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What Data Feeds Your Robo-Advisor?

Xignite

Article by Efi Pylarinou

Much like vegetables, we should all be concerned with the Kind of Data and the Quality of Data. Choices of data (and veggies) abound and we need to pick the appropriate set-combo. Quality of Data is a more complex issue that troubles mostly risk managers and regulators but should also be of great concern to the broader investment ecosystem. These issues – Picking Type and Checking Quality of Data – affect increasingly our risk-adjusted returns and their properties over time.

In this post, we will focus on the conventional investment subsector and explore what types of Data robo-advisors of all sorts use or will invest in. In future posts, we will look at the same issue – Data gathering and Quality control – in other WealthTech sectors, like marketplace lending and private markets. If you want to start a conversation on these topics, click here.

The conventional investment subsector operates mainly with publicly traded securities and has been focusing mostly on individual stocks and wrappers like ETFs, mutual funds, and only lately PTFs that provide exposure to other asset classes (e.g. fixed income, real estate, digital currencies, or private equity).

Primary Sources of Basic Data

Stock exchanges have traditionally been feeding the market with basic, fundamental data for stocks and ETFs. In our coverage of the innovations out of stock exchanges, we looked at the areas of digitization and realized that focus is either in clearing and settlements or in private markets. In the area of primary data, we only note the adaptation of XBRL for financial reporting.

The Fintech Sandbox in Boston, is a non-profit that has been facilitating access to such basic quality data for startups to test their MVP reliably without incurring the usual costs. Xignite is the major disruptor in the space of primary financial data.

All robo-advisors use this type of data.

Secondary Sources of Data

Conventional Financial Data

Data related to mutual funds and indices, which at their core are nothing more than a portfolio of individual holdings, are the next kind of Data that the industry consumes insatiably. Lipper’s, Thomson Reuters, Bloomberg and Xignite, are the big providers in this space.

The rich variety of this kind of Data, starts with Fund Flows, insider activity, fund classification (e.g. value, growth, small cap etc), expense ratios, performance measures (e.g. Beta, volatility measures, return-to-vol etc), historical benchmarking, etc.

In this category, we include financial data that is used as an input for equity valuations, and projections on whether a stock is overvalued or undervalued. Data therefore, with estimates of earnings, revenues-sales, and growth, which traditionally have been provided by financial analysts on the Street. Estimize is the leading Fintech in this space, offering crowd sourced estimates for stocks.

Unstructured non-financial Data 

This is any data beyond the usual company filings. It can be web site traffic and mobile access for online businesses, parking lot traffic for physical locations, human resources data (turnover), twitter trending key words, drug pipeline for pharmaceuticals etc.

1010Data, has been offering such data to hedge funds for both equities and fixed income.Thinknum is a Fintech that focuses also in this space.

Sentiment Data

Using sentiment data (i.e. optimism, fear, trust, capitulation, etc), to drive investment decisions, is an alternative way that will eventually be combined with the other kinds of data.

In our two part coverage of the Sentiment space, we identified a couple of early partnership that are combining primary and secondary data types (e.g. Thomson Reuters with Amareos, E-trade with TipRanks, Ameritrade with Likefolio). Since this is a nascent space, we have opened a conversation on the Fintech Genome that tracks the developments. Edit the wiki or simply post in this live conversation that monitors the adaptation of sentiment data and analysis into the mainstream.

There aren’t any robo-advisors using yet this type of data. Currently robo-advisors don’t allow for a scenario “Go cash, don’t invest now” or combining momentum with fundamental and therefore, sentiment data doesn’t add value.

Model generated Data – Alpha-generated, AI & ML

Using model-generated data to rebalance a robo-advisory portfolio is the next trend that we anticipate. We foresee, that the robo-advisory space will be moving from a primitive MPT portfolio-rebalancing method, to one that is dynamic and uses data generated by models to enhance the rebalancing and also allows for “I am out of the market for now”.

 In summary, robo-advisors are mainly using the primary resources of data. Any robo-advisors using any of the secondary sources of data?

Alpima is producing and using model generated data. They are actually offering a rich variety of model generated fundamental data. Similarly, Elm Partners is using Value-momentum models; combining fundamental analysis with momentum-technical signals. Logical Invest, is a Fintech focused on signal providing for financial advisors and therefore, producing model generated data.

Quandl is producing unstructured non-financial data, sentiment data, and alpha generating data.Their offering is rich and is coined, Alternative Data; and for now is referred to as “unorthodox data”. Is Quandl, the alternative for Yahoo Finance? Join the live discussion on this topic here.

Source: Daily Fintech

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01/25/2021

Xignite, Inc., a provider of market data distribution and management solutions for financial services and technology companies, today revealed the results of its collaboration with StockCharts, a leading technical analysis and financial charting platform for online retail investors. The collaboration involved a move from an on-premise market data provider to Xignite’s cloud-native technology hosted in Amazon Web Services (AWS). Download the case study containing the full results.

StockCharts requires vast quantities of financial data to power its visualization, charting and tracking tools, which investors use to analyze the markets to help with investment decisions. The company was frustrated by the limits of its on-premise market data center, which was forcing the team to make architectural decisions based on what its data center could handle in terms of speed and storage, not on their technology. Its previous market data provider was just starting to build out some cloud offerings, but they were far away from what the business required. StockCharts decided to migrate its infrastructure to the AWS cloud and partner with Xignite to gain access to endlessly scalable market and financial data delivered through innovative cloud APIs.

The collaboration made an immediate impact as StockCharts was able to expand its offerings and customer base by pursuing growth strategies enabled by Xignite’s cloud-based approach, which provides easy access to data and eliminates architectural limits on storage and speed.

The pandemic provided further validation. Seattle-based StockCharts was in one of the first areas hit by COVID-19 and was forced to quickly shut down its office. Pandemic-driven market volatility followed and StockCharts customers wanted to visualize what was happening. The company’s ability to scale quickly and accommodate a high volume of new requests would not have been possible without Xignite.

“The move to the AWS cloud and Xignite has unlocked tremendous new potential for us in a lot of architectural ways, and has given us a lot of data options that we could not even consider before,” said Grayson Roze, Vice President of Operations at StockCharts. “It relieved us of the burden of figuring out how to source things. Instead, we know exactly where we need to go to get the data and can access it instantly. That is a huge, huge benefit for our business.”

“We are proud to have played a role in transforming how StockCharts approaches data,” said Stephane Dubois, CEO and Founder of Xignite. “The events of this year unleashed a massive spike in retail trading and a host of other unexpected forces that reinforced the need for financial services firms to leverage the cloud. Despite the disruption of this year, StockCharts was positioned for success, and we look forward to continuing to deliver our financial and market data solutions to the industry at large.”

Xignite

Xignite has been disrupting the financial and market data industry from its Silicon Valley headquarters since 2006 when it introduced the first commercial REST API. Since then, Xignite has been continually refining its technology to help fintech and financial institutions get the most value from their data. Today, more than 700 clients access over 500 cloud-native APIs and leverage a suite of specialized microservices-delivered modules to build efficient and cost-effective enterprise data management solutions. Visit http://www.xignite.com or follow on Twitter @xignite

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