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on Market Microstructure |
| By: | Matteo Prata; Giuseppe Masi; Leonardo Berti; Viviana Arrigoni; Andrea Coletta; Irene Cannistraci; Svitlana Vyetrenko; Paola Velardi; Novella Bartolini |
| Abstract: | The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL models focusing on Stock Price Trend Prediction (SPTP) based on Limit Order Book (LOB) data. To carry out this study, we developed LOBCAST, an open-source framework that incorporates data preprocessing, DL model training, evaluation and profit analysis. Our extensive experiments reveal that all models exhibit a significant performance drop when exposed to new data, thereby raising questions about their real-world market applicability. Our work serves as a benchmark, illuminating the potential and the limitations of current approaches and providing insight for innovative solutions. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.01915&r=mst |
| By: | Cetemen, D.; Cisternas, G.; Kolb, A.; Viswanathan, S. |
| Abstract: | Two activists with correlated private positions in a firm's stock trade sequentially before simultaneously exerting effort that determines the firm's value. We document the existence of a novel linear equilibrium in which an activist's trades have positive sensitivity to her block size, but such orders are not zero on average: the leader activist manipulates the price to induce the follower to acquire a larger position and thus add more value. We examine the implications of this equilibrium for market outcomes and discuss its connection with the prominent phenomenon of \wolf-pack" activism: multiple hedge funds engaging in parallel with a target firm. We also explore the possibility of other equilibria where the activists trade against their initial positions. |
| Keywords: | activism; insider trading; noisy signaling; price manipulation; hedge funds |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:cty:dpaper:22/04&r=mst |
| By: | Daher, Wassim; Karam, Fida; Ahmed, Naveed |
| Abstract: | We study a generalization of the Kyle (1985) static model with two risk neutral insiders to the case where each insider is partially informed about the value of the stock and compete under Stackelberg setting. First, we characterize the linear Bayesian equilibrium. Then, we carry out a comparative statics analysis. Our findings reveal that partial information increases the insiders profits in a Stackelberg setting than in a Cournot setting. Finally we study the impact of the information sharing on equilibrium outcomes. |
| Keywords: | Insider trading, Risk neutrality, Partial Information, Stackelberg structure, Kyle model |
| JEL: | D82 G14 |
| Date: | 2023–06–29 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:118138&r=mst |