In this study we use machine learning algorithm to test Amareos sentiment indicator's predictive power of market reversals. We then build and test a viable trading strategy.As input for the algorithm, we used eight market sentiment indicators (Anger, Anticipation, Disgust, Fear, Gloom, Joy,...

We explore in this paper the use of deep signature models to predict equity financial time series returns. First, we use signature transformations to model the underlying shape of the input equity returns; further assuming the underlying shape remains the same, we predict future values based on...

We examine in this paper the training and test set performance of several equity factor models with a dataset of 20 years of data, 1,200 stocks and 100 factors. First, we examine several models to forecast expected returns, which can be used as baselines for more complex models: linear...

The purpose of this paper is to examine the existence and persistence of momentum, “the premier anomaly” according to Eugene Fama, as a source of out-performance in the Indian equity markets. We test a set of relative-strength strategies on twenty years of Indian equity markets data to...

The problem of multicollinearity in the assessments of coefficients is well established. However, it is rarely researched in the estimations of macroeconomic variables and economic performance of developing countries. Predicatively, it has impacts on the estimations of coefficients that should...

In order to have references for discussing mathematical menus in political science, I review the most common types of mathematical formulae used in physics and chemistry, as well as some mathematical advances in economics. Several issues appear relevant: variables should be well defined and...

This study sheds new light on the question of whether or not sentiment surveys, and the expectations derived from them, are relevant to forecasting economic growth and stock returns, and whether they contain information that is orthogonal to macroeconomic and financial data. I examine 16...

We describe in this paper a method allowing to order submodels in linear regression. A real function is attached to each submodel, allowing to graphically compare and order them. Our procedure defines an objective function depending on two factors (lack of fit and multicolinearity) with the...

Abstract. This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when the number of parameters is of a much larger...