In this paper, the author uses geometrical and topological aspects of Exploratory Data Analysis (EDA) to examine Standard and Poor's (S&P), MSCI's and Thomson Reuters' (TRI) ways of determining which stocks are growth and which are value. The results of the analysis are that two of the firms -...

This research examines the structural properties of the macroscopic model introduced in [AlShelahi and Saigal, 2018]. We present a theoretical analysis of the behavior of the macroscopic variables. In particular, we show that the model exhibits shock-like solutions, providing a new narrative for...

Stock market variance-return or price relations are sometimes negative and sometimes positive. We explain these puzzling findings using a model with two ("bad" and "good") variances. In the model, conditional equity premium depends positively on bad variance and negatively on good variance....

Executing a basket of co-integrated assets is an important task facing investors. Here, we show how to do this accounting for the informational advantage gained from assets within and outside the basket, as well as for the permanent price impact of market orders (MOs) from all market...

This articlemodels the dependence risk and resource allocation characteristics of two 20-stock coal–uranium and oil–gas sector portfolios fromthe Australian market in the context of the global financial crisis of 2008–2009. The modeling framework implemented consists of pair vine copulas...

• Demonstration of outstanding investment performance due to noise filtering of covariance matrices in optimum portfolio selection exercises. This demonstration is based on the out-of-sample simulation of rebalancing trading done daily, weekly, biweekly and monthly.• Exhibits of investment...

Alexander Izmailov, Ph.D (theoretical physics) and Brian Shay, Ph.D (mathematics) of Market Memory Trading, L.L.C. present, in a series of nine (9) white papers, aspects of a revolutionary advance in uncovering hidden dependencies via filtering noise from correlation matrices developed by the...

We develop a discrete-time affine stochastic volatility model with time-varying conditional skewness (SVS). Importantly, we disentangle the dynamics of conditional volatility and conditional skewness in a coherent way. Our approach allows current asset returns to be asymmetric conditional on...

Using a recursive modeling approach and data from the Euro area, the following paper analyzes the counter-cyclicity, stock price volatility is believed to demonstrate with respect to the state of the economy. It further tests whether such interdependence is exploitable for volatility...

This paper proposes a new information share for price discovery based on Russell's (1999) autoregressive conditional intensity model. While previous studies rely on equally spaced high frequency data, we use the information conveyed by trade intensities to determine a market's contribution to...