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Maurice Chia

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  • Your All-ETF Portfolio: Too Correlated? [View article]
    Thanks for your feedback. We are all in this together and learning from each other.
    Feb 28 03:26 AM | Likes Like |Link to Comment
  • Your All-ETF Portfolio: Too Correlated? [View article]
    Sir. If you can send to my inbox the ETFs that you currently hold, I will filter a list of other ETFs that are non-correlated with your portfolio.
    Feb 28 03:24 AM | Likes Like |Link to Comment
  • Power Optimization: Only When The Time Is Right [View article]
    Fat tails are implicit to the hybrid rebalancing process. However, the process goes beyond mapping a fixed fat tail distribution to return data. Probability is, at the end of the day, an asymptotic concept.
    Dec 8 03:45 AM | Likes Like |Link to Comment
  • Power Optimization: Only When The Time Is Right [View article]
    That's good news
    Dec 8 03:31 AM | Likes Like |Link to Comment
  • Power Optimization: Only When The Time Is Right [View article]
    The hybrid rebalancing process is the coupling between mean-variance theory and an actuarial process. The ideas stem from my 9 years as head of actuarial research in a publicly listed reinsurance corporation. Taming the vagaries of chance is the life-blood of any reinsurance entity. What may sound like astrology to you is a science to those whose profits depend on such rigorous analyses.
    Dec 6 09:10 PM | Likes Like |Link to Comment
  • The Art Of Non-Normal Rebalancing [View article]
    Update: The site where the software is found is being overhauled. Information on the software can be found here in the meantime
    Nov 24 09:50 PM | Likes Like |Link to Comment
  • Refining Our Optimized Portfolio With Western Refining Inc. [View article]
    Please read your inbox for my answer. Thanks again for your comment
    Nov 23 06:37 AM | Likes Like |Link to Comment
  • Power Optimization: There's Something About Wal-Mart [View article]
    Actually your interesting insight into Wal*Mart gives support to why it is negatively correlated to the rest of the pack. Based on what you say, it appears Wal*Mart might even be a better candidate for "geographical" diversification than alot of international stocks that are highly correlated with the US market.
    Nov 6 11:15 PM | Likes Like |Link to Comment
  • The Art Of Non-Normal Rebalancing [View article]
    Thank you for your kind comment. I use a software that can be found at this website:
    Nov 5 11:50 PM | Likes Like |Link to Comment
  • In Search Of The Ultimate Efficient Portfolio [View article]
    Just saw this comment. I was the head of the actuarial department in a publicly listed reinsurance corporation for 9 years. I am not sure if this qualifies me as a finance professional but as far as I can recall volatility as measured by standard deviation is still a key concept that goes beyond Finance 101. And it's a long term asymptotic concept, not a short term one.
    Oct 11 01:54 PM | Likes Like |Link to Comment
  • In Search Of The Ultimate Efficient Portfolio Part 2 [View article]
    I am afraid it's not exactly as you describe. I realize you are trying to visualize how it works so allow me to explain it in a slightly different way. Please bear with me while I build it up.

    Suppose you had 2 stocks in your portfolio. You would calculate the weighted return of these 2 stocks to arrive at the portfolio expected return. Let the weight for stock1 and stock2 be w1 and w2 respectively.

    The portfolio volatility where volatility is defined as standard deviation would be the square root of this whole formula: (w1 x volatility1)^2 + (w2 x volatility2)^2 + 2w1w2 x volatility1 x volatility2 x correlation between stock1 and stock2.

    My purpose of showing you this formula is so that you appreciate the fact that the weights are calculated not just on the basis of correlation but the aggregate result of individual volatilities and the correlation that exists between each and every pair of stocks, at the level of expected return of the portfolio.

    So in the article above, why wasn't more weight put on the other 15 stocks? It is a combination of reasons. If any weight was put at all on any of these 15 stocks it would be because by doing so, it helped reduce portfolio volatility,while maintaining the expected portfolio return which is the weighted average of all the stocks that are included in the portfolio.

    If any of the 15 stocks did reduce portfolio volatility, it was because it was negatively correlated (or more precisely, less than perfectly positively correlated) to either one or both of the 2 stocks to the extent that the reduction in portfolio volatility due to the effects of correlation was greater than any increase in portfolio volatility due to the introduction of the new stock.

    To elaborate on the process, let's say you started with the 2 stocks that you refer to above. You then try to include stock3 but it does not pass the criteria for inclusion into the portfolio. Do you then discard stock3? The answer is not just yet.

    This is because you will not know if stock3 combines with stock4, or stock5, etc to form an even lower volatility portfolio at the given return. In other words, you have to analyze all possible combinations before deciding which will be the efficient portfolio (i.e. lowest portfolio volatility for the given return) that sits on the frontier. Do this process for each and every possible portfolio return and you will then be able to construct the entire efficient frontier.

    Generating this by hand will obviously take forever. And there is no closed-end formula that will give you the answer. Optimizers use an iterative process which is an algorithm that loops until a certain tolerance is met.

    I hope this gives you a clearer picture of what is involved.

    To answer your second question, the optimization process will reduce the stocks automatically according to the process I just described. This usually turns out to be a manageable number.

    But if you want all 187 in the final analysis, you have to use a constrained frontier which artificially forces a minimum and/or maximum allocation for each and every stock. In a sentence, it is possible to impose any kind of constraint, as long as, it makes sense to do so.
    Oct 9 05:10 AM | Likes Like |Link to Comment
  • In Search Of The Ultimate Efficient Portfolio Part 2 [View article]
    Thanks for your input. The optimizer looks at the combinations which have the most advantage in terms of volatility, return, and correlation. For the intent of this article, I have used an unconstrained frontier.

    In a universe that consists of only one asset class, the correlations among the different stocks would necessarily be highly positive, resulting in many stocks being disqualified.

    Note however that you will see concentrations in varying numbers and degree, depending on where you are along the frontier but this in turn is dependent on the exact interplay between the returns, volatilities and correlations of the stocks in the universe under study.

    Having said that, practitioners sometimes constrain the frontier to "force" a minimum or maximum allocation to each stock so no stock is left out or not too much weight is allocated to one stock.

    This is mostly done when each stock in the universe has been pre-selected based on other criteria (e.g. value investing) and the practitioner does not want the optimizer to discriminate against, say, a stock that has almost equal diversification advantage but slightly lower return.

    Your comment gives me an idea for my next article which I hope you will also read.
    Oct 8 02:52 AM | Likes Like |Link to Comment
  • The Art Of Non-Normal Rebalancing [View article]
    Thanks. I have sent you a message
    Oct 6 01:02 AM | Likes Like |Link to Comment
  • The Art Of Non-Normal Rebalancing [View article]
    Thanks for your input. Actually the back-tests go to each review point and only take data up to that review point to do their calculations and predictions. It then goes to the next review point to see how the portfolio did after applying the allocations suggested at the previous review point. So I believe it is a real test of predictive performance.
    Oct 6 12:37 AM | Likes Like |Link to Comment
  • Rebalancing The PowerShares QQQ Trust ETF [View article]
    Thanks for your query. QQQX returned the following numbers during the same periods:

    2009 : -32.7%
    2010: 61.4%
    2011: 23.6%
    2012: 11.3%
    Sep 16 03:51 AM | Likes Like |Link to Comment