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 a critical question in finance: the use of large nonlinear over-parametrized models or simpler models to forecast financial time series and the balance between underfitting and overfitting, the bias-variance trade-off, and the absolute performance in the test set. The...

For a large financial market (which is a sequence of usual, “small” financial markets), we introduce and study a concept of no asymptotic arbitrage (of the first kind) which is invariant under discounting. We give two dual characterisations of this property in terms of (1) martingale-like...

This research examines voluntary financial communication on the Internet by companies quoted on Brussels' unregulated markets. In the absence of obligation to communicate, we wish to know if companies quoted on these markets are proactive regarding financial disclosure on their website? We also...

Hedge Fund returns are often highly serially correlated mainly due to illiquidity exposures given that investments in such securities tend to be inactively traded and associated market prices are not always readily available. Following that, observed returns of such alternative investments tend...

Traditional portfolio optimization models specify placement of capital as rather irrevocably and fully at risk through investment horizon(s) or continuously. Under this constraint, asset class allocation typically serves as primary mode of diversification, pursuing risk moderation by seeking to...

In recent years both equity and bond markets have been afflicted by high volatility. In order to build up a portfolio on a quantitative basis, several models may be used, such as minimum variance portfolio or equally weighted portfolio. In 2008/09 another way to deal with diversification came...

In finance, decision making and choice requires that we assume that asset prices tend to trend. This assumption also logically enables us to construct exits to limit losses and protect capital. But investors have good reason to be uneasy regarding the potential for significant loss when using a...

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,...