High frequency trading - page 9

Algorithmic Trading (AT) and High Frequency (HF) trading, which are responsible for over 70\% of US stocks trading volume, have greatly changed the microstructure dynamics of tick-by-tick stock data. In this paper we employ a hidden Markov model to examine how the intra-day dynamics of the stock market have changed, and how to use this information to develop trading strategies at high frequencies. In particular, we show how to employ our model to submit limit-orders to profit from the bid-ask spread and we also provide evidence of how HF traders may profit from liquidity incentives (liquidity rebates). We use data from February 2001 and February 2008 to show that while in 2001 the intra-day states with shortest average durations (waiting time between trades) were also the ones with very few trades, in 2008 the vast majority of trades took place in the states with shortest average durations. Moreover, in 2008 the states with shortest durations have the smallest price impact as measured by the volatility of price innovations.

Statistical arbitrage is a popular trading strategy employed by hedge funds and proprietary trading desks, built on the statistical notion of cointegration to identify profitable trading opportunities. Given the revolutionary shift in markets represented by high frequency trading (HFT), it is unsurprising that risks and rewards have changed. This paper explores the effect of HFT volume on statistical arbitrage profitability, and reports three trends in the data. First, higher levels of comovement due to HFT cause more stock pairs to be cointegrated. Second, profitability from statistical arbitrage remains steady among the deciles with the most HFT. Third, the range of profitability is larger in more recent years. These findings suggest that HFT increases correlation and volatility and have a direct impact on statistical arbitrage trading strategies. Number of Pages in PDF File: 36

This paper reviews the existing empirical evidence on the time-series behavior of the U.S. Treasury markets at high frequency: daily and intra-day data. The use of high-frequency data in econometric analyses is a major recent development in the study of the fixed income markets: the response of prices to scheduled and unscheduled news, conditional-volatility dynamics, and jump and diffusion behavior, can all be examined much more precisely with high-frequency data. High-frequency data are also important for the characterization of the trading environment as they allow us to examine the immediate impact of trading on prices and how this impact is affected by the presence of macro news. Lastly, the presence and impact of high-frequency trading can only be studied by analyzing high-frequency data. Number of Pages in PDF File: 69

We study the performance of mean-variance optimized (MVO) equity portfolios for retail investors, in various markets in the U.S. and around the world. Actively managed equity mutual funds have relatively high fees and tend to underperform their benchmark. Index funds such as ETFs still charge appreciable fees, and only deliver the performance of the benchmark. We find that an MVO is relatively easy to manage by a retail investor, and that they tend to outperform their benchmark or, at worst, equal its performance, even after adjusting for risk. Moreover, we show that the performance of these funds is not particularly sensitive to the frequency at which they are rebalanced so that, in the limit, an investor might have to rebalance her portfolio only once per year. This last finding translates into very low trading costs, even for a retail investors. Thus, we conclude that MVOs offer an easy, cheap alternative for a retail investors to invest in the world’s equity markets.

Regulation has long relied on securities exchanges to police the flow of capital in the economy. This Article shows that, because of recent regulatory policy, this dependence is deeply misplaced. Theory justifies a powerful governance role for exchanges on account of their capacity to gather a swathe of public companies and traders within their institution. Numbers allow exchanges to match traders, pool information, monitor expansively and to discipline bad actors through exclusion from an essential economic resource. This rationale no longer holds true in modern, fragmented markets. Rather than consolidate equity trading within a handful of exchanges, U.S. equity markets are defined by fierce competition for trades between several exchanges and around 45 less regulated, largely opaque, non-exchange venues known as "dark pools." This dynamic raises serious concerns for market governance. First, exchanges face far higher costs for and far lower returns from the effective performance of their governance role. Fragmentation institutionalizes information asymmetries in market structure. It raises monitoring and co-ordination costs to oversee multiple venues. And competition between these venues dramatically reduces the trading volume and profits on offer. Higher costs and lower returns sharpen conflicts of interest already endemic to the notion of relying on for-profit exchanges to oversee their customers. Secondly, an interconnected market of competing venues incentivizes exchanges to underinvest in governance. Expenditure in oversight benefits an exchange privately. But it also confers value on its competitors that can free ride off its efforts. Furthermore, in interconnected, fragmented markets, an exchange can gain by taking risks in providing oversight. It wins by lowering fees and capturing business. However, the full costs of its failure can be externalized and shared across many competing venues. The governance gap in fragmented markets is profoundly damaging for the regulation of capital in the economy. In recognizing the importance of private governance for efficient capital allocation, this Article concludes by exploring the creation of a new liability regime to align the incentives of trading venues more purposively towards better governance.

Previous research has identified that investors place more emphasis on technical analysis than fundamental analysis, however the research has largely been confined to daily data and stock market indices. This paper studies whether intraday technical trading rules produce significant payoffs in the gold and silver market using three popular moving average rules. We find that using the standard parameters previously used in the literature, technical trading rules offer are not profitable. However after utilising a universe of parameters, we find a number of parameter combinations offer significant profits in the gold market, but there remains no significant payoff in the silver market. Our results show that parameters that use longer histories are more successful than the traditional parameters chosen in the literature. Intraday technical trading rules can be profitable in the gold market but offer no significant profit in the silver market.

We examine the impact of dark trading and order flow segmentation on market quality in Canada. A new regulation requiring dark orders to offer price improvement over displayed prices virtually eliminated intermediation of retail orders in the dark, but did not impact other dark trading. Exploiting the differential reaction, we show that segmentation of retail orders harms lit liquidity. After the rule change retail traders receive less price improvement, retail brokers pay higher exchange fees and institutions experience lower lit and higher dark fill rates and higher implementation shortfall. High frequency traders earn higher fee revenues. Exchange revenues are unchanged.

This paper studies correlations between the strategies of high-frequency trading (HFT) firms, which is a manifestation of the extent of competition in which these firms engage when pursuing similar strategies. We use a principal component analysis to show that there are several underlying common strategies and that the competing HFT firms pursuing these strategies generate most HFT activity. We investigate whether competition between HFT firms creates a systematic return factor, but find no supporting evidence for such an influence. However, the short-interval return volatility of most stocks loads negatively on a market-wide measure of correlated HFT strategies. The mitigating impact of HFT competition on stock volatility appears to be driven at least in part by a market-making strategy. We further document a negative relationship between two forms of competition—that between HFT firms and that between trading venues. We investigate a potential driver behind this negative relationship, and show that greater HFT competition within a trading venue helps smaller trading venues become more competitive or viable in terms of posting better prices and narrower spreads.

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correlated_high-frequency_trading.pdf