Web Data for Investment Management

Transforming Investment Management

Not long ago, most investors had to wait until companies reported quarterly earnings to get a sense of how they were performing. Corporate CEOs would host investment calls and the investment community would listen closely for clues and data points to put into their models and determine what way the stock price would likely go. This was a traditional use of data to inform the investment decision.

Today, the transparency and ease of data collection coupled with advances in computing and machine learning, means early insights are that much closer and easier to access.

Fund managers who fail to beat their benchmarks need to quickly adapt or risk losing investors. Beating benchmarks is extremely difficult and professional hedge fund managers are always searching for new sources of alpha.

As hedge funds, institutional investors, traders, and other investment professionals are looking for their unique edge to improve alpha, alternative data generated through digital activities is emerging as the new definitive source.

The prevailing belief is that this data and the predictive power it promises is the most powerful alpha source.

What & Why Alternative Data?Alternative data refers to data used by investors to evaluate a company or investment that is not available from traditional market data sources. Alternative data helps investors get more accurate, faster, or more granular insights and metrics into company performance than traditional data sources.

In an industry where everyone has access to the same core financial and fundamental data, sophisticated investment managers are increasingly augmenting their decision making processes using alternative data sources. Most asset managers see direct benefit from using this information to improve their alpha generating capacity to bolster the performance.

Alternative data usage is on the rise and so is its cost

In recent years, banks and investment firms have increased their spend and consumption of alternative data sets, but there are still challenges around obtaining the most relevant data for their needs. Today, alternative data’s adoption is at a tipping point and their use is growing exponentially.

Recent surveys have shown that investment firms are spending well into 6 figures for a single data set per year while allocating more resources and budget with a third reserving over $1 million annually for alt data.

surge in the number of data

buyers with a data budget ⇡8.8%

alt-data budget for 33% of

data buyers $1M+

average price for each alt

dataset per year $50-120k

expect prices to increase or

remain the same >90%

$

The Alt Data GapA recent research report conducted by BattleFin and AlternativeData.org has found that while the alternative data landscape is exploding there is a gap between what data vendors offer and what data buyers actually want.

Data buyers view web data and social sentiment as two of the top three most valuable types of alternative data of which none are on the data providers top offerings.

Data buyers are also reporting dataset prices averaging between $50k and $120k each per year. And, even at these prices, data providers are still not giving buyers what they need.

Source: Opimas Analysis

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Internal Spend External Spend

Web data is seeing the same surge in spending

Total Spending YoY growth ⇢35%

Spending on external providers ⇢74%

Total Spending by 2020 >$7B

The scope of web data used by financial professionals has become more diverse, including job listings, social media posts and product pricing. Applications of artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are giving companies the ability to consume vast pools of data to make predicting future trends and formulating investment decisions more feasible.

Web data brings opportunities for fund managers to gain an informational advantage over its peers. The main appeal of web data is that it has the potential to offer information that other market incumbents simply don’t have. The data is often in disparate locations, with varying levels of quality. It is this obscure nature of web data that make its high value.

No doubt that alternative data is transforming the investment management processes for asset managers and web data is leading the way in driving this digital transformation to provide strategic opportunities.

Common Uses of Web Data• Market data aggregation: Market information is freely available on the web

but spans across hundreds of websites. Receive corporate operational data and eliminate the need to comb through multiple websites and online databases.

• Price Monitoring: Track prices and inventory levels to monitor supply, demand, and consumption as well as shifts in inflation across the globe.

• Customer sentiment: Financial professionals are leveraging social media to predict how the activity and buzz around a specific stock or product to identify potential market moving activity.

• Competitive Analysis: Competitive analysis can apply to numerous industries, including equity research and investments. Figure out how companies in your sector stack up against each other.

• Financial statement extraction: Getting a financial statement into a usable format for analysis can be daunting. Analysts need hundreds of financial statements to compare data for clients.

• News aggregation: By extracting headlines and article copy and using that data for predictive analysis, investment firms gain valuable insights into trends, events, and shifting views that affect the companies and products they are tracking.

• Back Testing: Capturing and storing historical web data enables backtesting to estimate the performance of a strategy or model if it had been employed during a past period.

High perceived business & legal risk

Stand alone data

Time consuming to get new data

Resource intensive & specialized skills

Poor quality, unreliable, dirty data The traditional method for extracting data from websites and making it machine-readable is known as web scraping. Web scraping projects are complicated, expensive and labor-intensive. They require organizations to employ engineers to write custom software for every type of web page that they want to target.

In addition to engineers, subject matter experts are needed to conduct extensive quality checks.

Web scraping projects are also not resilient to change on the target websites and they break easily due to its rigid extraction rules that are hard-coded.

Invariably, traditional web scraping leaves organizations with data that’s incomplete, inaccurate, unreliable, and out of date—while introducing high costs and business risk.

Web Scraping Challenges

Web Data Integration is a new approach to acquiring and managing web data that focuses on data quality and control. Web Data Integration treats the entire web data lifecycle as a single, integrated process composed of the following steps:

• Identification of data sources and requirements.

• Web data extraction.

• Data preparation and cleansing.

• Data integration and consumption by downstream applications and business processes.

• Analysis and visualization.

The purpose of adopting a Web Data Integration strategy is to allow the enterprise to build products and services on web data with confidence and without worrying about data quality or reliability issues.

Identify

Extract

Prepare

Integrate

Consume

High Quality

Timely

Low Risk

Comprehensive

Web Data Integration as a Service

Getting the required data from a Web Data Integration provider is by far the best way to extract data from the web. You get all the benefits of a Web Data Integration platform without the responsibility of extractor setup, maintenance and quality inspection of the extracted web data.

ü Scalable to your needs

ü Unique to your requirements

ü Quality to your standards

ü Timely so you can action

ü Relevant so you can focus on your core business

Alternative data sets fueled from web data

Sample Use Cases

Web data allows equity analysts to create unique datasets that power original, high-value research

A fund manager is building an in-house “alternative data” platform to provide all of their equity analyst teams easy access to the unique datasets that will distinguish the firm from competitors.

Historically the only sources of data that analysts could use were the datasets available through data providers.

The company found that the exact data needed for analysis was rarely available from data providers, and when it was available it was less valuable because it was available to all competitors.

As an example, the tire market is worth over $100bn and there are multiple public companies in the space. The fund manager has analysts dedicated to the market but there is not even a single data provider in the space.

Using web data, the company has been able to perform bottom-up “basket analysis” on the tire market, working out the number of products available and their average prices etc.

Tracking product volume & average price

Top 5 hedge funds in AUM tracks Invisalign partner dentists in order to project company earnings

Increasing consumer awareness for dental health is a factor fueling the growth of the “clear aligners” market in the US.

Invisalign is the market leader in this industry but is facing new competition from the likes of SmileDirectClub, Candid and others. Invisalign recently reported slowing sales and earnings growth and the hedge fund wanted to better understand the components that were contributing to this.

Hedge fund is using Import.io to track how many doctors are registered Invisalign providers and their respective service-provider tier (Gold, Silver Bronze etc). This is a real-time, leading-indicator of future revenue and allows the hedge fund to predict the future growth of Invisalign and to better understand the “clear aligners” market overall.

Monitoring the adoption of Invisalign amongst dentists

Investment fund monitors online blogs and forums to understand adoption of new HIV treatment drug

Biktarvy is a HIV treatment drug, developed by Gilead Sciences that received approval for use in the US in 2018. It is potentially a very important new drug in the fight against HIV and as such is important to the future success of Gilead.

In order to understand the adoption rates and sentiment towards the drug amongst the patient community, the investment manager monitored news and opinions shared openly and publicly in online discussion forums that discussed Biktarvy.

NLP techniques were used on the extracted web data to gain insight into community sentiment, popularity, influence and usage as well as to build models that would project future sales volumes of the drug.

Predicting drug adoption in the HIV community

Large Investment Management Firm monitors Chinese drug approvals to predict pharmaceutical revenues.

Each of the 23 provinces in China has its own food and drug authority that is responsible for the control and supervision of prescription drugs.

The successful launch of a pharmaceuticals product in China depends upon the product securing a registration with each provincial drug authority.

The firm was looking to invest in shares of Baxter Healthcare, a US pharmaceuticals company with a large production and consumer base in China.

New drug approvals are announced daily and the firm were able us Import.io to track approvals for Baxter products in real time.

Monitor drug approvals in China to predict drug launch success

Investment Bank identified structural weakness in Lululemon based on over 2 years worth of web data. The branded apparel team at a large investment bank have been using Import.io to build a database of product prices and product reviews for over 2 years and it powers a lot of their research.

In 2015 and 2016 Lululemon reported 20% year-on-year revenue growth.

However the banks’ analysts were able to show that this growth was not due to an increase in demand; instead they could see that over the same period Lululemon had simply increased average product price by 20%.

Combining this insight with other web data relating to product reviews, discounting strategies and new market entrants the investment bank changed its rating of Lululemon to a “sell”.

Worst yoga pants ever“ ”

Short lived short loved“ ”

Boooooo“ ”

Product prices, discounts & reviews to understand retailer health

Large Capital Markets Company mined historical pipeline data to discover hidden trends in transmission volumes.

TransCanada publish transmission reports for all the pipelines in their network every single day.

Each daily transmission report is published to a separate web page, making it very difficult to compare transmission volumes across separate days.

Company used Import.io to go back in time: extracting data from all of the historical reports and building a dataset of transmission volumes going back 18 years.

Company is able to use the historical data to understand transmission volumes in the present: they know when transmission volumes deviate from a moving average and they have insight into a variety of seasonal effects.

Understanding historical trends in pipeline transmission volumes

Investment Management professionals rely on Import.io to

ü Beat benchmarks by leveraging web data as a new source of alpha.

ü Jumpstart market research with continuous streams of timely, accurate, and differentiated web data.

ü Enhance machine learning investment models with clean, normalized, and enriched web data.

ü Access and process web data quickly to identify fast-emerging themes like momentum, value, and sentiment.

THE VALUE IS SIMPLE

The use and the analysis of web data drives unique insights and actions

beyond those that regular data sources are capable of providing. Wed data

can therefore be a very important ALPHA generator.

Web data gives investment professionals unique alpha generating potential that other market incumbents simply don’t

have

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Web Data for Investment Management