A risk-aware fuzzy linguistic knowledge-based recommender system for hedge funds

Risk Assurance for Hedge Funds Using Zero Knowledge Proofs

This work introduces a new tool for a fund manager to verifiably communicate portfolio risk characteristics to an investor. We address the classic dilemma: How can an investor and fund manager build trust when the two party’s interests are not aligned? In addition to high returns, a savvy investor would like a fund’s composition to reflect his own risk preferences. Hedge funds, on the other han...

A Knowledge Based Recommender System with Multigranular Linguistic Information

Recommender systems are applications that have emerged in the e-commerce area in order to assist users in their searches in electronic shops. These shops usually offer a wide range of items that cover the necessities of a great variety of users. Nevertheless, searching in such a wide range of items could be a very difficult and time-consuming task. Recommender systems assist users to find out s...

Hedge Funds: Risk and Return

ince the early 1990s, hedge funds have become an increasingly popular asset class. The amount invested globally in hedge funds rose from approximately $50 billion in 1990 to approximately $1 trillion by the end of 2004.1 And because these funds characteristically use substantial leverage, they play a far more important role in the global securities markets than the size of their net assets indi...

Systemic Risk and Hedge Funds∗

Systemic risk is commonly used to describe the possibility of a series of correlated defaults among financial institutions—typically banks—that occur over a short period of time, often caused by a single major event. However, since the collapse of Long Term Capital Management in 1998, it has become clear that hedge funds are also involved in systemic risk exposures. The hedge-fund industry has ...

DRARS, A Dynamic Risk-Aware Recommender System