Higgs properties measurements using the four lepton decay channel

The measurements of the properties of the Higgs boson are presented in the H$\rightarrow$ZZ$\rightarrow$4$\ell$ ($\ell$=e,$\mu$) decay channel using a data sample corresponding to an integrated luminosity of 35.9 fb$^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the CMS detector at the LHC. The signal-strength modifier $\mu$, defined as the production cross section of the Higgs boson times its branching fraction to four leptons relative to the standard model expectation, is measured to be $\mu=1.05^{+0.19}_{-0.17}$ at $m_{\mathrm{H}}=125.09~\mathrm{GeV}$. Constraints are set on the strength modifiers for the main Higgs boson production modes. The mass is measured to be $m_{\mathrm{H}}=125.26 \pm 0.21~\mathrm{GeV}$ and the width is constrained using on-shell production to be $\Gamma_{\mathrm{H}}<1.10~\mathrm{GeV}$, at $95\%$ CL. The fiducial cross section is measured to be $2.90^{+0.48}_{-0.44}({\rm stat.})^{+0.27}_{-0.22}({\rm sys.})~{\mathrm{fb}}$, which is compatible with the standard model prediction of $2.72\pm0.14~{\mathrm{fb}}$.Comment: Presented at LHCP201

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

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