nep-mst New Economics Papers
on Market Microstructure
Issue of 2023‒02‒20
five papers chosen by
Thanos Verousis

  1. An Optimal Control Strategy for Execution of Large Stock Orders Using LSTMs By A. Papanicolaou; H. Fu; P. Krishnamurthy; B. Healy; F. Khorrami
  2. Generalizing Impermanent Loss on Decentralized Exchanges with Constant Function Market Makers By Rohan Tangri; Peter Yatsyshin; Elisabeth A. Duijnstee; Danilo Mandic
  3. News Diffusion in Social Networks and Stock Market Reactions By David Hirshleifer; Lin Peng; Qiguang Wang
  4. How speculative asset characteristics shape retail investors' selling behavior By Bernard, Sabine Esther; Weber, Martin; Loos, Benjamin
  5. Does the Launch of Shanghai Crude Oil Futures Stabilize the Spot Market ? A Financial Cycle Perspective By Dan Zhang; Arash Farnoosh; Zhengwei Ma

  1. By: A. Papanicolaou; H. Fu; P. Krishnamurthy; B. Healy; F. Khorrami
    Abstract: In this paper, we simulate the execution of a large stock order with real data and general power law in the Almgren and Chriss model. The example that we consider is the liquidation of a large position executed over the course of a single trading day in a limit order book. Transaction costs are incurred because large orders walk the order book, that is, they consume order-book liquidity beyond the best bid/ask. We model these transaction costs with a power law that is inversely proportional to trading volume. We obtain a policy approximation by training a long short term memory (LSTM) neural network to minimize transaction costs accumulated when execution is carried out as a sequence of smaller sub orders. Using historical S&P100 price and volume data, we evaluate our LSTM strategy relative to strategies based on time-weighted average price (TWAP) and volume-weighted average price (VWAP). For execution of a single stock, the input to the LSTM includes the entire cross section of data on all 100 stocks, including prices, volume, TWAPs and VWAPs. By using the entire data cross section, the LSTM should be able to exploit any inter-stock co-dependence in volume and price movements, thereby reducing overall transaction costs. Our tests on the S&P100 data demonstrate that in fact this is so, as our LSTM strategy consistently outperforms TWAP and VWAP-based strategies.
    Date: 2023–01
  2. By: Rohan Tangri; Peter Yatsyshin; Elisabeth A. Duijnstee; Danilo Mandic
    Abstract: Liquidity providers are essential for the function of decentralized exchanges to ensure liquidity takers can be guaranteed a counterparty for their trades. However, liquidity providers investing in liquidity pools face many risks, the most prominent of which is impermanent loss. Currently, analysis of this metric is difficult to conduct due to different market maker algorithms, fee structures and concentrated liquidity dynamics across the various exchanges. To this end, we provide a framework to generalize impermanent loss for multiple asset pools obeying any constant function market maker with optional concentrated liquidity. We also discuss how pool fees fit into the framework, and identify the condition for which liquidity provisioning becomes profitable when earnings from trading fees exceed impermanent loss. Finally, we demonstrate the utility and generalizability of this framework with simulations in BalancerV2 and UniswapV3.
    Date: 2023–01
  3. By: David Hirshleifer; Lin Peng; Qiguang Wang
    Abstract: We study how the social transmission of public news influences investors' beliefs and securities markets. Using an extensive dataset to measure investor social networks, we find that earnings announcements from firms in higher-centrality locations generate stronger immediate price and trading volume reactions. Post announcement, such firms experience weaker price drifts but higher and more persistent volume. This evidence suggests that while greater social connectedness facilitates timely incorporation of news into prices, it also triggers opinion divergence and excessive trading. We provide a model of these effects and present further supporting evidence with granular data based on StockTwits messages and household trading records.
    JEL: G11 G12 G14 G4 G41
    Date: 2023–01
  4. By: Bernard, Sabine Esther; Weber, Martin; Loos, Benjamin
    Abstract: Using German and US brokerage data we find that investors are more likely to sell speculative stocks trading at a gain. Investors' gain realizations are monotonically increasing in a stock's speculativeness. This translates into a high disposition effect for speculative and a much lower disposition effect for non-speculative stocks. Our findings hold across asset classes (stocks, passive, and active funds) and explain cross-sectional differences in investor selling behavior which previous literature attributed primarily to investor demographics. Our results are robust to rank or attention effects and can be linked to realization utility and rolling mental account.
    Keywords: Selling Behavior, Disposition Effect, Retail Investor, Speculation, Higher Moments of Return, Realization Utility
    JEL: D14 D81 D9 G11
    Date: 2023
  5. By: Dan Zhang (CUP - China University of Petroleum Beijing, IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School); Arash Farnoosh (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School); Zhengwei Ma (CUP - China University of Petroleum Beijing)
    Abstract: Based on the examination of price discovery between Shanghai crude oil futures and the spot market, this paper explores whether the introduction of Shanghai crude oil futures can play a stabilizing role in the spot market, alleviating the impact of the financial cycle risk on the crude oil market from March 2018 to December 2019. The results show that there is only a uni-directional relationship of the spot price to futures price, and spot plays a leading role in price discovery. The risk of the financial cycle will increase the volatility of spot price, and the introduction of crude oil futures market can increase the impact of the financial cycle on the spot market. The additional research on the microcosmic mechanism of Shanghai crude oil futures indicates that crude oil futures market mainly influences the spot market fluctuation through the behaviour of traders: speculation increases price volatility in the spot market, which is more pronounced in the high volatility of the financial cycle as oppose to hedging transaction.
    Keywords: Shanghai crude oil futures, Price discovery, Stabilization, Financial cycle
    Date: 2022–01

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