Government Policy and Ownership of Financial Assets

Since World War II, direct stock ownership by households across the globe has largely been replaced by indirect stock ownership by financial institutions. We argue that tax policy is the driving force. Using long time-series from eight countries, we show that the fraction of household ownership decreases with measures of the tax benefits of holding stocks inside tax-deferred plans. This finding is important for policy considerations on effective taxation and for financial economics research on the long-term effects of taxation on corporate finance and asset prices.

The evolution of aggregate stock ownership : [Version December 2010]

Since World War II, direct stock ownership by households has largely been replaced by indirect stock ownership by financial institutions. We argue that tax policy is the driving force. Using long time-series from eight countries, we show that the fraction of household ownership decreases with measures of the tax benefits of holding stocks inside a pension plan. This finding is important for policy considerations on effective taxation and for financial economics research on the long-term effects of taxation on corporate finance and asset prices. JEL Classification: G10, G20, H22, H30 Keywords: Capital Gains Tax, Income Tax, Stock Ownership, Bond Ownership, Inflation, Bracket Creep, Pension Fund

Ensemble learning of model hyperparameters and spatiotemporal data for calibration of low-cost PM2.5 sensors.

he PM2.5 air quality index (AQI) measurements from government-built supersites are accurate but cannot provide a dense coverage of monitoring areas. Low-cost PM2.5 sensors can be used to deploy a fine-grained internet-of-things (IoT) as a complement to government facilities. Calibration of low-cost sensors by reference to high-accuracy supersites is thus essential. Moreover, the imputation for missing-value in training data may affect the calibration result, the best performance of calibration model requires hyperparameter optimization, and the affecting factors of PM2.5 concentrations such as climate, geographical landscapes and anthropogenic activities are uncertain in spatial and temporal dimensions. In this paper, an ensemble learning for imputation method selection, calibration model hyperparameterization, and spatiotemporal training data composition is proposed. Three government supersites are chosen in central Taiwan for the deployment of low-cost sensors and hourly PM2.5 measurements are collected for 60 days for conducting experiments. Three optimizers, Sobol sequence, Nelder and Meads, and particle swarm optimization (PSO), are compared for evaluating their performances with various versions of ensembles. The best calibration results are obtained by using PSO, and the improvement ratios with respect to R2, RMSE, and NME, are 4.92%, 52.96%, and 56.85%, respectively

The Evolution of Aggregate Stock Ownership - A Unified Explanation

Since World War II, direct stock ownership by households has largely been replaced by indirect stock ownership by financial institutions. We argue that tax policy is the driving force. Using long time-series from eight countries, we show that the fraction of household ownership decreases with measures of the tax benefits of holding stocks inside a pension plan. This finding is important for policy considerations on effctive taxation and for financial economics research on the long-term effects of taxation on corporate finance and asset prices.Capital gains tax; income tax; stock ownership; inflation; bracket creep; pension funds

Data Analytics and Machine Learning to Enhance the Operational Visibility and Situation Awareness of Smart Grid High Penetration Photovoltaic Systems

Electric utilities have limited operational visibility and situation awareness over grid-tied distributed photovoltaic systems (PV). This will pose a risk to grid stability when the PV penetration into a given feeder exceeds 60% of its peak or minimum daytime load. Third-party service providers offer only real-time monitoring but not accurate insights into system performance and prediction of productions. PV systems also increase the attack surface of distribution networks since they are not under the direct supervision and control of the utility security analysts. Six key objectives were successfully achieved to enhance PV operational visibility and situation awareness: (1) conceptual cybersecurity frameworks for PV situation awareness at device, communications, applications, and cognitive levels; (2) a unique combinatorial approach using LASSO-Elastic Net regularizations and multilayer perceptron for PV generation forecasting; (3) applying a fixed-point primal dual log-barrier interior point method to expedite AC optimal power flow convergence; (4) adapting big data standards and capability maturity models to PV systems; (5) using K-nearest neighbors and random forests to impute missing values in PV big data; and (6) a hybrid data-model method that takes PV system deration factors and historical data to estimate generation and evaluate system performance using advanced metrics. These objectives were validated on three real-world case studies comprising grid-tied commercial PV systems. The results and conclusions show that the proposed imputation approach improved the accuracy by 91%, the estimation method performed better by 75% and 10% for two PV systems, and the use of the proposed forecasting model improved the generalization performance and reduced the likelihood of overfitting. The application of primal dual log-barrier interior point method improved the convergence of AC optimal power flow by 0.7 and 0.6 times that of the currently used deterministic models. Through the use of advanced performance metrics, it is shown how PV systems of different nameplate capacities installed at different geographical locations can be directly evaluated and compared over both instantaneous as well as extended periods of time. The results of this dissertation will be of particular use to multiple stakeholders of the PV domain including, but not limited to, the utility network and security operation centers, standards working groups, utility equipment, and service providers, data consultants, system integrator, regulators and public service commissions, government bodies, and end-consumers

Empirical econometric evaluation of alternative methods of dealing with missing values in investment climate surveys

Investment climate Surveys are valuable instruments that improve our understanding of the economic, social, political, and institutional factors determining economic growth, particularly in emerging and transition economies. However, at the same time, they have to overcome some difficult issues related to the quality of the information provided; measurement errors, outlier observations, and missing data that are frequently found in these datasets. This paper discusses the applicability of recent procedures to deal with missing observations in investment climate surveys. In particular, it presents a simple replacement mechanism -- for application in models with a large number of explanatory variables -- which in turn is a proxy of two methods: multiple imputations and an export-import algorithm. The performance of this method in the context of total factor productivity estimation in extended production functions is evaluated using investment climate surveys from four countries: India, South Africa, Tanzania, and Turkey. It is shown that the method is very robust and performs reasonably well even under different assumptions on the nature of the mechanism generating missing data.E-Business,Statistical&Mathematical Sciences,Economic Theory&Research,Information Security&Privacy,Information and Records Management

Can k-NN imputation improve the performance of C4.5 with small software project data sets? A comparative evaluation

Missing data is a widespread problem that can affect the ability to use data to construct effective prediction systems. We investigate a common machine learning technique that can tolerate missing values, namely C4.5, to predict cost using six real world software project databases. We analyze the predictive performance after using the k-NN missing data imputation technique to see if it is better to tolerate missing data or to try to impute missing values and then apply the C4.5 algorithm. For the investigation, we simulated three missingness mechanisms, three missing data patterns, and five missing data percentages. We found that the k-NN imputation can improve the prediction accuracy of C4.5. At the same time, both C4.5 and k-NN are little affected by the missingness mechanism, but that the missing data pattern and the missing data percentage have a strong negative impact upon prediction (or imputation) accuracy particularly if the missing data percentage exceeds 40%

The Extreme Risk of Personal Data Breaches & The Erosion of Privacy

Personal data breaches from organisations, enabling mass identity fraud, constitute an \emph{extreme risk}. This risk worsens daily as an ever-growing amount of personal data are stored by organisations and on-line, and the attack surface surrounding this data becomes larger and harder to secure. Further, breached information is distributed and accumulates in the hands of cyber criminals, thus driving a cumulative erosion of privacy. Statistical modeling of breach data from 2000 through 2015 provides insights into this risk: A current maximum breach size of about 200 million is detected, and is expected to grow by fifty percent over the next five years. The breach sizes are found to be well modeled by an \emph{extremely heavy tailed} truncated Pareto distribution, with tail exponent parameter decreasing linearly from 0.57 in 2007 to 0.37 in 2015. With this current model, given a breach contains above fifty thousand items, there is a ten percent probability of exceeding ten million. A size effect is unearthed where both the frequency and severity of breaches scale with organisation size like $s^{0.6}$. Projections indicate that the total amount of breached information is expected to double from two to four billion items within the next five years, eclipsing the population of users of the Internet. This massive and uncontrolled dissemination of personal identities raises fundamental concerns about privacy.Comment: 16 pages, 3 sets of figures, and 4 table