We study the problem of finding optimal execution strategy in the presence of block trades
in the pools of non-displayed liquidity. In the single-stock trade both market impact and risk …

… For the machine learning culture an agnostic approach is taken to the question whether
nature and financial markets are simple. We do have good reason to suspect that it is not …

… In other words, the observation x i is the one whose embedding y i is the closest to the
cluster center y k in the embedding space. In this study, the first method is used to obtain …

Practical applications of financial models require a proper assessment of the model risk due
to uncertainty of the model parameters. Methods of the probabilistic decision theory achieve …

I present arguments against the hypothesis put forward by Silver, Singh, Precup, and Sutton
( https://www.sciencedirect.com/science/article/pii/S0004370221000862 ) : reward …

Solving general Markov decision processes (MDPs) is a computationally hard problem.
Solving finite-horizon MDPs, on the other hand, is highly tractable with well known polynomial-…

… What is more important is that as we treat financial application with the probabilistic decision
… us that no model is perfect, no theory is final, and no parameter is ever known with certainty. …

… algorithms is never assured and missing is the … author’s answer is: affirmative. In this short
article the author presents a straightforward quantitative optimal dark allocation framework. It is …

… Sergey Glukhov The Candidate of technical sciences, assistance professor, the head of
the Chair of military and technical training of the Department of post-graduate education, The …

… American insurance market, where non-life insurance dominates, is one of the biggest in the
world, because a share of total premium volume in the world GDP is over 2% during research …