Irene Aldridge, a co-author of the forthcoming book “Big Data Science in Finance” has launched her very own YouTube channel where she discusses her latest research in the areas of Big Data, Artificial Intelligence and Finance. Please subscribe here to receive updates: https://studio.youtube.com/channel/UCMYuhgyMhzkw5tBIyEa2p3g Aldridge has a seasoned portfolio of TV appearances, including CNBC, CNN, and even Comedy Central. Aldridge is looking to make her research more accessible through video clips and offerings. Please share with your colleagues and friends!
By Irene Aldridge, co-author of “Big Data Science in Finance” (Wiley, 2020) The NYPost reported on November 5, 2020, just two days after the still-inconclusive U.S. Presidential Election, that “Bitcoin rallies past $15,000 for the first time since January 2018”. Bitcoin is just one of now many cryptocurrencies, “crypto” for short. Other cryptocurrencies, like Ethereum, XRP, Chainlink, and many others are surging as well, offering investors an opportunity for unparalleled returns. The surge in may seem random to some, but it also may have very strong fundamentals rooted in the current political landscape. This article makes a case for Crypto becoming a stronger performer in
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:
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 at
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 is
By Irene Aldridge Many ETF and active portfolio managers need accurate end-of-day price direction forecasts to optimally execute their daily portfolio reallocation requirements. Predicting the end-of-day direction of the price is crucial to solidify performance, as the last half-hour is known to be marked by extreme volatility. The end-of-day volatility, if met on the wrong side of the price movement, may reduce the performance gains of even the most seasoned and talented portfolio managers to rubble. The main challenge with predicting market direction at the close of the day is to estimate the objectives and motivation of market participants. To simplify, most market participants in