Posts Tagged ‘high frequency


Paper: Hedge Funds, exposiciones, riesgos

On the High-Frequency Dynamics of Hedge Fund Risk Exposures

We propose a new method to model hedge fund risk exposures using relatively high frequency conditioning variables. In a large sample of funds, we find substantial evidence that hedge fund risk exposures vary across and within months, and that capturing within-month variation is more important for hedge funds than for mutual funds. We consider different within-month functional forms, and uncover patterns such as day-of-the-month variation in risk exposures. We also find that changes in portfolio allocations, rather than changes in the risk exposures of the underlying assets, are the main drivers of hedge funds’ risk exposure variation.

Link al Paper


Gráfico du Jour: HFT Bots

(Fuente: SEC, via WSJ)


Paper: Modelos HFT

Developing High-Frequency Equities Trading Models

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Llegamos a este paper via Quantivity, uno de los co-autores es un argentino suelto en NY, esperamos que cuando este en Buenos Aires y tenga tiempo nos pueda presentar su trabajo sobre HFT.


Paper: Flash Crash, el impacto del HFT

The Flash Crash: The Impact of High Frequency Trading on an Electronic Market

The Flash Crash, a brief period of extreme market volatility on May 6, 2010 raised questions about the current structure of the U.S. financial markets. We use audit-trail data to describe the structure of the E-mini S&P 500 stock index futures market on May 6. We ask three questions. How did High Frequency Traders (HFTs) trade on May 6? What may have triggered the Flash Crash? What role did HFTs play in the Flash Crash? We conclude that HFTs did not trigger the Flash Crash, but their responses to the unusually large selling pressure on that day exacerbated market volatility.

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Gráfico du Jour: HFT en el tiempo

(Fuente: JP Morgan, via FT Alphaville)


Paper: HFT y su impacto

High Frequency Trading and its Impact on Market Quality

This paper examines the impact of high frequency traders (HFTs) on equities markets. I analyze a unique data set to study the strategies utilized by HFTs, their profitability, and their relationship with characteristics of the overall market, including liquidity, price efficiency, and volatility. I find that in my sample HFTs participate in 77% of all trades and that they tend to engage in a price-reversal strategy. I find no evidence suggesting HFTs withdraw from markets in bad times or that they engage in abnormal front-running of large non-HFTs trades. The 26 high frequency trading (HFT) firms in the sample earn approximately $3 billion in profits annually. HFTs demand liquidity for 50.4% of all trades and supply liquidity for 51.4% of all trades. HFTs tend to demand liquidity in smaller amounts, and trades before and after a HFT demanded trade occur more quickly than other trades. HFTs provide the inside quotes approximately 50% of the time. In addition if HFTs were not part of the market, the average trade of 100 shares would result in a price movement of $.013 more than it currently does, while a trade of 1000 shares would cause the price to move an additional $.056. HFTs are an integral part of the price discovery process and price efficiency. Utilizing a variety of measures introduced by Hasbrouck (1991a, 1991b, 1995), I show that HFTs trades and quotes contribute more to price discovery than do non-HFTs activity. Finally, HFT reduces volatility. By constructing a hypothetical alternative price path that removes HFTs from the market, I show that the volatility of stocks is roughly unchanged when HFT initiated trades are eliminated and significantly higher when all types of HFT trades are removed.

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Círculos en los Cultivos, por HFT

Nanex tiene breve e interesante estudio sobre un “fenomeno” que ellos llaman Quote Stuffing, pero que se da en secuencias poco convencionales (que no siguen una lógica económica).

As we continue to monitor the markets for evidence of Quote Stuffing and Strange Sequences (Crop Circles), we find that there are dozens if not hundreds of examples to choose from on any given day. As such, this page will be updated often with charts demonstrating this activity.

The common theme with the charts shown on this page is they are obviously all generated in code and are algorithmic. Some demonstrate bizarre price or size cycling, some demonstrate large burst of quotes in extremely short time frames and some will demonstrate both. In most cases these sequences are from a single exchange with no other exchange quoting in the same time frame.

NASDAQ “Ask Mountain”. Symbol IAU. Over 56,000 quotes in 10 seconds, all with same Ask Price and the Ask Size increasing or decreasing by 1 (to almost 40,000!).


Las acciones favoritas de los HFT traders

(Fuente: Zero Hedge)


Regulación Financiera, dark pools y flash trading

Vox tiene un post sobre un tema donde la libre y la tortuga no condicen con su Fabula.


According to the Securities and Exchange Commission, the number of active dark pools dealing in stocks on major US stock markets trebled to 29 in 2009 from about 10 in 2002. For April to June 2009, the total dark pools volume was about 7.2% of the total volumes of all US exchanges.


Dark pools are a private or alternative trading system that allows participants to transact without displaying quotes publicly. Orders are anonymously matched and not reported to any entity, even the regulators (Younglai and Spicer 2009). Thus, the mainstream exchange-traded market does not have any clue about the volume of transactions happening in this parallel market or the prices at which they are being executed. Obviously, price discovery on the mainstream market, without dark pools information, becomes inefficient.


But what is unacceptable is the practice of allowing a privileged minority to flash trade to track the reaction with high-speed processing capacity and the algorithm that can take advantage of the reaction to reap benefits – as this is akin to front-running. It is an example of high-frequency trading system with knowledge of asymmetric information that confuses common investors by simultaneously issuing and cancelling orders and entices them to shell out more for a particular security and, thus, squeezes out their profits.

It was the Chicago Board Options Exchange which pioneered flash orders early this decade to increase its execution speed (Patterson et al. 2009). Flash orders remained in the dark until a newspaper report in 2009 blew the whistle on how Goldman Sachs had made a killing through this route. According to Rosenblatt, flash trading accounted for about 2.4% of the total US stock volumes in June 2009.


Por ultimo, una de las fuentes de este articulo es un trabajo de PWCDark Pools of liquidity


Paper: Trades, Inversores e información

To Trade or Not to Trade? Informed Trading with High-Frequency Signals for Long-Term Investors

When a long-term investor trades a slowly changing portfolio, she is not very time sensitive to when exactly she should place or change her bet. The value of the embedded optionality provided by this flexibility may be extracted by using high frequency information to choose when to trade (exercise the option). Mechanically, if the short-term view concurs with the trade, then the trade is placed. Otherwise, the investor waits for a more favorable environment. Strategic trade modification provides exposure to short-term signals without having to pay additional transaction costs and with no capacity limits. We implement our trading approach (Informed Trading) on real and simulated portfolios to illustrate its effect on portfolio performance. We show that realistic portfolios can achieve a five percent exposure to a high frequency signal. Long-term investors should no longer ignore high frequency information just because it is too expensive to trade on.

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Fun & Finance


Fun & Finance Rollover

"It is hard to be finite upon an infinite subject, and all subjects are infinite." Herman Melville

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July 2020



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