Hedge fund managers who fail to beat their benchmarks need to quickly adapt or risk losing investors. Beating benchmarks is extremely difficult to do, especially so if you are an Efficient Market Hypothesis (EMH) believer. This is why professional hedge fund managers are eager for new sources of alpha. Generate alpha or lose to the competition is the stark reality for every manager.
At the same time, we are living through a data explosion. By 2020, it’s estimated that for every person on earth, 1.7MB of data will be created every second. The prevailing belief is that this data — and the predictive power it promises — is the most powerful alpha source.
This is what we call alternative data and it refers to unique data not previously known to financial markets but nonetheless powerful to professional investors. It is not available from traditional market data providers. When people talk about alternative data, a couple of data types commonly come to mind: satellite data, sensor data and web data. Of the three, web data is by far the biggest in terms of size, the most diverse in terms of subject matter and therefore has the widest application to businesses of all kinds. The appeal of alternative data is largely the potential for an information advantage over the market regarding investment management decisions.
By 2020, spending on alternative data and its associated infrastructure is expected to exceed USD 7 billion. The $3.1 trillion hedge fund industry is about to bet its future on it.
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 from structured time series and accounting information. Today, alternative data’s adoption is at a tipping point and their use is growing exponentially.
The value is simple: the use and the analysis of alternative data drives unique insights and actions for your business beyond those that regular data sources are capable of providing. Alternative data can therefore be a very important competitive differentiator.
A new report from Greenwich Associates shows that asset managers are spending roughly $900,000 per year on alternative data sources with web data as the number one type of alternative data currently being used.
The scope of web data used by financial professionals has become more diverse, including job listings, social media posts and product pricing. Because of the application of artificial intelligence (AI), machine learning (ML) and natural language processing (NLP), companies are able to consume vast pools of data to make predicting future trends and formulating investment decisions more feasible.
Common Uses of Web Data for Hedge Funds
- Market data aggregation: Market information is freely available on the web but spans across hundreds of websites. Receive a continuous stream of 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. Tracking pricing data, for example, can provide a directional indicator for the sales of consumer products.
- 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. The idea behind this method is to figure out how companies in your sector stack up against each other, or your own company. Whether you’re looking at how financial performance or how their products and services differ, collecting competitive analysis data can fuel your analysis.
- KYC and risk management: Track regulatory development and evaluate the integrity of potential businesses by monitoring websites and social media and extracting changes automatically.
- 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: Investment firms are increasingly basing recommendations on news. 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.
Web data brings opportunities for hedge fund managers to gain an informational advantage over its peers when it comes time to make investment decisions. The main appeal of web data to hedge funds 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. Indeed, it is this obscure nature of web data that makes its high value, and hedge funds are only too willing to pay a premium for its acquisition.
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.
If you can’t measure it, you can’t improve it.
There are many metrics that measure hedge fund performance. Many that do hedge fund evaluations (funds of hedge funds for example) often designs their own proprietary metrics as well. The list can go on and on but here are a few common ones to get you started.
Assets Under Management (AUM)
Assets under management measures the total market value of all the financial assets which a financial institution such as a hedge fund, mutual fund, or broker manages on behalf of its clients and themselves.
Absolute Return
The absolute return or simply return is a measure of the gain or loss on an investment portfolio expressed as a percentage of invested capital.
Alpha
Alpha is the return to a portfolio over and above that of an appropriate benchmark portfolio. It is a term used in investing to describe a strategy’s ability to beat the market, or it’s “edge.” (the manager’s “value add”). Alpha is also often referred to as “excess return” or “abnormal rate of return,” which refers to the idea that markets are efficient, and so there is no way to systematically earn returns that exceed the broad market as a whole.
Beta
A measure of systematic (i.e., non-diversifiable) risk. The goal is to quantify how much systematic risk is being taken by the fund manager vis-à-vis different risk factors, so that one can estimate the alpha or value-added on a risk-adjusted basis.
Standard Deviation
Standard deviation is a statistical measurement in finance that, when applied to the annual rate of return of an investment, sheds light on the historical volatility of that investment. The greater the standard deviation of a security, the greater the variance between each price and the mean, which shows a larger price range.
R-Squared
R-Squared is a measure of how closely a portfolio’s performance varies with the performance of a benchmark, and thus a measure of what portion of its performance can be explained by the performance of the overall market or index. Hedge fund investors want to know how much performance can be explained by market exposure versus manager skill.
Sharpe Ratio
Sharpe ratio is a measure of risk-adjusted return, computed by dividing a fund’s return over the risk-free rate by the standard deviation of returns. The idea is to understand how much risk was undertaken to generate the alpha.
Hurdle Rate
A hurdle rate is the minimum rate of return on a project or investment required by a manager or investor. Hurdle rates allow companies to make important decisions on whether to pursue a specific project.
Value at Risk
Value at Risk is a technique which uses the statistical analysis of historical market trends and volatilities to estimate the likelihood that a specific portfolio’s losses will exceed a certain amount.
Sortino Ratio
The Sortino ratio measures the risk-adjusted return of an investment asset, portfolio, or strategy. Sortino ratio is a useful way to evaluate an investment’s return for a given level of bad risk. Since this ratio uses only the downside deviation as its risk measure, it addresses the problem of using total risk, or standard deviation, which is important because upside volatility is beneficial to investors and isn’t a factor most investors worry about.
Benchmark
To accurately measure fund performance, it is necessary to have a point of comparison against which to evaluate returns. A benchmark is a standard against which the performance of a security, mutual fund or investment manager can be measured. Generally, broad market and market-segment stock and bond indexes are used for this purpose.
Efficient Market Hypothesis (EMH)
The Efficient Market Hypothesis, or EMH, is an investment theory whereby share prices reflect all information and consistent alpha generation is impossible. According to the EMH, stocks always trade at their fair value on stock exchanges, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. As such, it should be impossible to outperform the overall market through expert stock selection or market timing, and the only way an investor can possibly obtain higher returns is by purchasing riskier investments.
Relative Return
Relative return is the return an asset achieves over a period of time compared to a benchmark. The relative return is the difference between the asset’s return and the return of the benchmark. Relative return can also be known as alpha.
Skewness
Skewness considers the extremes of the data set rather than focusing solely on the average. Short- and medium-term investors in particular need to look at extremes because they are less likely to hold a position long enough to be confident that the average will work itself out.
Techniques Governing Data Quality and Control
When making investment decisions it is paramount for a hedge fund manager to maintain history of data so that they can backtest and recreate the derived data from the source.
Two fundamentals to ensuring data quality and control are:
Audit Trail
An audit trail (also called audit log) is a security-relevant chronological record, set of records, and/or destination and source of records that provide documentary evidence of the sequence of activities that have affected at any time a specific operation, procedure, or event.
Backtesting
Backtesting is the general method for seeing how well a strategy or model would have done ex-post. Backtesting assesses the viability of a trading strategy by discovering how it would play out using historical data. If backtesting works, traders and analysts may have the confidence to employ it going forward.
Want to learn more about how web data can transform your alternative data strategies? Talk to an expert.
