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Private Equity is using Statistics to Analyze Data from the Past, if not yet to Write Forecasts & Investment Memos for the Future
Research
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- Supporting Evidence below from Oct 16, 2018 by Matthew Brodsky, Wharton School of Business
- Posted on Aug 1, 2018 by Peter Moore, Bullet Point Network, L.P.
In the past 12 months, Two Six has analyzed over $20 billion in individual transaction data. As Picache explains it, their data science approach has already become embedded in decision-making in private equity; it’s a big data play that the private equity types “get.”
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Recruiter Highlights Difficulty of Building Teams that Collaborate on Stories and Statistics
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- Posted on Aug 1, 2018 by Peter Moore, Bullet Point Network, L.P.
Big data will be increasingly important for asset managers, but many organizations are unsure how to proceed. This three-step process can help.
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Use Explicit Statistics on Human Progress as a Sanity-Check to Stories about Human Regress
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- Posted on Aug 1, 2018 by Peter Moore, Bullet Point Network, L.P.
Negative news is one reason why people consistently underestimate the progress humanity is making, complains Steven Pinker. To discern the true state of the world, he says, we should use numbers. In “Enlightenment Now”, he does just that. The result is magnificent, uplifting and makes you want to rush to your laptop and close your Twitter account.
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Investment Decisions based on Past Data Alone can go Very Wrong
Research
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- Supporting Evidence below from Apr 4, 2018 by Zachary Schleffer, Thomson Reuters Corporation
- Posted on Aug 1, 2018 by Peter Moore
Investments are continuing to flow into funds that use Artificial Intelligence (AI) to make trading decisions, but in the past few months we’ve seen just how important it is to still have human involvement and good fundamental reasoning behind these strategies.
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Quantamental Investing can be More Powerful than Pure Quant Investing
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- Supporting Evidence below from May 1, 2018 by Andrey Slimmon
- Posted on Aug 1, 2018 by Peter Moore
The real power is not in pure quant investing, but in a combination of quantitative and fundamental, stock-specific research—a ‘quantamental’ approach.
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AI has Difficulty when Humans use it to Change the Rules that it Learned
Research
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- Supporting Evidence below from Mar 7, 2018 by David Trainer, Forbes
- Posted on Aug 1, 2018 by Peter Moore, Bullet Point Network, L.P.
Most people do not understand that AI, especially the AI used in finance today, lacks the application of deep subject matter expertise to create the clean data and relationships that are the foundation of any successful investment strategy or AI. Winning games is one thing, but the real world is not a game that follows immutable rules in a strictly defined space. In the real world, humans change the rules, break the rules, or the rules don’t even exist. Current AI is nowhere near navigating real world situations without a great deal of human intervention.
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Big Data is Harder to apply to Finance because "the Ground is Always Shifting"
Research
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- Supporting Evidence below from Sep 26, 2018 by Adam Satariano, Bloomberg L.P.l
- Posted on Aug 1, 2018 by Peter Moore, Bullet Point Network, L.P.
Finance is perhaps AI’s most daunting challenge. Training a computer to correctly identify a Labrador is different from getting it to suss out a bond market. Markets move in mysterious ways, influenced by news events, economics, politics, regulation, and human judgment. “In the financial world,” says Gary Collier, Man AHL’s co-chief technology officer, “the ground is always shifting.”
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Deep Learning's Limits are becoming Clear
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- Supporting Evidence below from Aug 4, 2018 by Christopher Mims, Wall Street Journal
- Posted on Aug 8, 2018 by Peter Moore, Bullet Point Network, L.P.
Should Artificial Intelligence Copy the Human Brain? The biggest breakthrough in AI, deep learning, has hit a wall, and a debate is raging about how to get to the next level
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How can Machine Learning Rule Investing when it struggles with Medical Diagnosis?
Research
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- Supporting Evidence below from Aug 11, 2018 by Daniela Hernandez, Wall Street Journal
- Posted on Aug 13, 2018 by Peter Moore, Bullet Point Network, L.P.
If IBM can't use machine learning to match disease symptoms and genetics with treatments after spending over 6 years and $15 billion, how will companies use it to pick investments that match future supply with future demand, where there is an extra degree of difficulty in the reflexivity of social science?
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Billionaire Jeff Bezos believes that Logic (Data) must be married with Pathos (Narrative)
Research
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- Supporting Evidence below from Aug 15, 2018 by Carmine Gallo, Inc. Magazine
- Posted on Aug 16, 2018 by Peter Moore, Bullet Point Network, L.P.
Amazon uses "a ton of metrics" to measure success, explained Bezos. "I've noticed when the anecdotes and the metrics disagree, the anecdotes are usually right," he noted. "That's why it's so important to check that data with your intuition and instincts, and you need to teach that to executives and junior executives."
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