Machine learning (ML) is progressively reshaping the fields of quantitative finance and
algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers …

In this paper, we document the importance of memory in machine learning (ML)-based
models relying on firm characteristics for asset pricing. We find that predictive algorithms …

The authors propose three principles for evaluating the practical efficacy of machine
learning for stock selection, and they compare the performance of various models and …

The rapid advancement of models based on artificial intelligence demands innovative
monitoring techniques which can operate in real time with low computational costs. In …

We propose a novel method to improve estimation of asset returns for portfolio optimization.
This approach first performs a monthly directional market forecast using an online decision …

We reverse-engineer the equilibrium construction process of asset prices in order to obtain
returns which depend on firm characteristics, possibly in a linear fashion. One key …

Modern technology has made a big contribution to the distribution of counterfeit money and
the valuation of it. This paper recommends a deep learning-based methodology for currency …

Can machines learn to reliably predict auction outcomes in financial markets? The authors
study this question using classification methods from machine learning and auction data …

The main rationale of asset pricing theory is to identify the underlying pattern of the drivers
and establish their relationship with the financial performance of a firm. The proliferation of …

Marking illiquid assets to market is challenging due to the scarcity of information that could
indicate their fair price. In the case of illiquid debentures, one of the hindrances to figuring …