Portfolio Construction with Hierarchical Momentum

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Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

Using data on international equity portfolio allocations by U.S. mutual funds, we estimate a portfolio expression derived from a standard mean-variance portfolio model extended with portfolio frictions. The optimal portfolio depends on the previous month a ...

Central banks are increasingly concerned about climate-related risks and want to ensure that the financial system is resilient to them. As they integrate these risks into financial stability monitoring, they also discuss how to apply environmental criteria ...

We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. Our method learns the features necessary for an effective low-dimensi ...

This thesis aims to provide novel analyses and data that improve the understanding of the financing of investments in clean technologies. In particular, this thesis explores the role that private and public actors play in supporting young innovative firms. ...

This thesis examines how banks choose their optimal capital structure and cash reserves in the presence of regulatory measures. The first chapter, titled Bank Capital Structure and Tail Risk, presents a bank capital structure model in which bank assets ...

Given the urgency of deploying all possible ways to combat climate change, and in light of lessons learned from the Covid-19 pandemic outbreak that it was a mistake to ignore signals and not prepare for worst-case scenarios, this article suggests that tech ...

The COVID-19 pandemic has demonstrated the importance and value of multi-period asset allocation strategies responding to rapid changes in market behavior. In this article, we formulate and solve a multi-stage stochastic optimization problem, choosing the ...

The current pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected most of the world in a profound way. As an indirect consequence, the general public has been put into direct contact with the research process, almost ...

We solve a portfolio choice problem when expected returns, covariances, and trading costs follow a regime-switching model. The optimal policy trades towards an aim portfolio given by a weighted-average of the conditional mean-variance-efficient portfolios ...

This thesis addresses the question of a patent value from three different angles. It comprises three papers on the patent valuation methods. The patent valuation issues are well-known to the world of research and practice. However, the debates over what th ...

Popular clustering algorithms based on usual distance functions (e.g., the Euclidean distance) often suffer in high dimension, low sample size (HDLSS) situations, where concentration of pairwise distances and violation of neighborhood structure have advers ...

The need to evaluate natural resource investments under uncertainty has given rise to the development of real options valuation; however, the analysis of such investments has been restricted by the capabilities of existing valuation approaches. We re-visit ...

Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all avai ...

The industrial sector has a large presence in world energy consumption and CO2 emissions, which has made it one of the focal points for energy and resource efficiency studies. However, large investments are required to retrofit existing industrial plants, ...

Motivation: Unbiased clustering methods are needed to analyze growing numbers of complex data sets. Currently available clustering methods often depend on parameters that are set by the user, they lack stability, and are not applicable to small data sets. ...

An overlapping generations model with investors having heterogeneous investment horizons leads to a two-factor asset pricing model. The risk premiums are determined by the exposure to the market (myopic betas) and the future return on the efficient portfol ...

The Competition for Authenticated Encryption: Security, Applicability and Robustness (CAESAR) has as its official goal to “identify a portfolio of authenticated ciphers that offer advantages over [the Galois-Counter Mode with AES]” and are suitable for wid ...

Data is pervasive in today's world and has actually been for quite some time. With the increasing volume of data to process, there is a need for faster and at least as accurate techniques than what we already have. In particular, the last decade recorded t ...

This thesis examines predictability and seasonality in the cross-section of stock returns. The first chapter, titled ``Infrequent Rebalancing, Return Autocorrelation, and Seasonality,'' shows that a model of infrequent rebalancing can explain specific pred ...

Portfolio Construction with Hierarchical Momentum

Chattez avec Graph Search

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

International Portfolio Choice with Frictions: Evidence from Mutual Funds

Greening the Swiss National Bank's Portfolio

A machine learning approach to portfolio pricing and risk management for high-dimensional problems

Essays on the Role of Policy and Venture Financing in Clean Technology Innovation

Essays in Banking and Financial Regulation

Combatting climate change through a portfolio of approaches

COVID-19: data-driven dynamic asset allocation in times of pandemic

Transgenic Model Systems Have Revolutionized the Study of Disease

Liquidity regimes and optimal dynamic asset allocation

The triangular value of patents

On Perfect Clustering of High Dimension, Low Sample Size Data

Re-evaluating natural resource investments under uncertainty: An alternative to limited traditional approaches

Graph Laplacian Mixture Model

An Optimisation Approach for Long-Term Industrial Investment Planning

Two-Tier Mapper, an unbiased topology-based clustering method for enhanced global gene expression analysis

Non-myopic betas

Can Caesar Beat Galois?

Robust and Efficient Data Clustering with Signal Processing on Graphs

Three Essays on Predictability and Seasonality in the Cross-Section of Stock Returns