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 S&P Global just announced acquisition of IHS Markit for a whopping US $44 billion, the largest acquisition in the financial data industry to date. The amount reflects the current reality on Wall Street: premium data brings on premium revenues and valuations follow. IHS Markit’s flagship offerings, like the ISM Manufacturing Index, has long been a beacon to selected hedge funds, who paid a fair share of their investors’ earnings for the opportunity to squeeze an extra edge out of the data. As shown in our book, “Real-Time Risk: What Investors Should Know About Fintech, Flash Crashes and High-Frequency Trading”, in many cases
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
Not to scare the humans. As many of us know, the state of AI in Finance is still often best explained by this meme: An interesting new study, citing, ahem, moi, however, suggests that machine learning is very capable of beating at least some traders. Specifically, the study applies modern methods to technical analysis, an old workhorse of many chartists, and finds that machine learning is certainly way better than manual labor. Here is the link to the study: https://www.hindawi.com/journals/complexity/2020/8285149/
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: