Online portfolio selection is a fundamental problem in computational finance, which has
been extensively studied across several research communities, including finance, statistics …

Successful investment strategies are specific implementations of general theories. An
investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset …

With the aim to sequentially determine optimal allocations across a set of assets, Online
Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape …

The Black–Litterman (BL) model for portfolio optimization combines investors' expectations
with the Markowitz framework. The BL model is designed for investors with private …

We explored the application of a machine learning method, Logitboost, to automatically
calibrate a trading model using different versions of the same technical analysis indicators …

We provide detailed descriptions, including over 550 mathematical formulas, for over 150
trading strategies across a host of asset classes (and trading styles). This includes stocks …

Market making is a high-frequency trading problem for which solutions based on
reinforcement learning (RL) are being explored increasingly. This paper presents an …

Cluster analysis is a commonly used technique by investors to create a diversified portfolio.
The approach aims at maximizing returns for a tolerable degree of risks. To diversify …

The objective of this paper is to present and discuss a link mining algorithm called
CorpInterlock and its application to the financial domain. This algorithm selects the largest …

Investors generally learn from historical data and use it to improve future investment
decisions. However, existing portfolio selection research rarely considers such a concept …