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on Market Microstructure |
| By: | Rindi, Barbara (Bocconi University); Werner, Ingrid M. (Ohio State University) |
| Abstract: | The U.S. equity markets are currently conducting a pilot study of the effects of a larger tick size on market quality and on the rewards for liquidity provision. We show that the larger tick size causes quoted and effective spreads, but also depth, to increase. This raises the cost for retail-sized liquidity demanding orders by almost fifty percent. However, average trade size increases, suggesting that institutions may benefit from the deeper quotes. The larger tick size translates into forty percent higher profits to liquidity providers despite larger price impacts. We attribute these changes mainly to the changes in tick size for displayed quotes, while there are modest or no effects of requiring all trades to execute on a coarser price grid. Moreover, the bulk of the effects occur for tick-constrained stocks which trading costs more than double. By contrast, trading costs for unconstrained stocks decline by more than ten percent. Finally, we document significant spillovers to stocks with unchanged tick size. Our evidence suggests that some market makers left stocks trading in decimals for the more lucrative pilot stocks, and that the reduced competition causes quoted spreads and rewards for liquidity provision to increase also for stocks trading in decimals. |
| JEL: | G12 G14 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:ohidic:2017-18&r=mst |
| By: | Wonse Kim; Sungjae Jun |
| Abstract: | This paper investigates the effects of a price limit change on the volatility of the Korean stock market's (KRX) intraday stock price process. Based on the most recent transaction data from the KRX, which experienced a change in the price limit on June 15, 2015, we examine the change in realized variance after the price limit change to investigate the overall effects of the change on the intraday market volatility. We then analyze the effects in more detail by applying the discrete Fourier transform (DFT) to the data set. We find evidence that the market becomes more volatile in the intraday horizon because of the increase in the amplitudes of the low-frequency components of the price processes after the price limit change. Therefore, liquidity providers are in a worse situation than they were prior to the change. |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1805.04728&r=mst |
| By: | Ben-David, Itzhak (Ohio State University); Franzoni, Francesco (University of Lugano); Moussawi, Rabih (Villanova University) |
| Abstract: | Over nearly a quarter of a century, ETFs have become one of the most popular passive investment vehicles among retail and professional investors due to their low transaction costs and high liquidity. By the end of 2016, the market share of ETFs topped over 10% of the total market capitalization traded on US exchanges, while representing more than 30% of the overall trading volume. ETFs revolutionized the asset management industry by taking market share from traditional investment vehicles such as mutual funds and index futures. Because ETFs rely on arbitrage activity to synchronize their prices with the prices of the underlying portfolio, trading activity at the ETF level translates to trading of the underlying securities. Researchers found that while ETFs enhance price discovery, they also inject non-fundamental volatility to market prices and affect the correlation structure of returns. Furthermore, ETFs impact the liquidity of the underlying portfolios, especially during events of market stress. |
| JEL: | G12 G14 G15 |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:ohidic:2016-22&r=mst |
| By: | Huiwen Wang; Shan Lu; Jichang Zhao |
| Abstract: | The increasing richness in volume, and especially types of data in the financial domain provides unprecedented opportunities to understand the stock market more comprehensively and makes the price prediction more accurate than before. However, they also bring challenges to classic statistic approaches since those models might be constrained to a certain type of data. Aiming at aggregating differently sourced information and offering type-free capability to existing models, a framework for predicting stock market of scenarios with mixed data, including scalar data, compositional data (pie-like) and functional data (curve-like), is established. The presented framework is model-independent, as it serves like an interface to multiple types of data and can be combined with various prediction models. And it is proved to be effective through numerical simulations. Regarding to price prediction, we incorporate the trading volume (scalar data), intraday return series (functional data), and investors' emotions from social media (compositional data) through the framework to competently forecast whether the market goes up or down at opening in the next day. The strong explanatory power of the framework is further demonstrated. Specifically, it is found that the intraday returns impact the following opening prices differently between bearish market and bullish market. And it is not at the beginning of the bearish market but the subsequent period in which the investors' "fear" comes to be indicative. The framework would help extend existing prediction models easily to scenarios with multiple types of data and shed light on a more systemic understanding of the stock market. |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1805.05617&r=mst |