Here Martijn Groot, VP Marketing and Strategy at Asset Control , discusses how AI and Machine Learning techniques are finding their way into financial services and changing the way we do things, in particular how we invest.
Ranging from operational efficiencies to more effective detection of fraud and money-laundering, firms are embracing techniques that find patterns, learn from them and can subsequently act on signals coming out of large volumes of data. The most promising, and potentially lucrative, use cases are in investment management though.
Among the groups that benefit most are hedge fund managers and other active investors who increasingly rely on AI and machine learning to analyse large data sets for actionable signals that support a faster; better-informed decision-making process. Helping this trend is the increased availability of data sets that provide additional colour and that complement The typical market data feeds from aggregators, such as Bloomberg or Refinitiv, range from data gathered through web scraping, textual analysis of news, social media and earnings calls. Data is also gathered through transactional information from credit card data, email receipts and point of sale (“POS”) data.
The ability to analyse data has progressed to apply natural language processing (NLP) to earning call transcripts to assess whether the tone of the CEO or CFO being interviewed is positive or negative.
The ability to analyse data has progressed to apply natural language processing (NLP) to earning call transcripts to assess whether the tone of the CEO or CFO being interviewed is positive or negative.
Revenue can be estimated from transactional information to gauge a company’s financials ahead of official earnings announcements and with potentially greater accuracy than analyst forecasts. If, based on this analysis, a fund believes the next reported earnings are going to materially differ from the consensus analyst forecast, it can act on this. Satellite information on crops and weather forecasts can help predicting commodity prices.
These are just a few examples of the data sets available. The variety in structure and volume of data now available is such that analysing it using traditional techniques is becoming increasingly unrealistic. Moreover, some has a limited shelf life and can quickly become out-of-date.
Scoping the Challenge
Setting up a properly resourced team to assess and process this type of data is costly.
The best approach therefore is to more effectively assess and prepare the data for machine learning so that the algorithms can get to work quickly. Data scientists can then focus on analysis rather than data preparation. Part of that process is feature engineering, essentially selecting the aspects of the data to feed to a machine learning algorithm. This curation process involves selecting the relevant dimensions of the data, discarding for instance redundant data sets or constant parameters, and plugging gaps in the data where needed.
An active manager could potentially analyse hundreds of data sets per year; the procedure to analyse and onboard new data should be cost-effective. It should also have a quick turnaround time as the shelf life of some of these data sets is short.
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Addressing these challenges means that traditional data management (the structured processes to ingest, integrate, quality-proof and distribute information) has to evolve. It needs to extend data ingestion and managing data quality into a more sophisticated cross-referencing of feeds looking for gaps in the data; implausible movements and inconsistency between two feeds. For instance, speed of data loading is becoming more important as volumes increase. With much of the data unstructured, hedge funds should be conscious of needing to do more with the data to make it usable. More sophisticated data mastering will also be key in making machine learning work effectively for hedge funds.
This functionality coupled with the capability to quickly onboard new data sets for machine learning will enable funds to save money and especially time in the data analysis process. It will allow data scientists to focus on what they do best and generate more actionable insight for the investment professionals.
Reaping the Rewards
Machine learning clearly has huge potential to bring a raft of benefits to hedge funds, both in reducing the time and cost of the data analysis process and in driving faster time to insight. It also gives firms the opportunity to achieve differentiation and business advantage. Hedge funds need to show returns to attract investment in an increasingly competitive space, machine learning supported by high quality data management offers a positive way forward.
Industry made overall gains of 7.40% through the year, the highest annual performance seen since 2013. The Preqin All-Strategies Hedge Fund benchmark posted returns of 7.40% in 2016, marking the best performance year for the industry since 2013 and more than tripling the gains made through 2015 (+2.03%). Despite a volatile start to the year which caused some performance difficulties, hedge funds rebounded to post positive returns in nine of the final 10 months of the year. This strong period of performance for the asset class sees three-year annualized returns stand at 4.83%, while five-year annualized gains have reached 7.47%.
Event driven strategies hedge funds saw double-digit gains in 2016, returning 12.47% for the year. This marks a sharp contrast from the previous year, when event driven funds were the only leading strategy to suffer losses (-0.78%). Overall, all leading hedge fund strategies posted positive returns across 2016, and only relative value funds saw their 2016 returns (+4.74%) fail to match 2015 performance (+5.65%).
Other Key Hedge Fund Performance Facts:
Smaller Funds Post Higher Gains: According to Preqin’s size classifications*, smaller hedge funds were able to generate the greatest returns in 2016. Emerging and small hedge funds saw gains of 8.18% and 6.40% respectively in 2016, while medium and large vehicles posted performance of 5.53% and 4.63%.
North American Funds Rebound: After making gains of 0.45% in 2015, North America-focused hedge funds returned 10.20% in 2016. Funds focused on Europe (+2.89%) and the Asia-Pacific region (+1.68%) struggled through the year, but strong gains in Latin America saw emerging markets funds return 9.96%.
Discretionary Funds Succeed: Hedge funds following a discretionary trading methodology returned 7.51% in 2016, improving on 2.51% gains made in 2015. By contrast, systematic funds saw their annual performance fall from 5.46% in 2015 to 4.44% the following year.
CTAs Struggle: Despite a strong start to the year, CTAs did not enjoy sustained gains through 2016, and returned 0.91% for the year. This an improvement on the 0.15% recorded in 2015, but remains a long way short of the double-digit returns CTAs posted in 2014.
Funds of Funds Lose Ground:
Funds of hedge funds recorded five months of losses in 2016, and only returned more than 1.00% in July. As such, annual returns for funds of hedge funds fell to -0.25% in 2016, their lowest performance year since 2011, when they saw losses of 3.98%.
Amy Bensted, Head of Hedge Fund Products at Preqin says: “2016 showed that hedge funds were able to cast off performance struggles that hampered them in 2015, and they posted the best performance year for the industry since 2013. Fear over China’s economy in Q1, the Brexit vote at the end of Q2 and the US presidential election in Q4 drove the narrative in 2016; and although there were some high- profile losses, the associated volatility created opportunities for hedge funds to produce significant returns for investors. Looking ahead to 2017, the continued consequences of these geo-political events are likely to remain key determinants of industry performance.
“Despite the marked improvement in performance, hedge fund managers will be aware that in recent years returns have still fallen short of other alternative asset classes and public market indices. This is especially pertinent in the wake of some high-profile investors announcing the reduction or elimination of hedge fund investments from their portfolio. As a result, firms will be eager to sustain the momentum built over the latter part of 2016 and to prove their worth as investments capable of generating non-correlated, downside-protected performance.”
*Preqin size classifications: Emerging (less than $100mn); Small ($100-499mn); Medium ($500-999mn); Large ($1bn plus)
(Source: Preqin)
Deutsche Bank Tokyo
Hedge fund industry assets are set to surpass $3 trillion by the end of the year, according to Deutsche Bank’s 13th annual Alternative Investment Survey. Institutional investment in hedge funds is set to increase, with 39% of these investors planning to increase their allocation to hedge funds in 2015.
Deutsche Bank surveyed 435 hedge fund investors, representing over $1.8 trillion in hedge fund assets under management (AUM), who shared insights into their sentiment and allocation plans for 2015.
The survey found that asset growth continues to be concentrated among the largest managers. Since 2008, assets managed by firms with more than $5 billion AUM have grown 141%, compared to 53% for firms with less than $5 billion. Today, it is estimated that less than 200 hedge fund firms account for more than two thirds of industry assets.
“As institutional investors’ needs continue to evolve, they are increasingly looking to work with larger hedge fund managers and intermediaries who can meet their appetite for comprehensive portfolio solutions,” said Barry Bausano, Co-head of Global Prime Finance at Deutsche Bank. “More and more, we’re seeing today’s hedge fund assets concentrated among the largest managers.”
“Hedge fund managers who continue to focus on alignment of interests with the allocator community will have an increasingly competitive advantage as our industry grows and evolves,” said Murray Roos, Co-head of Global Prime Finance at Deutsche Bank. “Reward for alpha generation and co-investment opportunities will be key factors in building strong partnerships between limited partnerships and general partnerships.”
Manager selection is becoming increasingly important, as the gap between outperforming and underperforming hedge funds widens. While the average hedge fund returned 3.33% in 2014, the top 5th percentile generated returns greater than 22%.
Investors risk/return expectations for traditional hedge fund products continues to come down in favour of steady and predictable performance: only 14% of respondents still target returns of more than 10% for the hedge fund portfolio, compared to 37% in 2014.
With this in mind, however, 40% of respondents now co-invest with hedge fund managers as a way to increase exposure to a manager’s best ideas and enhance returns. 72% of these investors plan to increase their allocation in 2015.
Following a strong year of performance, at least one in every three respondents are planning to increase their allocation to quantitative strategies in 2015. Three of the most sought after quantitative strategies include commodity trading advisor (CTA), quant equity market neutral and quant equity.
