investment strategy - asset allocation 20160204

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Data: the raw material of data science. Beer data leads to beer esmates. Our process begins with collecon of data from across the globe including: Fundamental Data: Underlying financial condion of companies Macroeconomic Data: The overall health of the economy Senment Data: The general atude of investors and the media Financial Data: Price and volume data generated from market acon Technical Data: Paern recognion and indicators from financial data Naismith Wealth Management | 2626 N Lakeview Avenue #307 |Chicago, Illinois 60614 | 312.757.4547 | www.naismithwealth.com Big Data Risk & Return Regardless of your market hypothesis, mathemacal asset allocaon requires... ...esmates of expected rates of return, risk, and asset similarity. In most financial models, these esmates are 'naive' forecasts meaning historical performance is assumed to be the same going into the future. This is similar to invesng while looking in the rear-view mirror. We seek to improve on naive forecasng by condioning return and risk esmates on preempve indicators of performance. This allows us to beer posion porolios for changing market environments. From passive to acve, we provide investment strategies that align with your personal point-of-view on how the markets work. Personalized Porolio Construcon Using Advanced Mathemacal Opmizaon

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Page 1: Investment Strategy - Asset Allocation 20160204

Data: the raw material of data science. Better data leads to better estimates.

Our process begins with collection of data from across the globe including: Fundamental Data: Underlying financial

condition of companies Macroeconomic Data: The overall health of

the economy Sentiment Data: The general attitude of

investors and the media Financial Data: Price and volume data

generated from market action Technical Data: Pattern recognition and

indicators from financial data

Naismith Wealth Management | 2626 N Lakeview Avenue #307 |Chicago, Illinois 60614 | 312.757.4547 | www.naismithwealth.com

Big Data Risk & Return

Regardless of your market hypothesis, mathematical asset allocation requires...

...estimates of expected rates of return, risk, and asset similarity. In most financial models, these estimates are 'naive' forecasts meaning historical performance is assumed to be the same going into the future. This is similar to investing while looking in the rear-view mirror. We seek to improve on naive forecasting by conditioning return and risk estimates on preemptive indicators of performance. This allows us to better position portfolios for changing market environments.

From passive to active, we provide investment strategies that align with your

personal point-of-view on how the markets work.

Personalized Portfolio

Construction Using Advanced

Mathematical Optimization

Page 2: Investment Strategy - Asset Allocation 20160204

For all investment strategies, your personal risk tolerance, time horizon, tax sensitivity, and personal needs are always taken into consideration.

Human + Machine Learning: We

leverage the best of both minds. We deploy machine learning algorithms such as adaptive boosting with

trees, random forests, and support vector machines, to identify

relationships in our historical datasets with hundreds of thousands of

explanatory variables. The machines learn how to identify the optimal

expected rate of return for each asset class. Through rolling window,

out-of-sample back-testing, we understand the precision of our

machines and know what behavior is within the range of expectations.

Note that the information provided is not intended to give any specific advice nor an offer to purchase or sell any securities. It purpose is for informational purposes only. Please remember that past performance may not

be indicative of future results. Information pertaining to Naismith Wealth Management operations, services and fees is set forth in our current disclosure statement, Form ADV Part 2A and 2B. A copy of which is available

at www.naismithwealth.com under Important Disclosure. ©2016 Naismith Wealth Management LLC, All Rights Reserved

Global Optimization

Global optimization identifies the portfolios which maximize the risk/return trade-off.

Our optimal estimates of expected rates of return, risk, and asset relationship are inputs into our global optimization algorithms. Custom routines give us the flexibility to identify the portfolio that will maximize your expected return for your level of risk tolerance. This portfolio is traditionally called the optimal portfolio which lies on the efficient frontier.

Investment Strategies

Building on tactical asset allocation, we loosen the constraints on allocation range and

place more weight on the forecasts produced by our predictive analytic models. The goal

is to outperform SAA and TAA by continually positioning the portfolio for what is likely to

happen looking ahead. Your portfolio's calculated risk will always be equal to or less than

your personal risk tolerance level. DAA is typically implemented using low cost exchange

traded funds.

Building on strategic asset allocation, we factor in multiple market forecasts from our

internal predictive analytic models. We then tactically adjust allocation size within a

predetermined range around the strategic allocations with the goal of outperforming the

base policy mix. Additionally, periodic rebalancing is performed when allocations drift

outside their predetermined allocations. This strategy can be implemented using either

passive or active funds.

Using historical long-term expected rates of return, risk, and correlation, we create a

diversified portfolio across equities, bonds, real estate, commodities, and alternative,

both domestic and international. Your portfolio is rebalanced when allocations drift

outside predefined bounds or when there are changes in your time horizon, risk

tolerance, tax sensitivity, and/or personal needs. Strategic Asset Allocation can be

implemented using either passive or active funds.

Strategic Asset Allocation

Tactical Asset Allocation

Dynamic Asset Allocation