In light of all the current market volatility, have you taken the time to score yourself? Score your portfolio? Scoring both yourself and your portfolio over time is just not about scoring your investment choices, but also your trading activity which deals with timing and emotions. Let’s face it, investing over long periods of time just isn’t as easy as it may sound. Investors go through emotional periods of both fear and greed with each possibly creating impulse moves that cause you to buy and sell your holdings when you probably shouldn’t have. For the average investor, timing the market over many attempts is often futile. Maybe 20% of the time your moves are perfect, and you make money. The others - well, you’ve been trying to forget about.
However, studies have been done through a Massachusetts research firm called Dalbar that has been studying the behavior of mutual fund investors for the last 25 years. Their findings* show not a good track record of success for those that choose to market-time. Their findings show over the past year, and for periods of five, 10, 15, 20, and 30 years, the average mutual fund investor has underperformed the market for both stocks and bonds. * NY Times 7/26/2019
Unfortunately, the results show us that almost everyone would have done better over a long-term period if they just bought and held onto their investments and did nothing. One problem with this technique – does anyone want to watch their account value drop as much as 40%-50% again, as happened to many the during 2008 Great Recession, to only patently wait to get back to even over the next 3-4 years? My guess would be NO.
Portfolio Optimization is an eye-opening alternative to either regular old Buy and Hold, or Actively trading your account. When done correctly, it may give you a better chance of designing a portfolio that performs better in both up and down markets. However, that being said, it takes a lot of work, not to mention education, experience, and patience.
Portfolio Optimization is the process of selecting an allocation of individual securities out of the group of securities being examined, as well as analyzing other securities out in the universe that is available that you may have not considered according to an investor's objective. The objective typically maximizes factors such as expected return, reduced risk, minimal volatility, and consistency. Rarely, the percentage of each security allocated within the overall portfolio is a proportional number or includes securities from each asset class per modern portfolio theory.
Optimization can be an effective tool to look back at all the data on a group of securities to narrow down the individual securities, and the amount of each security to include in your portfolio, in hopes of achieving an above-average performance in both up and down markets moving
Here is a typical group of stocks measured from 2005 – 2020. The Hypothetical shows an even allocation of 10% between them versus Optimizing the allocation of the same securities with the objective of better overall performance in both up and down markets looking forward (past performance is no guarantee of future results).
2020 Silicon Cloud Technologies LLC
These are the hypothetical Back-tested portfolio results before and after being Optimized. The computer has Optimized the portfolio based on a strategy that allocates different amounts between the chosen securities based on a proprietary algorithm.
© 2020 Silicon Cloud Technologies LLC
As you can see in this hypothetical example, optimization may have a significant effect on a portfolio. An analysis may be done with your current holdings as well as using our computer algorithm to search the universe of individual stocks to give you a completely different portfolio of stocks and/or funds than you may currently have now. For more information or a free optimization analysis of your portfolio please contact us.
Data has been compiled by Silicon Cloud Technologies, LLC, and is retrieved from a number of sources they believe to be accurate and correct. Harboursldewealth Management and Silicon Cloud Technologies, LLC, are not affiliated with Kovack Securities, Inc. or Kovack Advisors, Inc. All portfolio returns presented are hypothetical and back-tested. Hypothetical returns do not reflect trading costs, transaction fees, or taxes. The results are based on information from a variety of sources considered reliable, but we do not represent that the information is accurate or complete. The results are based on the total return of assets and assume that all received dividends and distributions are reinvested. The back-tested results assume the portfolio was rebalanced to its original percentage on an annual basis at the close of the market on 4/17/2020, with all dividends reinvested. The annual results were back-tested from January 1, 2005, to April 17, 2020, and are based on monthly returns. Silicon Cloud Technologies uses the Vanguard 500 Index Investor (VFINX) as the hypothetical portfolio’s Benchmark with the Prospectus found at https://investor.vanguard.com/mutual-funds/profile/VFINX. The back-tested results assume the portfolio was rebalanced to its original percentage on an annual basis at the close of the market on 4/17/2020, with all dividends reinvested. The annual results were back-tested from January 1, 2005, to April 17, 2020, and are based on monthly returns. Past performance is no guarantee of future results.
All performance results of the Portfolios are based on the performance of individual stocks. The hypothetical back-tested performance was achieved with the benefit of hindsight and computer-generated portfolio optimization; it does not represent actual investments in any investment strategies. There's are certain limitations inherent in hypothetical model results like those portrayed, particularly that such hypothetical model returns do not reflect trading in actual client accounts and do not reflect the impact that material economic and market factors may have had on the adviser’s decision-making had the adviser actually been managing client funds. Unlike an actual performance record, hypothetical back-tested performance results do not represent actual trading. These types of simulated trading programs, in general, benefit compared to actual performance results because such simulated programs are designed with the benefit of hindsight. In addition, simulated trading does not involve or take into account financial risk and does not take into account that material and market factors may have impacted your advisors' decision making, all of which can adversely affect actual trading results and performance. For example, the ability to withstand losses or adhere to a particular trading program in spite of trading losses are material points which can also adversely affect markets in general or the implementation of any specific trading program. Hypothetical back-tested performance does not represent actual performance, trading costs, or the impact of taxes and should not be interpreted as an indication of such performance.