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Some pundits claim that there is a revolution in portfolio management under way: The rise of the robots, a.k.a. robo-advisors. The most well known are Betterment.com, FutureAdvisor, Schwab Intelligent Portfolios and Wealthfront.

According to wikipedia

robo-advisors employ algorithms such as Modern portfolio theory that originally served the traditional advisory community, which has used algorithmically-based automated investment solutions (dubbed in the industry as "rebalancing software") to conduct portfolio management.

My question
Do you know whether there is some more information available which algorithms these firms use exactly? Is it just good old MPT or also more sophisticated stuff? (In this context it is interesting that they often get totally different portfolios). Best would be some kind of overview of the algorithms used by different providers.

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    $\begingroup$ Some I know are using more robust portfolio optimization techniques, some are using a bit of naive risk-parity, some are using variants of Black-Litterman, those are the things that I am aware of... $\endgroup$ – experquisite Oct 14 '15 at 18:08
  • $\begingroup$ @experquisite what do you mean exactly by robust portfolio optimization (People are using robust for everything). $\endgroup$ – math Oct 14 '15 at 18:55
  • $\begingroup$ Bootstrapping, Monte Carlo with perturbations, Meucci's techniques, etc. $\endgroup$ – experquisite Oct 14 '15 at 19:35
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    $\begingroup$ @experquisite: Are you sure you are talking about robo-advisors like Betterment and Wealthfront?!? $\endgroup$ – vonjd Oct 15 '15 at 5:19
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    $\begingroup$ Nobody (hopefully) would be using vanilla Markowitz for anything. Robust portfolio optimization is table stakes, this is not rocket science. $\endgroup$ – experquisite Oct 15 '15 at 17:52
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After having done a lot of research on the topic I found the following excellent research piece on ETF.com:

Wealthfront modifies historic asset-class returns with current market implied expected returns (Black-Litterman) as well as with the in-house views of Chief Investment Officer Burton Malkiel’s team. In addition, Wealthfront sets minimum and maximum weights for each asset type. The resulting portfolio has an unmistakable Malkiel flavor to it, with an emerging market allocation that reflects his interest in China.

Betterment uses Black-Litterman currently implied market expected returns, but deliberately includes small-cap and value as separate asset classes, adding a classic Fama-French factor tilt. It doesn’t constrain the portfolio weights, but they do account for downside risk. Betterment’s portfolios wind up quite similar to the global market, at least on the equities side.

Covestor deliberately veers away from its optimizer to hedge its portfolios against inflation and to adjust for downside risk. Its wide constraints allow heavy weights to emerging markets.

Wise Banyan constrains its portfolio weights “tighter than most,” back toward market-cap weights, according to Herbert Moore, co-founder and chief investment officer. This might explain why its portfolios allocate generously to U.S. equities, and away from the rest of the global equity market.

Invessence includes the largest number of asset types, adding granularity to the fixed-income side. It bases asset-class returns expectations on up to 80 years of historical ETF or index returns, but uses only nine years of volatility history. Invessence employs gold as an inflation hedge. It also constrains all asset weights except for U.S. equity. Sure enough, the U.S. dominates its equity allocation.

FutureAdvisor doesn’t optimize. Instead, its builds its portfolio in sleeves, creating a glide path much as the target-date mutual funds do. It builds in a “strategic” allocation to REITs as an inflation hedge, adding Fama-French type tilts. They’re not kidding. The firm’s portfolios emphasize small- and midcap stocks, and financials (REITS), with highest-in-class dividend yields and lowest price/book ratios.

There a many more details here:
http://www.etf.com/sections/blog/22973-ghosts-in-the-robo-advisor-machine.html?nopaging=1
and here:
http://www.etf.com/sections/blog/22982-inside-robo-advisor-asset-allocation.html?nopaging=1

The whole 7-parts series on the topic starts here:
http://www.etf.com/sections/blog/22946-which-robo-advisor-for-my-teen.html?nopaging=1

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Well, I did some modest research on this topic, looking at peers. Most of them use Modern Portfolio Theory, see this pic: Roboadvisor and portfolio construction

You can find this small survey here: https://www.linkedin.com/pulse/roboadvisors-like-commodore-vic20-apparently-according-raffaele-zenti?trk=mp-reader-card

The sector, I mean Roboadvisors, has a lot of disruption potential, obviously. But it is still immature, methodologically, with respect to the "traditional" (i.e. offline) asset management/wealth management industry.

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Many use systemic trading strategies that buy/sell according but not limited to:

  • P/E - price to earning
  • P/B - price to book
  • Dividend size
  • STD Deviation - volatility
  • Sharpe Ratio - return factoring volatility of returns in
  • Interest Rates
  • Credit Ratings
  • Economic indicators (GDP growth, CPI/inflation, Housing data, PMI)
  • other various financial ratios for pricing and measuring risk and return of assets
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  • $\begingroup$ Thank you. Do you have a source for me? $\endgroup$ – vonjd Oct 24 '15 at 20:17
  • $\begingroup$ My personal experience in algorithmic trading (i've been experimenting for the past 9 months or so) as well as various lectures on Quantopian, a site for beginners interested in algorithmic trading. I highly recommend visiting their youtube if your interested in algorithmic trading. quantopian.com $\endgroup$ – Alex Rodriguez Oct 24 '15 at 20:25
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    $\begingroup$ My question was not about algorithmic trading in general but about robo advisors! $\endgroup$ – vonjd Oct 24 '15 at 20:28

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