By Irene Aldridge This article first appeared in the Big Data Finance magazine Social media has fascinated Finance for about a decade. Extracting sentiment from online posts have proven to be both innovative for gauging investor sentiment and profitable for estimating direction of the impending price move and volatility. Companies like AbleMarkets, a Big Data platform and a supplier of Internet sentiment index for most U.S.-based stocks, and Suite LLC, an industrial-grade derivatives pricing and risk management software agree: Internet sentiment is highly predictive of impending volatility. In addition to social media sentiment, a new kind of social media is entering Finance as we know it:Read More →

By Irene Aldridge This article was first published on Medium Big Data, Machine Learning and Artificial Intelligence are three du-jour buzzwords of today’s business. If your business does not do one of the three, you risk being considered tardy, inefficient, or, gasp, uncool, particularly with the dreaded taste-making millennial set. Worst of all, you may miss the next chance of becoming a unicorn — a billion-dollar entity like Google and Facebook that deployed Big Data, Machine Learning and Artificial Intelligence techniques to turn reams of data points into solid gold. Various Big Data, Machine Learning and AI methodologies, trends and ideas, will be discussed in detail atRead More →

Irene Aldridge’s research, titled “Big Data in Portfolio Allocation: A New Approach to Successful Portfolio Optimization” has been published in Journal of Financial Data Science, edited by Frank Fabozzi, among others. Citation: Aldridge, Irene, 2019. “Big Data in Portfolio Allocation: A New Approach to Successful Portfolio Optimization.” The Journal of Financial Data Science Winter 2019, 1 (1) 45-63; DOI: https://doi.org/10.3905/jfds.2019.1.045/ Abstract In the classic mean-variance portfolio theory as proposed by Harry Markowitz, the weights of the optimized portfolios are directly proportional to the inverse of the asset correlation matrix. However, most contemporary portfolio optimization research focuses on optimizing the correlation matrix itself, and not its inverse. In this article, the author demonstrates that this isRead More →

By Irene Aldridge The last two weeks witnessed a somewhat forgotten phenomenon — a market headed south at a rapid pace for several consecutive days. Unlike flash crashes, brief spikes of downward volatility that can be predictable (see Aldridge, I., “High-Frequency Runs and Flash Crash Predictability”, Journal of Portfolio Management, 2014, and AbleMarkets Streaming Flash Crash Index), the sell-off of the past two weeks was methodical, slow and painful. AbleMarkets, a Big Data platform for finance, tracks institutional activity by pinpointing electronic algorithms used to break up large orders throughout the day. AbleMarkets uses the most granular tick-level data from exchanges to identify market microstructure footprints of institutionsRead More →

By Irene Aldridge Selling volatility has been a popular trading strategy among hedge funds over the past couple of years. At the core of the strategy’s popularity is the observation that volatility becomes considerably more severe when the markets are moving down rather than when they are rising up (see, for example, “The Cross-Section of Volatility and Expected Returns” by Ang, Hodrick, Xing and Zhang, Journal of Finance, 2005). In other words, selling volatility is a complicated way of betting on the rise of the market. During the current administration’s tenure, the U.S. markets have consistently risen, while dampening volatility in the process and generating excitement amongRead More →

In the next five years, big data analysis is poised to become one of the most important and competitive skill sets around. Portfolio analysis in particular is where pension funds are focusing their big data investments. Big data is a set of techniques embedded in the latest, most sophisticated technologies: social media analytics, digital video recognition, 5G cellular technology and much more. The capabilities of big data are incredibly powerful and extend far beyond traditional systems. Supported as a spying technology in the World War II and later, the Cold War, core big data techniques were developed in the 1940s, 1960s and 1980s and areRead More →

Irene Aldridge’s latest paper on Big Data optimization in portfolio management is the first to show that spectral decomposition of an inverse of the correlation matrix delivers 400% improvement over the equally-weighted and other common portfolio optimization schemes. Read more here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3142880Read More →

By Irene Aldridge Figure 1. A question for you: if you have 9 dots lined up in a 3×3 grid (Figure 1), how do you connect them all with just four segments without lifting your pen? This is an example of an out-of-the-box problem which kids love and which expands their sense of spatial coordinates and problem solving not usually taught at school. The Math & Science curriculum in early childhood education is focussed on understanding numbers — a great feat and a standard-meeting requirement. While mastering basic arithmetic is necessary, what makes you more interested in learning math & science: 1) reciting arithmetic orRead More →

By Irene Aldridge The word on the street is that no indicator is more reliable about one’s beliefs of future economic conditions than one’s trading activity. It is fascinating to observe institutional investor activity immediately before and after the announcement that the U.S. Senate has conditionally passed the proposed new tax law in the context of understanding the thoughts and beliefs of institutional investors as expressed by their often billion-dollar investing decisions. The new tax regime slashes corporate tax rates from 35% down to 21%, potentially resulting in direct increase in after-tax earnings, and, therefore, stock prices in 2018. From a cursory analysis, it wouldRead More →