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November 01, 2016
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Architecture of a RoboAdvisor..

This three part series explores the automated investment management or the “Robo-advisor” (RA) movement. The first post in this series @- http://www.vamsitalkstech.com/?p=2329 – discussed how Wealth Management has been an area largely untouched by automation as far as the front office is concerned. As a result, automated investment vehicles have largely begun changing that trend and they helping create a variety of business models in the industry esp those catering to the Millenial Mass Affluent Segment. The second post @- http://www.vamsitalkstech.com/?p=2418  focused on the overall business model & main functions of a Robo-Advisor (RA). This third and final post covers a generic technology architecture for a RA platform.Business Requirements for a Robo-Advisor (RA) Platform…

Some of the key business requirements of a RA platform that confer it advantages as compared to the manual/human driven style of investing are:

  • Collect Individual Client Data – RA Platforms need to offer a high degree of customization from the standpoint of an individual investor. This means an ability to provide a preferably mobile and web interface to capture detailed customer financial background, existing investments as well as any historical data regarding customer segments etc.
  • Client Segmentation – Clients are to be segmented  across granular segments as opposed to the traditional asset based methodology (e.g mass affluent, high net worth, ultra high net worth etc).
  • Algorithm Based Investment Allocation – Once the client data is collected,  normalized & segmented –  a variety of algorithms are applied to the data to classify the client’s overall risk profile and an investment portfolio is allocated based on those requirements. Appropriate securities are purchased as we will discuss in the below sections.
  • Portfolio Rebalancing  – The client’s portfolio is rebalanced appropriately depending on life event changes and market movements.
  • Tax Loss Harvesting – Tax-loss harvesting is the mechanism of selling securities that have a loss associated with them. By doing so or by taking  a loss, the idea is that that client can offset taxes on both gains and income. The sold securities are replaced by similar securities by the RA platform thus maintaining the optimal investment mix.
  • A Single View of a Client’s Financial History- From the WM firm’s standpoint, it would be very useful to have a single view capability for a RA client that shows all of their accounts, interactions & preferences in one view.

 

Algorithms for a Robo-Advisor (RA) Platform…

There are a variety of algorithmic approaches that could be taken to building out an RA platform. However the common feature of all of these is to –

  • Leverage data science & statistical modeling to automatically allocate client wealth across different asset classes (such as domestic/foreign stocks, bonds & real estate related securities) to automatically rebalance portfolio positions based on changing market conditions or client preferences. These investment decisions are also made based on detailed behavioral understanding of a client’s financial journey metrics – Age, Risk Appetite & other related information.
  • A mixture of different algorithms can be used such as Modern Portfolio Theory (MPT), Capital Asset Pricing Model (CAPM), the Black Litterman Model, the Fama-French etc. These are used to allocate assets as well as to adjust positions based on market movements and conditions.
  • RA platforms also provide 24×7 tracking of market movements to use that to track rebalancing decisions from not just a portfolio standpoint but also from a taxation standpoint.

 

Technology Requirements for a Robo-Advisor (RA) Platform…

An intelligent RA platform has a few core technology requirements (based on the above business requirements).

  1. A Single Data Repository – A shared data repository called a Data Lake is created, that can capture every bit of client data (explained in more detail below) as well as external data. The RA datalake provides more visibility into all data to a variety of different stakeholders. Wealth Advisors access processed data to view client accounts etc. Clients can access their own detailed positions,account balances etc.
  2. Customer Data Collection – Existing Financial Data across the below categories is collected & aggregated into the data lake. This data ranges from Customer Data, Reference Data, Market Data & other Client communications. All of this data, can be ingested using a API or pulled into the lake from a relational system using connectors supplied in the RA Data Platform. Examples of data collected include – Customer’s existing Brokerage accounts, Customer’s Savings Accounts, Behavioral Finance Suveys and Questionnaires etc etc. The RA Data Lake stores all internal & external data.
  3. Algorithms – The core of the RA Platform are data science algos. Whatever algorithms are used – a few critical workflows are common to them. The first is Asset Allocation is to take the customers input in the “ADVICE” tab for each type of account and to tailor the portfolio based on the input. The others include Portfolio Rebalancing and Tax Loss Harvesting.
  4. The RA platform should be able to store market data across years both from a macro and from an individual portfolio standpoint so that several key risk measures such as volatility (e.g. position risk, any residual risk and market risk), Beta, and R-Squared – can be calculated at multiple levels.  This for individual securities, a specified index, and for the client portfolio as a whole.

                                             roboadvisor_design_arch    

                                                  Illustration: Architecture of a Robo-Advisor (RA) Platform 

Conclusion…

As one can see clearly, though automated investing methods are still in early stages of maturity – they hold out a tremendous amount of promise. As they are unmistakably the next big trend in the WM industry industry players should begin developing such capabilities.

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