摘要： Right around the time you get your first basic regression or classification model going, it will at least cross your mind. The vast piles of time series data, coupled with the possibility of retiring young has the irresistible pull of finding an old treasure map in your grandfather’s attic. How can you NOT think about it? Can you use machine learning to predict the market?
摘要： The short answer to this question is: It depends! The longer answer is different for different investors and trading signals. That, in turn, means issues like long queues, invested venues and speed bumps matter more (or less) depending on how you need to trade.
摘要： There are many challenges associated with performing algorithmic trades in the real world. Despite these challenges, there are a dozen well-known names in the algorithmic trading world and more players are trying their hands in this business every year.
摘要： Regulators are pushing brokers to disclose more information about how and why they route equity trades. Some industry practitioners want to go a step further.
摘要： We sat down with an algorithmic trader to learn more about how algorithms are remaking the industry, and why it matters. We talked about what algorithmic finance actually looks like, who the winners and losers are likely to be in the new big data gold rush, and why we may be entering an era of irrational cyborg exuberance.
摘要： The stellar growth of ETFs is something we have discussed before. However, in that study we actually found that ETF turnover is falling, resulting in a far more gradual increase in ETF value traded than previously expected.
摘要： Snapchat recently announced upgrades to its ad strategy with the incorporation of an artificial intelligence platform known as “goal-based bidding,” which allows advertisers on Snapchat to target their messages more effectively. And by allowing users to swipe ads and dive deeper into the journey, it increases the potential for more consistent and continued consumer engagement.
摘要： Big Data has swiftly become a dated, catch-all phrase in the online marketing lexicon, but it is undeniable that the amount of data and the speed at which is delivered, is still increasing at an exponential rate. For example, Gartner has predicted that enterprise data will grow 650 percent between by 2020, and IDC has stated that the world’s information now doubles approximately every 18 months.
摘要： While volatility and illiquidity in the U.S. equities markets have created obstacles to low-cost transacting, trading innovations being put in place in 2019 are now making liquidity more efficient and accessible to buy-side firms that continue to invest in technology, according to new research, IET 2019, Liquidity: Blocks, Algos, Analytics and Impact, the first of a six-part US equities trading interview-based benchmark study that has been published annually by TABB Group for 15 years.
摘要： When first introduced, algorithms were designed primarily for automation to mimic a trader executing orders in pursuit of specific benchmarks. In the second phase, brokers stressed qualitative analysis by leveraging real-time data from the order book to model their assertions, and tailor how model behavior would respond to changing market conditions. In the most recent phase, leading providers on the sell-side have begun to use quantitative measures into their execution strategies, most notably integrating machine learning principles.
摘要： The integration of environmental, social, and governance (ESG) considerations into passive investing is gathering momentum, but there are potential pitfalls ahead, according to the latest The Cerulli Edge—Europe Edition.
摘要： Fundamentally, trading is about analyzing the supply and demand of a security (asset which can be traded), such as stocks, commodities, or Forex pairs. A trader then makes decisions to purchase or sell these securities, ideally for a profit. When entering a trade, there are numerous factors to take into consideration, such key price levels, liquidity, and momentum.
