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on Market Microstructure |
| By: | David Batista Soares (ECO-PUB - Economie Publique - INRA - Institut National de la Recherche Agronomique - AgroParisTech, GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen - UNICAEN - Université de Caen Normandie - NU - Normandie Université - ENSICAEN - École Nationale Supérieure d'Ingénieurs de Caen - NU - Normandie Université - CNRS - Centre National de la Recherche Scientifique); Alain Bretto (Equipe CODAG - Laboratoire GREYC - UMR6072 - GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen - UNICAEN - Université de Caen Normandie - NU - Normandie Université - ENSICAEN - École Nationale Supérieure d'Ingénieurs de Caen - NU - Normandie Université - CNRS - Centre National de la Recherche Scientifique); Joël Priolon (ECO-PUB - Economie Publique - INRA - Institut National de la Recherche Agronomique - AgroParisTech) |
| Abstract: | In efficient markets, asset prices are equal to their fundamentals. This classical view is considered valid for agricultural commodities' spot and futures markets. However, fragmentation of orders impacts price dynamics, leading to modification in spot and futures' trade frequency, relative trade frequency, and quantities exchanged. To highlight public policies on the impacts of fragmentation of orders, it is necessary to improve the understanding of its theoretical consequences. Based on a sequential trading framework, our main result showed that unbiased prices and a minimal volatility of fundamental basis are achieved not with optimal trade frequencies but with an optimal relative trade frequency. |
| Keywords: | commodities,spot and futures prices,market efficiency,volatility,stock and fragmentation |
| Date: | 2019–11–15 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-02364549&r=all |
| By: | Alex S. L. Tse; Harry Zheng |
| Abstract: | A speculative agent with Prospect Theory preference chooses the optimal time to purchase and then to sell an indivisible risky asset as to maximize the expected utility of the round-trip profit net of transaction costs. The optimization problem is formulated as a sequential optimal stopping problem and we provide a complete characterization of the solution. Depending on the preference and market parameters as well as the initial price of the asset, the optimal strategy can be "buy and hold", "buy low sell high", "buy high sell higher" or "no trading". Transaction costs do not necessarily curb speculative trading. For example, while a large proportional transaction cost on sale can unambiguously suppress trading participation, introducing a fixed market entry fee will indeed encourage trading when the asset price level is high. |
| Date: | 2019–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1911.10106&r=all |
| By: | Jin, Muzhao; Kearney, Fearghal; Li, Youwei; Yang, Yung Chiang |
| Abstract: | This study conducts an investigation of intraday time-series momentum across four Chinese commodity futures contracts: copper, steel, soybean, and soybean meal. Our results indicate that the first half-hour return positively predicts the last half-hour return across all four futures. Furthermore, in metals markets, we find that first trading sessions with high volume or volatility are associated with the strongest intraday time-series momentum dynamics. Based on this, we propose an intraday momentum informed trading strategy that earns a return in excess of standard always long and buy-and-hold benchmarks. |
| Keywords: | Intraday Predictability; Time-Series; Momentum |
| JEL: | G12 G13 G15 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:97134&r=all |
| By: | Zihao Zhang; Stefan Zohren; Stephen Roberts |
| Abstract: | We adopt Deep Reinforcement Learning algorithms to design trading strategies for continuous futures contracts. Both discrete and continuous action spaces are considered and volatility scaling is incorporated to create reward functions which scale trade positions based on market volatility. We test our algorithms on the 50 most liquid futures contracts from 2011 to 2019, and investigate how performance varies across different asset classes including commodities, equity indices, fixed income and FX markets. We compare our algorithms against classical time series momentum strategies, and show that our method outperforms such baseline models, delivering positive profits despite heavy transaction costs. The experiments show that the proposed algorithms can follow large market trends without changing positions and can also scale down, or hold, through consolidation periods. |
| Date: | 2019–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1911.10107&r=all |