蒂姆·林德, 存常务董事，数据服务, explains how firms can access more data than ever before and describes an ideal data set for buy-side firms.
In an investment climate where passive management through index funds and ETFs continues to capture an increasing share of US investment dollars, are there new sources of data to help active management outperform market benchmarks? And as the availability of new alternative data products provide different perspectives on market activity, 资产管理公司会抓住这些新机遇吗?
买方公司的管理策略的关键是拥有最好的, most comprehensive and innovative market and reference data possible to feed their proprietary sentiment models. 新的数据来源可以帮助主动型基金经理获得优势, coupled with advanced technologies and analytics designed to provide more insightful analysis, 准备提高对冲基金和其他买方公司的能力吗.
The top story around retail equity investing over the past decade has been the dramatic rise of passive management: according to Bank of America Merrill Lynch, 截至2018年5月, 45%的美国股票资产属于被动投资, 而十年前这一比例约为25%.
According to Morningstar Manager Research, “Passive investment vehicles gained £19 billion ($28.8 billion) in 2019, while £32 billion was redeemed from active vehicles during the year.”
同时, investment managers on the institutional side have also struggled with performance on behalf of their client base of high-net-worth individuals, 大型养老基金, 主权财富基金和其他大型基金. 市场波动的爆发可能会给积极管理型基金带来提振, 因为这样的时期往往是这些基金大放异彩的时候. 在相对平静的时期, 相比之下，是2018年1月至11月, 例如，对冲基金平均损失2%, 据研究公司HFR称.
而被动投资似乎仍受散户投资者的欢迎, given how it has lowered fees significantly while cost-conscious millennials become an increasing share of the investing public, the direction in active management strategies for institutional investors is to bolster the power of their quantitative and algorithmic models with data that can illuminate important trading trends.
数据提供
According to the reporting of the Wall Street Journal, 31% of all investing is now quant-driven.
Firms can now access aggregated market data on trading activity in key markets, 按安全类型和划定的时间段. They can track trading volumes by security for slices of market participants – brokers most active in specific securities, 例如. They can drill down into commercial paper and institutional certificates of deposit settlements and track position data on global credit derivatives transactions.
这些数据可以使交易者, portfolio managers and research analysts to identify salient trading trends and market risk indicators that may have been harder to spot in the past.
达到最佳表现的能力在很大程度上取决于深度, breadth and quality of data an asset manager can access and utilise in its in-house trading models. 用算法以毫秒的速度分析数据, 这些模型的输入是企业可以找到自己优势的地方.
The two primary sources of such data are the in-house investment book of record, 通常称为银行同业拆放, 显示经理的综合现金和证券头寸, and external trade and reference data that provide insights into market momentum, 流动性和情绪. 鉴于美国股票交易场所的分散性, consolidated sources of market information have until now been difficult to obtain and challenging to compile, but some new data offerings and tools are emerging that may help overcome these obstacles.
An ideal data resource for buy-side asset managers and traders would be a kind of seismograph of the US equity markets revealing factors such as liquidity patterns, 证券卖空趋势, 以及主要经纪交易商的交易集中度.
A wishlist of information such a resource would provide might include the following:
- Consolidated equity market summary – A broad view of market activity combining trade data from all US exchanges, alternative trading systems and dark pools – saving users from collecting and organising this voluminous information manually. To present the data in a single, standardised format would be an extra benefit.
- Market sentiment – A view across securities’ buy, sell, sell-short, and sell-short-exempt trades. Seeing the differences in trade types over time helps users understand the market sentiment towards a stock and whether that sentiment is trending positive or negative.
- Liquidity – Volume statistics and sub-category data to indicate how many brokers are trading large volumes of particular stocks.
This data informs institutional investors looking to make large investments in a stock, 或者相反，退出现有的位置, about the number of brokers trading large enough volumes to deliver on their needs.
主动管理的回归
Low-cost passive investment strategies have flourished in a ten-year bull market cycle, 但随着更加动荡的投资环境可能即将到来, 对主动管理vnsr威尼斯城官网登入的需求可能会开始上升.
The power of these models can be further amplified by applying these technologies to process and analyse expansive, 如上所述的多角度交易数据. 在一个每个回报基点都很重要的环境中, firms will also need to optimise their understanding of liquidity and limit the market impact of trading in and out of positions.
Advancements in data science and AI increasingly have created sophisticated trading models that will become more pervasive; the availability of accurate and consolidated sources of data will ultimately determine if they are effective in driving new insight on the kinetics of equity markets.
Mastery of the new sources of market data and the technologies that mine and package it can become a distinguishing value proposition for buy-side firms and help data-driven active strategies make a comeback. 客户会注意到并要求更多.
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