nep-mst New Economics Papers
on Market Microstructure
Issue of 2023‒06‒12
five papers chosen by
Thanos Verousis

  1. Impact of size and volume on cryptocurrency momentum and reversal By Milan Fičura
  2. The 2022 Spike in Corporate Security Settlement Fails By Michael J. Fleming; Or Shachar; Peter Van Tassel
  3. PRIME: A Price-Reverting Impact Model of a cryptocurrency Exchange By Christopher J. Cho; Timothy J. Norman; Manuel Nunes
  4. Why Fixed-Price Policy Prevails: The Effect of Trade Frictions and Competition By Selcuk, Cemil; Gokpinar, Bilal
  5. NFT Wash Trading Detection By Derek Liu; Francesco Piccoli; Katie Chen; Adrina Tang; Victor Fang

  1. By: Milan Fičura
    Abstract: We analyse how cryptocurrency size and trading volume impact the momentum and reversal dynamics of their returns. We show that the previously reported weekly return reversal occurs for small and illiquid coins only (t-stat = -7.31), while the large and liquid coins exhibit weekly momentum effect instead (t-stat = 2.33). Long-term returns exhibit reversal effects, which are, however, insignificant for the large and liquid coins. We further analyse the impact of high momentum on future cryptocurrency returns, measured as the distance of previous-week closing price from the k-week high. High momentum has not been analysed on cryptocurrency markets before, and we show it to be a superior predictor of future returns when compared to regular momentum. The distance from the 1-week high predicts negatively future returns of small and illiquid coins (t-stat = -9.03) and positively future returns of large and liquid coins (t-stat = 4.93). The results are highly robust to different settings of the size and liquidity thresholds. We further show that the short-term reversal of small and illiquid coins is driven mostly by their low trading volumes, while the short-term momentum of large and liquid coins is driven mostly by high market capitalizations and to a lower degree by high trading volumes.
    Keywords: Cryptocurrency, momentum, reversal, high-momentum, size, liquidity, asset pricing
    JEL: G11 G12 G17
    Date: 2023–04–05
  2. By: Michael J. Fleming; Or Shachar; Peter Van Tassel
    Abstract: Settlement fails in corporate securities increased sharply in 2022, reaching levels not seen since the 2007-09 financial crisis. As a fraction of trading volume, fails that involve primary dealers reached an all-time high in the week of March 23, 2022. In this post, we investigate the 2022 spike in settlement fails for corporate securities and discuss potential drivers for this increase, including trading volume, corporate issuance, fails in bond ETFs, and operational problems.
    Keywords: settlement fails; corporate securities; primary dealers
    JEL: G1
    Date: 2023–04–10
  3. By: Christopher J. Cho; Timothy J. Norman; Manuel Nunes
    Abstract: In a financial exchange, market impact is a measure of the price change of an asset following a transaction. This is an important element of market microstructure, which determines the behaviour of the market following a trade. In this paper, we first provide a discussion on the market impact observed in the BTC/USD Futures market, then we present a novel multi-agent market simulation that can follow an underlying price series, whilst maintaining the ability to reproduce the market impact observed in the market in an explainable manner. This simulation of the financial exchange allows the model to interact realistically with market participants, helping its users better estimate market slippage as well as the knock-on consequences of their market actions. In turn, it allows various stakeholders such as industrial practitioners, governments and regulators to test their market hypotheses, without deploying capital or destabilising the system.
    Date: 2023–05
  4. By: Selcuk, Cemil; Gokpinar, Bilal
    Abstract: Fixed-price selling is common in today's markets. While previous research in marketing and economics literatures provide several intuitive reasons for the emergence of fixed-price selling (e.g. clarity and simplicity of managing the fixed-price process, reduced coordination and information costs) our study offers an entirely different rationale---based on market competition and trade frictions---that explains the prevalence of fixed-price selling. Using a market equilibrium approach, and employing a novel competitive search framework to account for a fully competitive and dynamic market, we offer a new and micro-founded account for the widespread use of fixed pricing policy. Considering three important market characteristics---customer risk aversion, the degree of trade frictions and the level of market competition---we explore the strategic choice between the fixed-price, best-offer, and over-the-sticker pricing policies. Unlike the standard models in the literature, which are based Hotelling, Cournot, Bertrand frameworks, the competitive search framework enables us to model competition with a large number of buyers and sellers, and to vary the degree of competition accordingly. We find that fixed pricing emerges as the unique or the de-facto selling rule in most parameter regions. Indeed, the only region where haggling matters is the case in which customers are risk neutral and trade frictions are significant and market competition is moderate.
    Keywords: fixed-price selling, haggling, risk aversion, trade friction, competition
    JEL: D40
    Date: 2023
  5. By: Derek Liu; Francesco Piccoli; Katie Chen; Adrina Tang; Victor Fang
    Abstract: Wash trading is a form of market manipulation where the same entity sells an asset to themselves to drive up market prices, launder money under the cover of a legitimate transaction, or claim a tax loss without losing ownership of an asset. Although the practice is illegal with traditional assets, lack of supervision in the non-fungible token market enables criminals to wash trade and scam unsuspecting buyers while operating under regulators radar. AnChain.AI designed an algorithm that flags transactions within an NFT collection history as wash trades when a wallet repurchases a token within 30 days of previously selling it. The algorithm also identifies intermediate transactions within a wash trade cycle. Testing on 7 popular NFT collections reveals that on average, 0.14% of transactions, 0.11% of wallets, and 0.16% of tokens in each collection are involved in wash trading. These wash trades generate an overall total price manipulation, sales, and repurchase profit of \$900K, \$1.1M, and negative \$1.6M respectively. The results draw attention to the prevalent market manipulation taking place and inform unsuspecting buyers which tokens and sellers may be involved in criminal activity.
    Date: 2023–02

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