A Leland model for delta hedging in central risk books

Using a tractable extension of the model of Leland (1985), we study how a delta-hedging strategy can realistically be implemented using market and limit orders in a centralized, automated market-making desk that integrates trading and liquidity provision for both options and their underlyings. In the continuous-time limit, the optimal limit-order exposure can be computed explicitly by a pointwise maximization. It is determined by the relative magnitudes of adverse selection, bid–ask spreads, and volatilities. The corresponding option price—from which the option can be replicated using market and limit orders—is characterized via a nonlinear PDE. Our results highlight the benefit of tactical liquidity provision for contrarian trading strategies, even for a trading desk that is not a competitive market maker. More generally, the paper also showcases how reduced-form models are competitive with “brute force” numerical approaches to market microstructure. Both the estimation of microstructure parameters and the simulation of the optimal trading strategy are made concrete and reconciled with real-life high frequency data.     

创业者自信程度与融资表现:来自众筹的经验证据

基于京东众筹的微观数据,研究创业者自信程度对融资表现的影响,并通过创业经验调节、创业者类别异质性分析探讨不同众筹创业者的自信倾向和比较心理,结果发现:众筹创业者自信程度与融资表现呈倒U型曲线关系;在创业经验调节下的创业者心理认知更为有限理性;相较于中小企业创业者,“草根”创业者过度悲观的心理导致了更低的超额融资效率和投资人参与度。进一步研究发现,在适度范围内随着创业者自信程度的增大,其对于融资表现的促进作用不断增强,验证了已有结论及指标的适用性。研究对于响应国家“双创”政策、加深投资人对各类众筹创业者心理认知的了解、提升创业者在疫情后特殊时期的融资表现具有一定意义。     

Information asymmetry,east–west cultural differences,and divergence in investor reactions

This paper investigates the divergence in investor behaviour between the United States and China following the abolition of the Chinese presidential term limit in 2018, which may, in part, have reflected the heterogeneous opinions expressed in public online media regarding this event. Compared with Chinese investors, the sentiment among US investors was considerably more pessimistic. Accordingly, we find that Chinese companies listed in the United States significantly underperformed relative to a sample of propensity score-matched firms listed in China. Additionally, we find that the political connectedness of firms to the Chinese government strongly influenced the stock prices of US-listed Chinese firms.     

Deep order flow imbalance: Extracting alpha at multiple horizons from the limit order book

We employ deep learning in forecasting high-frequency returns at multiple horizons for 115 stocks traded on Nasdaq using order book information at the most granular level. While raw order book states can be used as input to the forecasting models, we achieve state-of-the-art predictive accuracy by training simpler “off-the-shelf” artificial neural networks on stationary inputs derived from the order book. Specifically, models trained on order flow significantly outperform most models trained directly on order books. Using cross-sectional regressions, we link the forecasting performance of a long short-term memory network to stock characteristics at the market microstructure level, suggesting that “information-rich” stocks can be predicted more accurately. Finally, we demonstrate that the effective horizon of stock specific forecasts is approximately two average price changes.     

When the going gets tough,do the tough go shopping?

This study examines the impacts of consumer confidence on stockpiling behavior and, subsequently, retail inventory management. We show how stockpiling behavior evolved during the “Great Recession” of 2008–2009 as consumer confidence waned and demonstrate the impact of this development on inventory management. Drawing on the two-segment household inventory theory consisting of nonstockpiling and stockpiling segments, we use a panel dataset (2005–2015) to calibrate household inventory holdings. This dataset then serves as input for a retailer-level case study. Our empirical analysis reveals significant impacts from changing stockpiling behavior. When consumer confidence is low, both stockpiling and nonstockpiling segments respond by reducing weekly consumption rates; however, the stockpiling segment also significantly lengthens the time between shopping trips, and ultimately increases the duration of inventory holdings. These changes to consumption and stockpiling add complexity to inventory planning, requiring retailers to carefully adjust inventory levels to maintain service levels.     

Unravelling the JPMorgan spoofing case using particle physics visualization methods

On 29 September 2020, JPMorgan was ordered to pay a settlement of $920.2 million for spoofing the metals and Treasury futures markets from 2008 to 2016. We examine these cases using a visualization method developed in particle physics (CERN) and the messages that the exchange receives about market activity rather than time-based snapshots. This approach allows to examine multiple indicators related to market manipulation and complement existing research methods, thereby enhancing the identification and understanding of, as well as the motivation for, market manipulation. In the JPMorgan cases, we offer an alternative motivation for spoofing than moving the price.