Communication Theoretic Data Analytics

Widespread use of the Internet and social networks invokes the generation of big data, which is proving to be useful in a number of applications. To deal with explosively growing amounts of data, data analytics has emerged as a critical technology related to computing, signal processing, and information networking. In this paper, a formalism is considered in which data is modeled as a generalized social network and communication theory and information theory are thereby extended to data analytics. First, the creation of an equalizer to optimize information transfer between two data variables is considered, and financial data is used to demonstrate the advantages. Then, an information coupling approach based on information geometry is applied for dimensionality reduction, with a pattern recognition example to illustrate the effectiveness. These initial trials suggest the potential of communication theoretic data analytics for a wide range of applications.Comment: Published in IEEE Journal on Selected Areas in Communications, Jan. 201

Rotary balances: A selected, annotated bibliography

This bibliography on rotary balances contains 102 entries. It is part of NASA's support of the AGARD Fluid Dynamics Panel Working Group 11 on Rotary Balances. This bibliography includes works that might be useful to anyone interested in building or using rotor balances. Emphasis is on the rotary balance rigs and testing techniques rather than the aerodynamic data. Also included are some publications of historical interest which relate to key events in the development and use of rotary balances. The arrangement is chronological by date of publication in the case of reports and by presentation in the case of papers

Predictable Signals in Excess Returns: Evidence from Non-Gaussian State Space Models

The present work investigates predictable components in size-based and value-weighted market portfolios excess returns from NYSE, AMEX, and NASDAQ stocks over US Treasury bills using various Gaussian and non-Gaussian versions of state space or unobserved components models. Our state space or unobserved components model improves on Conrad and Kaul (1988) by taking into account fat tails that are widely documented in the returns series. Statistical hypotheses tests show existence of predictable components in excess returns for most size-based portfolios (Cap-1 through Cap-9) even at percent level of significance. However, for value-weighted market and largest size-based portfolio (Cap-10) the hypothesis tests fail to reveal existence of any predictable component. The results for most size-based portfolios are in conformance with Conrad and Kaul (1988) except the value-weighted market excess returns as well as the largest size-based portfolio (Cap-10). Conrad and Kaul (1988) isolated time-varying expected returns in weekly size-based excess returns using the same methodology but in a Gaussian setting. However, our results on value-weighted market excess returns are in line with Bidarkota and McCulloch (2004) who investigated value-weighted market excess returns in CRSP data.stock return predictability unobserved components fat tails stable distributions

Forecasting in dynamic factor models using Bayesian model averaging

This paper considers the problem of forecasting in dynamic factor models using Bayesian model averaging. Theoretical justifications for averaging across models, as opposed to selecting a single model, are given. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms which simulate from the space defined by all possible models. We discuss how these simulation algorithms can also be used to select the model with the highest marginal likelihood (or highest value of an information criterion) in an efficient manner. We apply these methods to the problem of forecasting GDP and inflation using quarterly U.S. data on 162 time series. For both GDP and inflation, we find that the models which contain factors do out-forecast an AR(p), but only by a relatively small amount and only at short horizons. We attribute these findings to the presence of structural instability and the fact that lags of dependent variable seem to contain most of the information relevant for forecasting. Relative to the small forecasting gains provided by including factors, the gains provided by using Bayesian model averaging over forecasting methods based on a single model are appreciable

Microeconomic Structure determines Macroeconomic Dynamics. Aoki defeats the Representative Agent

Masanao Aoki developed a new methodology for a basic problem of economics: deducing rigorously the macroeconomic dynamics as emerging from the interactions of many individual agents. This includes deduction of the fractal / intermittent fluctuations of macroeconomic quantities from the granularity of the mezo-economic collective objects (large individual wealth, highly productive geographical locations, emergent technologies, emergent economic sectors) in which the micro-economic agents self-organize. In particular, we present some theoretical predictions, which also met extensive validation from empirical data in a wide range of systems: - The fractal Levy exponent of the stock market index fluctuations equals the Pareto exponent of the investors wealth distribution. The origin of the macroeconomic dynamics is therefore found in the granularity induced by the wealth / capital of the wealthiest investors. - Economic cycles consist of a Schumpeter 'creative destruction' pattern whereby the maxima are cusp-shaped while the minima are smooth. In between the cusps, the cycle consists of the sum of 2 'crossing exponentials': one decaying and the other increasing. This unification within the same theoretical framework of short term market fluctuations and long term economic cycles offers the perspective of a genuine conceptual synthesis between micro- and macroeconomics. Joining another giant of contemporary science - Phil Anderson - Aoki emphasized the role of rare, large fluctuations in the emergence of macroeconomic phenomena out of microscopic interactions and in particular their non self-averaging, in the language of statistical physics. In this light, we present a simple stochastic multi-sector growth model.Comment: 42 pages, 6 figure