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

Co-design of forward-control and force-feedback methods for teleoperation of an unmanned aerial vehicle

The core hypothesis of this ongoing research project is that co-designing haptic-feedback and forward-control methods for shared-control teleoperation will enable the operator to more readily understand the shared-control algorithm, better enabling him or her to work collaboratively with the shared-control technology.} This paper presents a novel method that can be used to co-design forward control and force feedback in unmanned aerial vehicle (UAV) teleoperation. In our method, a potential field is developed to quickly calculate the UAV's risk of collision online. We also create a simple proxy to represent the operator's confidence, using the swiftness with which the operator sends commands the to UAV. We use these two factors to generate both a scale factor for a position-control scheme and the magnitude of the force feedback to the operator. Currently, this methodology is being implemented and refined in a 2D-simulated environment. In the future, we will evaluate our methods with user study experiments using a real UAV in a 3D environment.Accepted manuscrip

On Forecasting Recessions via Neural Nets

In this research, we employ artificial neural networks in conjunction with selected economic and financial variables to forecast recessions in Canada, France, Germany, Italy, Japan, UK, and USA. We model the relationship between selected economic and financial (indicator) variables and recessions 1-10 periods in future out-of-sample recursively. The out-of-sample forecasts from neural network models show that among the 10 models constructed from 7 indicator variables and their combinations that we investigate, the stock price index (index) and spread between bank rates and risk free rates (BRTB) are most likely candidate variables for possible forecasts of recessions 1-10 periods ahead for most countries.business cycles neural network out-of-sample forecasts recession real GDP

DETERMINATION OF VOLATILITY AND MEAN RETURNS: AN EVIDENCE FROM AN EMERGING STOCK MARKET

In the present research we work with excess returns for an emerging stock market i.e. Jamaican Stock Price Index for the determination of volatility persistence and persistence in the mean returns series. We model excess returns in this stock market using state space or unobserved component models, which is a signal extraction approach. Our model encompass stable distributions to account for fat tails and GARCH-like effects to account for time varying volatility that may be present in the series. The study results that are obtained using the most general as well as the restricted versions of the state space models reveal statistically significant evidence of volatility persistence in the excess returns series. Further, there exist persistent predictable signals in returns series at 5 percent level of significance, and the value of an efficiently estimated excess returns series is percent per month (percent per annum). Further, the series encompass a stable characteristic exponent of showing a non-normal behavior in this market.stock return predictability, unobserved components, fat tails, stable distributions

Predictability in Stock Returns in an Emerging Market: Evidence from KSE 100 Stock Price Index

We investigate the persistence in monthly KSE100 excess stock returns over the Treasury bills rates using non-Gaussian state space or unobservable component model with stable distributions and volatility persistence. Results from our non-Gaussian state space model, which is an improvement over Conard and Kaul (1988), show that the conditional distribution has a stable of 1.748 and normality is rejected even after accounting for GARCH. There exists a statistically significant predictable component in the KSE 100 excess stock returns. The optimal predictor in the unconditional expectation of the series is estimated to be 0.18 percent per annum. An evidence of highly nonconstant scales in different periods of time exhibits a tendency towards stock market crashes which invites remedial policy action.Stock Return Predictability, Unobserved Components, Fat Tails, Stable Distributions

BUSINESS CYCLE ASYMMETRIES IN STOCK RETURNS: ROBUST EVIDENCE

In this study we employ augmented and switching time series models to find possible existence of business cycle asymmetries in U.S. stock returns. Our approach is fully parametric and testing strategy is robust to any conditional heteroskedasticity, and outliers that may be present. We also approximate in sample as well as out-of-sample forecasts from artificial neural networks for testing business cycle nonlinearities in U.S. stock returns. Our results based on nonlinear augmented and switching time series models show a strong evidence of business cycle asymmetries in conditional mean dynamics of U.S. stock returns. These results also show that conditional heteroskedasticity is unimportant when testing for asymmetries in conditional mean. Moreover, the conditional volatility in stock returns is asymmetric and is more pronounced in recessions than in expansion phase of business cycles. Similarly, the results based on neural network models show a statistically significant evidence of business cycle nonlinearities in US stock returns. The magnitude of these nonlinearities is more obvious in post World War II era than in the full sample period.asymmetries; business cycles; conditional heteroskedasticity; long memory; nonlinearities; outliers; excess returns; stable distributions

Intermittency study of charged particles generated in Pb-Pb collisions at sNN= 2.76 TeV\sqrt{s_{\mathrm{NN}}}\text{= 2.76 TeV} using EPOS3

Charged particle multiplicity fluctuations in Pb-Pb collisions are studied for the central events generated using EPOS3 (hydro and hydro+cascade) at $\sqrt{s_{\mathrm{NN}}}\text{ = 2.76 TeV}$. Intermittency analysis is performed in the mid-rapidity region in two-dimensional ($\eta$, $\phi$) phase space within the narrow transverse momentum (p_\rm{{T}}) bins in the low p_\rm{{T}}~region (p_\rm{{T}}~\leq~1.0~GeV/\textit{c}). Power-law scaling of the normalized factorial moments with the number of bins is not observed to be significant in any of the p_\rm{{T}}-bin. Scaling exponent $\nu$, deduced for a few p_\rm{{T}} bins is greater than that of the value 1.304, predicted for the second order phase-transition by the Ginzburg-Landau theory. The link in the notions of fractality is also studied. Fractal dimensions, $D_{q}$ are observed to decrease with the order of the moment $q$ suggesting the multifractal nature of the particle generation in EPOS3.Comment: 7 pages, 8 figure

A strategy for achieving manufacturing statistical process control within a highly complex aerospace environment

This paper presents a strategy to achieve process control and overcome the previously mentioned industry constraints by changing the company focus to the process as opposed to the product. The strategy strives to achieve process control by identifying and controlling the process parameters that influence process capability followed by the implementation of a process control framework that marries statistical methods with lean business process and change management principles. The reliability of the proposed strategy is appraised using case study methodology in a state of the art manufacturing facility on Multi-axis CNC machine tools