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researcher

Tessa Bauman

2 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author1

Across the 2 of 2 papers where every author was matched, so the position is known.

fields
  • q-fin.PM1
  • q-fin.TR1
ORCID 0000-0003-4228-7583

identity via Semantic Scholar / OpenAlex

most citedDeep reinforcement learning with positional context for intraday trading

10 citations · 11 across the 2 of their papers we have counts for

collaborators

2 papers

q-fin.TR2024★ 10 cited

Deep reinforcement learning with positional context for intraday trading

Sven Goluža, Tomislav Kovačević, Tessa Bauman +1

Deep reinforcement learning (DRL) is a well-suited approach to financial decision-making, where an agent makes decisions based on its trading strategy developed from market observa…

q-fin.PM2023★ 1 cited

Deep Reinforcement Learning for Robust Goal-Based Wealth Management

Tessa Bauman, Bruno Gašperov, Stjepan Begušić +1

Goal-based investing is an approach to wealth management that prioritizes achieving specific financial goals. It is naturally formulated as a sequential decision-making problem as…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.