This article presents results from the first statistically significant study of causes of cost escalation in transport infrastructure projects. The study is based on a sample of 258 rail, bridge, tunnel and road projects worth US$90 billion. The focus is on the dependence of cost escalation on (1) length of project imp…
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48 results for “private ownership”
The sustainability conditions for the market participants with a different ownership model were also determined. It was revealed, that the nonlinear form of the equations describing the market behavior with the prevailing private capital, predetermines the development of such a market according to the subharmonic casca…
Bangladesh's banking sector improved through financial reforms, but challenges remain.
problem Weak asset quality, inadequate provisioning, and negative capitalization of state-owned banks.
method Two phases of reforms: private ownership promotion and gradual deregulation.
result Significant improvements in asset quality and capitalization, but challenges persist.
The study finds variations in ownership structure and efficiency across sectors in Malaysia.
problem Investigate variations in ownership structure and firm efficiency across sectors in Malaysia.
method Frequency distributions of ownership structure, DEA under CRS and VRS, stratified random sampling.
result There are variations in firm ownership structure and efficiency across sectors in Malaysia.
Study shows foreign institutional investment increases liquidity commonality in large Australian stocks.
problem Impact of foreign institutional investment on liquidity commonality in Australian stocks.
method Cross-sectional and time-series analysis of Australian equity market data.
result Foreign institutional investment contributes to increased exposure of large stocks to unexpected liquidity events.
Cross-Ownership as a Structural Explanation for Over- and Underestimation of Default Probabilityq-fin.RM
Based on the work of Suzuki (2002), we consider a generalization of Merton's asset valuation approach (Merton, 1974) in which two firms are linked by cross-ownership of equity and liabilities. Suzuki's results then provide no arbitrage prices of firm values, which are derivatives of exogenous asset values. In contrast …
Ownership Cost Calculations for Distributed Energy Resources Using Uncertainty and Risk Analysesecon.GN
Ownership cost calculation plays an important role in optimal operation of distributed energy resources (DERs) and microgrids (MGs) in the future power system, known as smart grid. In this paper, a general framework for ownership cost calculation is proposed using uncertainty and risk analyses. Four ownership cost calc…
We generalize Merton's asset valuation approach to systems of multiple financial firms where cross-ownership of equities and liabilities is present. The liabilities, which may include debts and derivatives, can be of differing seniority. We derive equations for the prices of equities and recovery claims under no-arbitr…
Study simulates liquidity in fractional ownership markets using ABM.
problem Understanding liquidity dynamics in illiquid markets.
method Agent-based modeling (ABM) with empirical data.
result Simulation reveals insights into market structures and trading behaviors.
NTL protects AI models by restricting their generalization ability to specific domains.
problem Protecting AI models as intellectual property in a secure and robust manner.
method Non-Transferable Learning (NTL) captures exclusive data representation and restricts model generalization ability.
result NTL provides robust resistance to watermark removal and data-centric protection for usage authorization.
Data taggants verify dataset ownership without harming models or requiring model internals.
problem Verifying dataset ownership without harming models or requiring model internals.
method Using pairs of out-of-distribution samples and random labels as secret keys, subtly altering a dataset to detect models trained on it.
result Data taggants reliably detect models trained on the protected dataset with high confidence, without compromising validation accuracy.
The price of a given stock is exactly known only at the time of sale when the stock is between the traders. If we know the price (owner) then we have no information on the owner (price). A more general description including cases when we have partial information on both price and ownership is obtained by using the quan…
Uncovering Offshore Financial Centers: Conduits and Sinks in the Global Corporate Ownership Networkphysics.soc-ph
Multinational corporations use highly complex structures of parents and subsidiaries to organize their operations and ownership. Offshore Financial Centers (OFCs) facilitate these structures through low taxation and lenient regulation, but are increasingly under scrutiny, for instance for enabling tax avoidance. Theref…
The paper shows how cross-ownership increases equity correlations during financial crises.
problem Understanding and explaining rising correlations in financial markets during crises.
method Examined interlinkages among firms through a financial network, mathematically relating equity correlations to asset correlations and network sensitivity.
result Equity correlations are higher than asset correlations, and this relationship is independent of the equities level.
Tokenized RWAs face liquidity issues despite promising markets.
problem Low trading volumes and limited investor participation in tokenized assets.
method Empirical analysis of tokenized real estate, private credit, and treasury funds.
result Most tokenized assets exhibit low transfer activity and limited secondary trading.
This paper uses graph neural networks to predict SME default risk using transaction and ownership networks.
problem Predicting credit risk for SMEs facing limited financial histories and collateral constraints.
method Graph Neural Networks applied to multilayer network data of SME transactions and ownership.
result Combining network data with traditional data improves credit scoring and models contagion risk.
NFTs revolutionize art sales by providing proof of ownership.
problem Lack of provenance and authenticity in digital art.
method Analysis of major art NFT marketplaces.
result NFTs reduce the need for intermediaries in the art trade.
In this paper, we consider a dynamic asset pricing model in an approximate fractional economy to address empirical regularities related to both investor protection and past information. Our newly developed model features not only in terms with a controlling shareholder who diverts a fraction of the output, but also goo…
Paper introduces proof-of-learning to verify ML model training.
problem No mechanism to prove ML model training parameters were obtained through optimization.
method Inspired by proof-of-work and verified computations, introduces proof-of-learning mechanism.
result Proves model training parameters were obtained through optimization with minimal adversary work.
The commercialization of deep learning creates a compelling need for intellectual property (IP) protection. Deep neural network (DNN) watermarking has been proposed as a promising tool to help model owners prove ownership and fight piracy. A popular approach of watermarking is to train a DNN to recognize images with ce…
We introduce a model for the adaptive evolution of a network of company ownerships. In a recent work it has been shown that the empirical global network of corporate control is marked by a central, tightly connected "core" made of a small number of large companies which control a significant part of the global economy.…
Unified theory of ownership concentration, overlap, and dependence.
problem Understanding the complex layers of ownership concentration, overlap, and dependence in financial markets.
method Develops a unified quadratic framework for analyzing these layers and their interactions.
result Unified framework shows that the same residual operator measures static overlap and governs linearized market transmission.
Study clusters Indian stocks using polyspectral means for nuanced market insights.
problem Analyzing temporal patterns and financial relationships in Indian stock market.
method k-means clustering algorithm applied to polyspectral means of stock data.
result Identified five distinctive clusters of stocks with varying ownership structures.
Investors trade based on shifting prices, leading to market inefficiencies.
problem Market inefficiencies due to trading behavior and financial dynamics.
method Analyzes trading strategies and market dynamics.
result Arbitrage opportunities arise from concentrated ownership and low collateral requirements.
For the convenience of readers of the article {\em No-arbitrage pricing under systemic risk: accounting for cross-ownership} (Fischer, 2012, arXiv:1005.0768), a full proof of Lemma A.5 and a shorter proof of Lemma A.6 of that paper are provided.
Paper introduces a new edge exchangeable block model for complex networks.
problem Limitations of the stochastic block model in analyzing complex networks.
method Develops a Bayesian nonparametric edge exchangeable block model.
result The new model outperforms state-of-the-art SBMs for link prediction.
A simple quantitative example of a reflexive feedback process and the resulting price dynamics after an exogenous price shock to a financial network is presented. Furthermore, an outline of a theory that connects financial reflexivity, which stems from cross-ownership and delayed or incomplete information, and no-arbit…
This paper studies trade-offs in private prediction methods.
problem Leakage of training data information in machine learning predictions.
method Private training and private prediction methods with trade-offs.
result Private training methods outperform private prediction methods in various settings.
Private method measures nonlinear correlations between data hosted across two entities.
problem Measuring nonlinear correlations between sensitive data hosted across multiple parties while preserving privacy.
method Differentially private estimator of distance correlation.
result First private estimator of nonlinear correlations in a multi-party setup.
Study uses synthetic data to estimate credit risk for underbanked consumers in Istanbul.
problem Estimating credit risk for underbanked consumers lacking formal credit records.
method Created synthetic dataset, used retrieval augmented generation, trained CatBoost, LightGBM, and XGBoost models.
result Alternative financial data improves credit risk estimation, raising AUC by 13%.
Study shows how business cycle affects dividend payout based on managerial stock incentives.
problem Impact of managerial stock incentives on dividend payout policy during business cycles.
method Using S&P 1500 companies data from 2000-2018, analyzing full sample and recession periods.
result Negative relationship between managerial stock options and dividend payouts, significant for medium-sized companies.
New algorithms for privately learning decision lists and halfspaces.
problem Private learning of decision lists and halfspaces.
method Differentially private algorithms for PAC and online models.
result Private algorithms match or surpass non-private guarantees.
Private PGB boosts synthetic data quality using GANs and privacy techniques.
problem Differentially private GANs struggle with convergence and poor output quality.
method Combines reweighted samples from GAN training using Private Multiplicative Weights method.
result Improves synthetic data quality across various datasets and tasks.
Near-optimal private tests for simple and MLR hypotheses developed under Gaussian differential privacy.
problem Developing private tests for simple and MLR hypotheses under Gaussian differential privacy.
method A private mean estimator with data-driven clamping bounds, constructing private test statistics.
result Private tests achieve the same asymptotic relative efficiency as non-private most powerful tests.
PEARL uses AI to replicate private equity performance with liquid assets.
problem Lack of access to private equity due to high costs and complexity.
method Combines AI with liquid assets, incorporating asymmetry for better performance.
result Model outperforms liquid proxies and aligns with private equity benchmarks.
We present a provably optimal differentially private algorithm for the stochastic multi-arm bandit problem, as opposed to the private analogue of the UCB-algorithm [Mishra and Thakurta, 2015; Tossou and Dimitrakakis, 2016] which doesn't meet the recently discovered lower-bound of [Shar…
On the Benefits of Public Representations for Private Transfer Learning under Distribution Shiftcs.LG
Public pretraining improves private model training even in extreme distribution shift scenarios.
problem Improving private model training accuracy in settings with large distribution shift.
method Empirical evaluation and theoretical explanation of public representations improving private training accuracy.
result Public representations can improve private training accuracy by up to 67% over private training from scratch in settings with large distribution shift.
Private learning of Gaussian Mixture Models without boundedness assumptions.
problem Private estimation of parameters of Gaussian Mixture Models with unbounded components.
method Reduction to non-private problem, blackbox privatization, Moitra and Valiant's algorithm.
result First sample complexity upper bound and polynomial time algorithm for privately learning GMMs.
Private estimation with public data reduces sample complexity.
problem Estimating private distributions with limited public data.
method Differentially private estimation with public data under constraints of pure or concentrated DP.
result Public data can significantly reduce private sample complexity for estimation.
Cryptocurrencies evolve through survival of the fittest, modeled with evolutionary finance.
problem Understanding the dynamics of cryptocurrency markets.
method Evolutionary finance concepts applied to toy models of cryptocurrency data.
result Survival of the fittest in cryptofinance is explained through scaling laws.
Develops Merton's model for private companies using DDM.
problem Lack of observable asset values for private companies.
method Uses dividend discount model (DDM) to develop structural model.
result Obtains closed-form formulas for equity and liability values, default probability.
Transform non-private e-values into differentially private ones.
problem Leaking sensitive data through non-private e-values.
method Developed a novel biased multiplicative noise mechanism.
result Differentially private e-values maintain strong statistical power and asymptotic equivalence to non-private ones.
Algorithm selects public datasets for private machine learning.
problem Choosing the most suitable public dataset for private machine learning.
method Measures gradient subspace distance between public and private datasets.
result Excess risk scales with the subspace distance between gradients.
Differentially private hyperparameter tuning improves privacy in machine learning.
problem Hyperparameter tuning leaks private information through selected configurations.
method Local Bayesian optimization using Gaussian Process surrogate for private gradient approximation.
result DP-GIBO converges to locally optimal hyperparameters with polynomial dimensional dependence.
Optimizes private statistics with noisy methods.
problem Private inference in statistical models.
method Noisy optimization for M-estimators and confidence regions.
result Private estimators converge to non-private ones with high probability.
Dynamic model considers private asset markets' complexities.
problem Understanding and optimizing private asset allocation.
method State-of-the-art dynamic model with machine learning.
result Optimal investment policies quantified over fund life.
Bounds on Littlestone dimension for private learning and online prediction.
problem Understanding the Littlestone dimension of composed classes for private learning.
method Deriving bounds on Littlestone dimension and transforming private learners.
result Improved bounds on sample complexity for private learning.
We consider learning problems where the training set consists of two types of examples: private and public. The goal is to design a learning algorithm that satisfies differential privacy only with respect to the private examples. This setting interpolates between private learning (where all examples are private) and cl…