers in setting deleveraging policy parameters ex-ante when faced with potential market turbulence. We also test the solution algorithm using random problem instances under thousands of assets to demonstrate the scalability and solvability of the deleveraging model.

Cost gap, Shapley, or nucleolus allocation: Which is the best game‐theoretic remedy for the low‐risk anomaly?

Empirical research in investment management has discovered the puzzling phenomenon that, contrary to established capital market theory, low‐risk assets tend to earn larger returns than their high‐risk counterparts. In a recent contribution, Auer and Hiller (2019) emphasize that an inadequate quantification of risk may be the root of this problem. By interpreting a portfolio as a cooperative game, they arrive at the interesting finding that using assets' risk‐based Shapley values instead of classic stand‐alone risk measures has the potential to solve the low‐risk puzzle. In this article, we extend their study first by considering additional game‐theoretic risk allocation schemes, namely, the cost gap technique and the nucleolus method, and then by improving their simulation design via a larger number of assets and a supplementary determination of risk–return slopes. We find that the Shapley method outperforms the alternatives with respect to the generation of positive risk–return slopes.

Dynamic Correlation Multivariate Stochastic Volatility Black-Litterman With Latent Factors

In finance, it is often of interest to study market volatility for portfolios that may consist of a large number of assets using multivariate stochastic volatility models. However, such models, though useful, do not usually incorporate investor views that might be available. In this paper we introduce a novel hierarchical Bayesian methodology of modeling volatility for a large portfolio of assets that incorporates investor’s personal views of the market via the Black-Litterman (BL) model. We extend the scope and use of BL models by using it within a multivariate stochastic volatility model based on latent factors for dimensionality reduction but allows for time varying correlations. Detailed derivations of MCMC algorithm are provided with an illustration with S&P500 asset returns. Moreover, sensitivity analysis for the confidence levels that the investor has in their personal views is also explored. Numerical results show that the proposed method provides flexible interpretation based on the investor’s uncertainty in personal beliefs, and converges to the empirical sample estimate when their confidence level of the market becomes weak.

Incorporating Financial Big Data in Small Portfolio Risk Analysis: Market Risk Management Approach

When applying Value at Risk (VaR) procedures to specific positions or portfolios, we often focus on developing procedures only for the specific assets in the portfolio. However, since this small portfolio risk analysis ignores information from assets outside the target portfolio, there may be significant information loss. In this paper, we develop a dynamic process to incorporate the ignored information. We also study how to overcome the curse of dimensionality and discuss where and when benefits occur from a large number of assets, which is called the blessing of dimensionality. We find empirical support for the proposed method.