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on Market Microstructure |
| By: | Zhishun Wang; Wei Lu; Kaixin Zhang; Tianhao Li; Zixi Zhao |
| Abstract: | Making profits in stock market is a challenging task for both professional institutional investors and individual traders. With the development combination of quantitative trading and reinforcement learning, more trading algorithms have achieved significant gains beyond the benchmark model Buy&Hold (B&H). There is a certain gap between these algorithms and the real trading decision making scenarios. On the one hand, they only consider trading signals while ignoring the number of transactions. On the other hand, the information level considered by these algorithms is not rich enough, which limits the performance of these algorithms. Thus, we propose an algorithm called the Multi-frequency Continuous-share Trading algorithm with GARCH (MCTG) to solve the problems above, which consists of parallel network layers and deep reinforcement learning. The former is composed of three parallel network layers, respectively dealing with different frequencies (five minute, one day, one week) data, and day level considers the volatilities of stocks. The latter with a continuous action space of the reinforcement learning algorithm is used to solve the problem of trading stock shares. Experiments in different industries of Chinese stock market show our method achieves more extra profit comparing with basic DRL methods and bench model. |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2105.03625&r= |
| By: | Abdi, Farshid; Kormanyos, Emily; Pelizzon, Loriana; Getmansky, Mila; Simon, Zorka |
| Abstract: | We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they affect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump's tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump's decision to tweet about the economy. |
| Keywords: | Market efficiency,Social media,Twitter,High-frequency event study,Machine learning,ETFs |
| JEL: | G10 G14 C58 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:safewp:314&r= |