In multivariate time series systems, key insights can be obtained by discovering lead-lagrelationships inherent in the data, which refer to the dependence between two timeseries shifted in time relative to one another, and which can be leveraged for thepurposes of control, forecasting or...

This paper presents an analytical solution to the dynamic portfolio selection problem, considering transaction costs and signals with different persistence properties. Our proposed optimal portfolio policy involves a smooth adjustment towards a forward-looking portfolio that is composed of a...

We introduce a novel covariance estimator for portfolio selection that adapts to the persistent heteroskedastic environments of financial time series by employing exponentially weighted averages and nonlinearly shrinking the sample eigenvalues through cross-validation. Our estimator is structure...

We investigate the concept of network momentum, a novel trading signal derived from momentum spillover across assets. Initially observed within the confines of pairwise economic and fundamental ties, such as the stock-bond connection of the same company and stocks linked through supply-demand...

One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order book (LOB). Essential to this choice is the fill probability of a passive limit order placed in the LOB. This paper...

Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques, based on machine learning, can readily be employed when treating volatility as a univariate, daily time-series. However, econometric studies have shown that...

This paper introduces a large-dimensional covariance estimator that exploits the hierarchical structure in financial returns. Prevailing techniques that filter the noise in a covariance matrix according to hierarchical agglomeration are fragile to data perturbations and inordinately suppress...

We present a novel framework for analyzing the optimal asset and signal combination problem. We reformulate the original problem of portfolio selection from a set of correlated assets and signals into one of selecting from a set of uncorrelated trading strategies through the lens of Canonical...

Cross-sectional strategies are a classical and popular trading style, with recent high performing variants incorporating sophisticated neural architectures. While these strategies have been applied successfully to data-rich settings involving mature assets with long histories, deploying them on...