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Interview with Adam Afshar, Hyde Park Global
In the latest in our series of interviews with High Frequency Trading Leaders, Mike O’Hara speaks with Adam Afshar, President and CEO of Hyde Park Global Investments.
Adam has over 2 decades of financial industry experience including 12 years at Bear Stearns where he was a Managing Director, overseeing long/short multi asset portfolios for both onshore and offshore clients. Hyde Park Global Investments is a 100% Robotic investment and trading firm based on Artificial Intelligence (AI). The system is built primarily on Genetic Algorithms (GA) and other Evolutionary models to identify mispricings, arbitrage and patterns in electronic financial markets. Additionally, Hyde Park Global Investments has developed programs applying natural language processing and sentiment analytics to trade equities based on machine readable news. Hyde Park Global employs no analysts, portfolio managers or traders, ONLY scientists and engineers.
Adam Afshar has served on the finance committee of the board of trusties of Wofford College and is currently member of the Board of Directors of Ariana Pharma in Paris, France. Ariana is a leading Artificial Intelligence company, focused on transforming data into information to accelerate the discovery, development process and safety of pharmaceutical drugs. Ariana is located in central Paris, and is a spinoff of the Institut Pasteur. Adam Afshar has a BA in Economics from Wofford College and received his MBA from the University of Chicago, Booth School of Business.
High Frequency Trading Review: Adam, what’s your definition of high-frequency trading?
Adam Afshar: It varies. It could mean anything from a sub-second holding period to one day. Internally at Hyde Park Global, we prefer to speak more in terms of holding period and its variance.
We find the term “high frequency” is only slightly more descriptive than terms such as “short-term investment”. It’s important to note here that whatever the definition, high-frequency trading is a means to implement the strategy and never the strategy itself.
HFTR: That’s interesting. That’s the first time somebody has said that.
Adam: Let’s look at who uses high-frequency trading: the first three groups that come to mind are statistical arbitragers, algorithmic execution and electronic market makers. Even at a superficial level it becomes evident that what these three groups have in common is a high level of engineering in their approach.
Engineers have a very difficult time with nonlinearity; engineering students learn very early on that they should try to work the non linearity out of their problem. A linear example is the volume knob on your radio. As you turn the volume knob, the radio becomes louder; non linearity is where you turn the knob a little and the volume goes up a 100 fold; you turn the knob even more and the volume goes down. So, basically high-frequency and low latency trading allows us to implement strategies which have significant amounts of engineering to capture short term and small inefficiencies in the market. In other word, high-frequency and low latency trading attempts to cut timeslots into small enough chunks so that in a particular time period, the data we are dealing with becomes linear enough for our model.
Professor Luboš Pástor of University of Chicago through Bayesian analysis has shown that uncertainty compounds as the time horizon grows longer. Perhaps a simple example would be the degradation of the quality of information such as news. If you have news that is driving your decision and this news is a week old, then it is very possible that it will not be very useful in your trading.
This same time degradation applies to the latency of price and volume quotes. It is easy to imagine that if there is significant latency in your quote delivery system, then you will have an ineffective market making operation.
HFTR: So where does artificial intelligence fit into the high-frequency trading space?
Adam: A prefatory note to disambiguate important concepts; in terms of what we at Hyde Park Global mean by artificial intelligence (AI). What we do not mean is a system that is trying to simulate human behaviour or human reasoning. I have seen several articles written in the financial press about a number of very large banks using AI in an attempt to replicate the behaviour of a human trader. At Hyde Park Global we find no reason to simulate human behaviour or reasoning, when we don’t even argue that the logic behind that reasoning is rational.
Now let us explain what are we do mean by AI at Hyde Park Global. To make better sense of the answer, let us first look at the issues that we face which compel us to use the combination of AI and a robotic platform in the first place. How do you make millions of calculations every second to monitor prices, volume and news on 3,000+ stocks every second with multiple and varying objective functions? Most people can appreciate that only a machine can look at and assess 3,000+ stocks every second and it is obvious to most people that no human trader, analyst or portfolio manager or even legions of traders can do that. If you have 1000 traders instead of one trader, you can do 1000x more calculations but you cannot do your calculations 1000x faster! You can read 1000x more news but you cannot read the news 1000x faster. So the next question is: how does the machine know what to do? The machine is assessing and analysing the news, price, and volume, but to what end? Here, scientists have to program the machine with an objective function or fitness function.
For example, we can ask the machine to give us the highest return (which, in our experience at Hyde Park Global is rarely an optimal objective) or, preferably, we can ask for the highest risk-adjusted return and define risk as volatility, Sharpe Ratio or Ulcer ratio or whatever you may find as the most appropriate fitness function.
To achieve this goal, our AI platform is given a set (or library) of analytic variables, from which it identifies relevancy, and a means of constructing automated trade decision logic from these relevant variables. The platform then attempts to maximize the objective function by tuning the logic and any unknown parameters in the model.
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