By Irene Aldridge In the Spring of 2013, a lively discussion on LinkedIn went like this: – “If someone says ‘Big Data’ one more time, I am going to throw up”, declared Head of Marketing at a prominent software firm – “Agree, ‘Big Data’ is such an annoying buzzword”, chimed in Head of Research at a mid-tier broker-dealer – “Uh, it’s such a fad”, stated a well-funded hedge fund manager. And so it went: “Big Data” is annoying, fleeting, and, by implication, useless. Fast forward to today, even though Big Data is a much more established term, eyes still roll when the subject comes up.
You are in an unfamiliar terrain, looking somewhat like a lunar landscape. Turning around, you observe craters and oddly-patterned star formations. Your hands are in thick blue gloves. All of a sudden, you hear a scream – a wild high-pitched tone. And then you see the source: a three-eyed creature rapidly approaching you. You panic and respond to the best of your ability: you rip off your helmet and gloves and return to your room. This is a realistic scenario from a virtual reality video game. The complexity of the 3-D simulation, aided by multiple data points and, increasingly, sensors from the player’s body, require
Adapted from “Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading and Flash Crashes” (with Steve Krawciw, Wiley, 2017) Big data is all the rage, but not without pitfalls. In fact, data analysis is subject to risks that may lead to poor inferences and bad decisions that follow. The process of analyzing data, regardless of complexity, can go off the rails on several fronts: A small data sample may pick up a pattern that does not recur on a sufficiently long timeline, misleading the researchers of the pattern’s power and predictability. Oversampling of data may occur when researchers torture the same sample of data
An Analysis of Institutional Activity in October 2016 Adapted from Real-Time Risk: What Investors Should Know About FinTech, High-Frequency Trading and Flash Crashes (forthcoming, Wiley, NJ). Pre-order on Amazon.com. “Money talks, bull*%$# walks”, says a classic Wall Street proverb. The expression has a lot of merit: nothing reflects one’s beliefs more than a financial bet on the markets. The larger is the bet, the stronger is the belief. AbleMarkets research indicates that institutional money sold off when negative news affected Donald Trump in October 2016, but when Hillary Clinton’s negative news emerged later in the same month, there was very little reaction from institutional money.
Minimizing volatility is important to investment managers focused on capital preservation. After all, lower volatility helps protect capital and improve the key portfolio performance metric, the Sharpe Ratio, which is equal to the average annualized return divided by annualized volatility. An acceptable Sharpe Ratio for a portfolio starts in the 1.8 range. Some high-frequency trading funds produce Sharpe as high as 20. Even very small positive returns can produce large Sharpe ratios that attract investors, but only if the volatility of the portfolio is tiny. Minimizing volatility is a challenging task. In a nutshell, to minimize volatility, one needs to: 1. Identify the conditions that
An interview with Prof. Marco Avellaneda, Professor of Mathematical Finance at New York University Courant Institute of Mathematical Sciences, as told to Irene Aldridge. With 2016 registering levels of volatility in the U.S. markets not seen for a long period of time, we sat down with the volatility expert, Prof. Marco Avellaneda of NYU Courant Institute for Mathematical Sciences to discuss what is underpinning developments in the markets. Prof. Marco Avellaneda is an internationally recognized figure in the field of computational volatility modeling. Some of his latest research will be on display at the Big Data Finance Conference to take place at New York University
Once upon a time, or, more precisely, just some 20 years ago, “data” was a term reserved for magnetic tapes and numerous governance committees, engaged in weekly discussions on the best ways to name data fields in order to accommodate the universe of financial products. Fast forward to today, and, surprise, many financial firms still engage in the same practices. Extensive data governance committees spar over what is the optimal way to write out a name of a listed option and how that should differ from the requirements of a custom “over-the-counter” derivative. Much of the latest CFTC Technology Advisory Committee (TAC) discussion focused on
The first iPhone was launched on June 29, 2007, and the world has never been the same since. The speed and convenience with which we now communicate created the new levels of urgency, including the urgency to understand and participate in further unbridled innovation. Since the launch of the iPhone, many companies have adopted the so-called Digital One company strategy with the idea to integrate social media, mobile technology, fast analytics and cloud data storage. Social media alone creates change and not just because of all the new tools connecting billions of individuals worldwide. People use social networks to gain immediate access to information that
JP Morgan’s announcement last week “to pull the plug” on all of their thousands and thousands of Bloomberg terminals is last week’s leading example of the sweeping disruption facing investment managers from innovation (the contract value exceeding tens of million U.S. dollars each month). Many still struggle to wrap their head around the situation with social media platforms like LinkedIn buzzing with discussions about whether pulling the plug on Bloomberg is feasible or advisable. Yet, here is fact: while Bloomberg rested on its laurels and celebrated its founder’s New York mayoralty, the competition was not sleeping, but working hard. And now, the competition is so
The new revelations surrounding the Flash Crash of May 6, 2010, once again brought to light an undeniable fact: U.S. regulators desperately need to boost their real-time surveillance capabilities. Nearly five years has elapsed between the time the London-based Navinder Singh Sarao, allegedly influenced the Flash Crash and the government identification of this event! Gone are the days when market issues could be analyzed by a team watching for on-screen images of market events. Regulatory agencies are ill-equipped to handle real-time issues in a timely manner. However, market solutions, such as AbleMarkets.com suite of real-time products are designed to spot market microstructure issues, such as