business models are being created to challenge established ways of doing business. For example, robo‐investing is a substitute for online brokers as well as full‐service brokers and financial planners. The idea has been around for a while; however, in the last five years the momentum has started to grow. According to Corporate Insights, robo‐advisers had gathered $20 billion in assets by the end of 2014, which is a small portion of the $24 trillion in retirement assets in the United States. The growth and the high‐profile venture capital funding of Betterment and Wealthfront have led players such as Vanguard to launch their own robo‐advisers. The growth of these companies is a topic the entire investment management industry is watching and the question becomes will the baby boom generation adopt this form of wealth management in their retirement or is this service geared to the millennials.
The innovation to use predictive technology is not just about consumer habits. Of course, future fintech solutions will churn through transaction history to spot trends and use that information to provide intelligent recommendations on decisions such as what credit card to pay off first, how much to put down on a home, or how to save for a new car. They'll even suggest things like whether it's better to buy or lease a car. However, the majority of changes from predictive analytics will occur at the institutional level, resulting in sweeping organizational and operational changes at most financial services.
For institutional asset managers, predictive analytics assess future volatility, price direction and likely decisions by fund managers. A pioneer in predictive analytics for investment management is AbleMarkets, which brings aggressive high‐frequency trading (HFT) transparency to market participants. AbleMarkets estimates, aggregates, and delivers simple daily averages of aggressive HFT so that professionals can improve their prediction of the market's reaction to events, assessments of future volatility, and shorter‐term price movement. It is used for portfolio management, volatility trading, market surveillance by hedge funds, pension funds, and banks.
What is different now? Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big data sets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Advanced databases and computer languages are required for most large data sets; after all, even the latest version of Excel stops at some one million rows. What if your data set contains five billion records? Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. A popular technique called principal component analysis does just that: it estimates clusters of properties common among the records. Those clusters next become important variables in slicing and dicing the data. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more selective ways to model complex relationships.
Конец ознакомительного фрагмента.
Текст предоставлен ООО «ЛитРес».
Прочитайте эту книгу целиком, купив полную легальную версию на ЛитРес.
Безопасно оплатить книгу можно банковской картой Visa, MasterCard, Maestro, со счета мобильного телефона, с платежного терминала, в салоне МТС или Связной, через PayPal, WebMoney, Яндекс.Деньги, QIWI Кошелек, бонусными картами или другим удобным Вам способом.
