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Why Machine Learning Hasn’t Made Investors Smarter

Nastel Technologies®
July 13, 2019

Machine Learning – Hedge funds have been in the doldrums and face mounting pressure to justify their fees. Will artificial intelligence come to the rescue? A growing number of hedge funds are putting money behind the idea that a branch of AI called machine learning could provide a way to get back on top. The problems? It’s hard, expensive and prone to failure.

1. What’s machine learning?

A software program that searches for regularly occurring patterns in more data than even the most sleep-deprived junior analyst could examine, and then tests its hypotheses against even more data. What can satellite shots of mall parking lots tell you when combined with in-store sales data? Does a default premium of A to B and a yield curve slope of C to D have an E percent chance of boosting a stock price by F percent or above? If a company’s chief executive or a central bank official uses specific words, does that have an impact on asset prices? Put me in, coach, the algorithm says, I’ll figure it out.

2. Is everybody trying this?

Lots of funds, big and small, are testing the waters. Fifty-eight percent of managers in one survey said machine learning will have a medium-to-large impact on the industry. Hedge fund giant Bridgewater Associates and Man Group Plc as well as Highbridge Capital Management and Simplex Asset Management in Japan are among firms developing machine learning or investing in it. Renaissance Technologies and Two Sigma have used the techniques for a long time. In a potential source of capital for AI funds and signs that the strategy is slowly becoming mainstream, JPMorgan Chase & Co.’s asset management arm is planning to invest in emerging and established machine-learning statistical-arbitrage hedge funds.

3. Is it hard to do?

Finding patterns isn’t that hard; finding ones that work reliably in the real world is. Financial data is very noisy, markets are not stationary and powerful tools require deep understanding and talent that’s hard to get. One quantitative analyst, or quant, estimates the failure rate in live tests at about 90%. Man AHL, a quant unit of Man Group, needed three years of work to gain enough confidence in a machine-learning strategy to devote client money to it. It later extended its use to four of its main money pools.

This article originally appeared on To read the full article, click here.

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards

If you would like to learn more, click here.

Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering tools for Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT, IBM Cloud Pak for Integration and many more.


The Nastel i2M Platform provides:


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