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on Market Microstructure |
| By: | Nidhi Aggarwal (Indira Gandhi Institute of Development Research); Susan Thomas (Indira Gandhi Institute of Development Research) |
| Abstract: | The causal impact of algorithmic trading on market quality has been difficult to establish due to endogeneity bias. We address this problem by using the introduction of co-location, an exogenous event after which algorithmic trading is known to increase. Matching procedures are used to identify a matched set of firms and set of dates that are used in a difference-in-difference regression to estimate causal impact. We find that securities with higher algorithmic trading have lower liquidity costs, order imbalance, and order volatility. There is new evidence that higher algorithmic trading leads to lower intraday liquidity risk and a lower incidence of extreme intraday price movements. |
| Keywords: | Electronic limit order book markets, matching, difference-in-difference, efficiency, liquidity, volatility, flash crashes |
| JEL: | G10 G18 |
| Date: | 2014–07 |
| URL: | http://d.repec.org/n?u=RePEc:ind:igiwpp:2014-023&r=mst |
| By: | Tobias R. Rühl; Michael Stein |
| Abstract: | In this study, we analyze the effect of US macroeconomic announcements on European stock returns, return volatility and bid-ask spreads using intraday data. We find that certain announcements are generally more important to the European stock market than others, and that the direction of news is important for returns. We provide first evidence that a stock-individual analysis is crucial to disentangle overall market reactions from stock-specific impacts and that effects vary dramatically between stocks. The analysis of quoted spreads reveals that return volatility affects the spread size positively, and that spreads are systematic ally higher directly after news releases. This is followed by structurally lower spreads, indicating quickly decreasing asymmetric information in the market after announcements. Additionally, spreads tend to react to announcements even if the returns or the volatility of the underlying stock is not significantly affected. This points at the importance of the analysis of news events beyond return and volatility analyses. |
| Keywords: | Macroeconomic announcement effects; european stock market; market microstructure; intraday analysis; bid-ask spreads |
| JEL: | E44 G14 G15 |
| Date: | 2014–08 |
| URL: | http://d.repec.org/n?u=RePEc:rwi:repape:0500&r=mst |
| By: | Wolfgang Karl Härdle; Andrija Mihoci; Christopher Hian-Ann Ting; |
| Abstract: | A flexible statistical approach for the analysis of time-varying dynamics of transaction data on financial markets is here applied to intra-day trading strategies. A local adaptive technique is used to successfully predict financial time series, i.e., the buyer and the seller-initiated trading volumes and the order flow dynamics. Analysing order flow series and its information content of mini Nikkei 225 index futures traded at the Osaka Securities Exchange in 2012 and 2013, a data-driven optimal length of local windows up to approximately 1-2 hours is reasonable to capture parameter variations and is suitable for short-term prediction. Our proposed trading strategies achieve statistical arbitrage opportunities and are therefore beneficial for quantitative finance practice. |
| Keywords: | multiplicative error models, trading volume, order flow, forecasting |
| JEL: | C41 C51 C53 G12 G17 |
| Date: | 2014–07 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2014-035&r=mst |
| By: | Tae-Hwy Lee (Department of Economics, University of California Riverside); Huiyu Huang (GMO Emerging Markets) |
| Abstract: | In prediction of quantiles of daily S&P 500 returns we consider how we use high-frequency 5-minute data. We examine methods that incorporate the high frequency information either indirectly through combining forecasts (using forecasts generated from returns sampled at different intra-day interval) or directly through combining high frequency information into one model. We consider subsample averaging, bootstrap averaging, forecast averaging methods for the indirect case, and factor models with principal component approach for both cases. We show, in forecasting daily S&P 500 index return quantile (VaR is simply the negative of it), using high-frequency information is beneficial, often substantially and particularly so in forecasting downside risk. Our empirical results show that the averaging methods (subsample averaging, bootstrap averaging, forecast averaging), which serve as different ways of forming the ensemble average from using high frequency intraday information, provide excellent forecasting performance compared to using just low frequency daily information. |
| Keywords: | VaR, Quantiles, Subsample averaging, Bootstrap averaging, Forecast combination, High-frequency data. |
| JEL: | C53 G32 C22 |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:ucr:wpaper:201409&r=mst |