Alternative Data

Hedge Funds are Relying on Alternative Data Insights to Gain an Investing Edge

Hedge Funds are Relying on Alternative Data Insights to Gain an Investing Edge

The thing about data that we all know is that it is growing at a rate that no human, organization or machine can process individually.

However, what a lot of us tend to forget is that while high quality or expensive data does not guarantee successful strategies, bad data does ensure failure at the operational level. Having that said, industries across the globe are developing algorithms to break this data down into actionable insights. Finance has seen a growing application of data analytics and mathematical tools in decision making.

Scope of advanced data-based tools in the hedge funds and finance industry

Hedge Funds are Relying on Alternative Data Insights to Gain an Investing Edge

Decision making is not only based on historical data on customer behavior but also on unconventional data sources like social media reviews and complaints, search engine results and even website and application traffic data. While these data sources, when analyzed can give you insights on not only how your own customer perceives your brand but also market trends, competitor performance, and even cross-industry behavior.

This data obtained from various sources is known as Alternative Data and helps the finance sector analyze and measure the performance of funds and investments. Quant hedge funds are the biggest manifestation of wide-scale application of math and algorithms to finance. However, that alone is not sufficient to ensure the success of quant hedge funds. We need more than mining data on traditional price and fundamentals and trading flow data to produce consistent returns. Although machine learning, deep learning, and alternative data will help one step into the competitive finance industry, it will not provide them with the edge.

Data and its analysis is not limited to the high value easily obtained insights from relatively cleaner data sources. A lot of data sources may contain useful insights that are camouflaged in the noise present in data. The trick is to extract useful information from these high noise data sources and use that for quantitative research and risk management at global investment banks, fundamental analysis at leading private equity firms and even generation of investment returns. The adoption of alternative data by the investment community has been driven by not only the developments of financial technology but also by the technological advancements to procure, store, manipulate and use different datasets for decision making.

Most industry experts like investors, hedge funds and asset managers alike consider these developments to be complementary tools with their conventional investment methodology. Using hyped technologies like alternative data and machine learning can lure customers in for investment managers and companies. However, the benefits do not stop there. Application of mathematical and data-based algorithms can help one sift through large amounts of information to make better marketing decisions, price the products and services better and have a more informed way of operation.

Challenges in the adoption of advanced technologies

News and data companies like Bloomberg and Thomson Reuters were a couple of the first few firms to include alternative data in their offerings. Moreover, over 75% of hedge funds had already started using social media and social-driven news feeds to inform investment decisions, back in 2017. Many surveys and reports on alternative data and artificial intelligence by sell-side banks, data providers, and consultants, in the last year, has helped to educate both the buy-side and the wider industry.

While ‘alternative data’ is also becoming mainstream in the finance industry, there is a common notion that this has led to quant hedge funds turning lackluster.

A major reason for that would be other financial industry products like exchange-traded funds (ETF) using data analytics in their operational strategies. The best example of this is all the popular smart beta and alternative investment strategy ETFs with lower fee structures. Although those strategies will not produce the same returns, the methodology will drive adaptation and consumption of data analytics post customization and some tweaking of the algorithms. It is safe to conclude here by saying that too many players investing in these developments with huge amounts of money will reduce the profit margins gained from them.

There are a few caveats in the adoption of advanced data analytics in the field of financial markets. Although established and popular streams like quant hedge funds and investment firms will have substantial data from various data sources, emerging markets will lack the same due to the absence of market penetration. Using Alternative data poses a lot of challenges. Due to its messy nature, cleaning and preprocessing the same becomes absolutely necessary. Extracting usable information from huge piles of noisy data is a big operational challenge. Additionally, data integration is a challenging process that involves establishing data linkages, dealing with bi-temporality (data based on varying timeframes) and data querying. Moreover, making the call on which alternative data source or technological tool to adopt must be well thought through. With so many sources available and a large number of competitors accessing the same, it becomes necessary to ensure that the additional return generated by using a data set is greater than the cost of such data.

Wish to know more about how alternative data can give your organization an investment edge? Contact us at Datahut, your big data experts.

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