nep-mst New Economics Papers
on Market Microstructure
Issue of 2022‒08‒29
two papers chosen by
Thanos Verousis

  1. Dealing with multi-currency inventory risk in FX cash markets By Alexander Barzykin; Philippe Bergault; Olivier Gu\'eant
  2. Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models By Ramit Sawhney; Shivam Agarwal; Vivek Mittal; Paolo Rosso; Vikram Nanda; Sudheer Chava

  1. By: Alexander Barzykin; Philippe Bergault; Olivier Gu\'eant
    Abstract: In FX cash markets, market makers provide liquidity to clients for a wide variety of currency pairs. Because of flow uncertainty and market volatility, they face inventory risk. To mitigate this risk, they typically skew their prices to attract or divert the flow and trade with their peers on the dealer-to-dealer segment of the market for hedging purposes. This paper offers a mathematical framework to FX dealers willing to maximize their expected profit while controlling their inventory risk. Approximation techniques are proposed which make the framework scalable to any number of currency pairs.
    Date: 2022–07
  2. By: Ramit Sawhney; Shivam Agarwal; Vivek Mittal; Paolo Rosso; Vikram Nanda; Sudheer Chava
    Abstract: The rapid spread of information over social media influences quantitative trading and investments. The growing popularity of speculative trading of highly volatile assets such as cryptocurrencies and meme stocks presents a fresh challenge in the financial realm. Investigating such "bubbles" - periods of sudden anomalous behavior of markets are critical in better understanding investor behavior and market dynamics. However, high volatility coupled with massive volumes of chaotic social media texts, especially for underexplored assets like cryptocoins pose a challenge to existing methods. Taking the first step towards NLP for cryptocoins, we present and publicly release CryptoBubbles, a novel multi-span identification task for bubble detection, and a dataset of more than 400 cryptocoins from 9 exchanges over five years spanning over two million tweets. Further, we develop a set of sequence-to-sequence hyperbolic models suited to this multi-span identification task based on the power-law dynamics of cryptocurrencies and user behavior on social media. We further test the effectiveness of our models under zero-shot settings on a test set of Reddit posts pertaining to 29 "meme stocks", which see an increase in trade volume due to social media hype. Through quantitative, qualitative, and zero-shot analyses on Reddit and Twitter spanning cryptocoins and meme-stocks, we show the practical applicability of CryptoBubbles and hyperbolic models.
    Date: 2022–05

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