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on Market Microstructure |
| By: | Danial Saef; Odett Nagy; Sergej Sizov; Wolfgang Karl H\"ardle |
| Abstract: | While attention is a predictor for digital asset prices, and jumps in Bitcoin prices are well-known, we know little about its alternatives. Studying high frequency crypto data gives us the unique possibility to confirm that cross market digital asset returns are driven by high frequency jumps clustered around black swan events, resembling volatility and trading volume seasonalities. Regressions show that intra-day jumps significantly influence end of day returns in size and direction. This provides fundamental research for crypto option pricing models. However, we need better econometric methods for capturing the specific market microstructure of cryptos. All calculations are reproducible via the quantlet.com technology. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.09429&r= |
| By: | Ingomar Krohn; Philippe Mueller; Paul Whelan |
| Abstract: | We document that intraday currency returns display systematic reversals around the major benchmark fixings, characterized by an appreciation of the U.S. dollar pre-fix and a depreciation post-fix. We propose an explanation based on constrained intermediation by foreign exchange dealers. Exploiting data from a major inter-dealer platform, we present evidence of an unconditional demand for U.S. dollars at currency fixings. Dealers hedge this demand pre-fix, driving intraday reversals in both over-the-counter and exchange-traded markets. Furthermore, order imbalances in futures markets are not related to intraday reversal patterns, suggesting that the marginal investors in foreign exchange markets are intermediaries. |
| Keywords: | Financial markets; Exchange Rates; Market structure and pricing |
| JEL: | F31 G15 |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:21-48&r= |
| By: | Yufei Wu; Mahmoud Mahfouz; Daniele Magazzeni; Manuela Veloso |
| Abstract: | The success of machine learning models in the financial domain is highly reliant on the quality of the data representation. In this paper, we focus on the representation of limit order book data and discuss the opportunities and challenges for learning representations of such data. We also experimentally analyse the issues associated with existing representations and present a guideline for future research in this area. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.04752&r= |
| By: | Lin Li |
| Abstract: | We propose a novel portfolio trading system, which contains a feature preprocessing module and a trading module. The feature preprocessing module consists of various data processing operations, while in the trading part, we integrate the portfolio weight rebalance function with the trading algorithm and make the trading system fully automated and suitable for individual investors, holding a handful of stocks. The data preprocessing procedures are applied to remove the white noise in the raw data set and uncover the general pattern underlying the data set before the processed feature set is inputted into the trading algorithm. Our empirical results reveal that the proposed portfolio trading system can efficiently earn high profit and maintain a relatively low drawdown, which clearly outperforms other portfolio trading strategies. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.05299&r= |
| By: | David Ardia; Keven Bluteau; Kris Boudt |
| Abstract: | We conduct a tone-based event study to examine the aggregate abnormal tone dynamics in media articles around earnings announcements. We test whether they convey incremental information that is useful for price discovery for nonfinancial S&P 500 firms. The relation we find between the abnormal tone and abnormal returns suggests that media articles provide incremental information relative to the information contained in earnings press releases and earnings calls. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.10800&r= |