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Recent Discouragement

Back in October 2015, I saw this posted in one of my forums:

     > Looking at my past recent trades, I am thinking Mon and Fri
     > are not good days for me to trade. I am also thinking Tues,
     > Weds, and Thurs might not be so good either.

Richard Weissman’s Trading Rules

Over three years ago, I read an article on Richard Weissman’s top trading rules. Weissman has written articles, books, and supposedly consults and/or trains people. What I think is more important is a comment I wrote after these 10 rules: are there situations where every cliché rule does not apply? Let’s go through these one at a time in order to find out.

      1) Trade the market, not the money

Is the “market” a technical chart? Is it my unrealized gain/loss? Is “money” a technical indicator or is it my gain/loss? I can come up with a number of contradictory interpretations here.

      2) When there’s nothing to do, do nothing

I like this. Overtrading is a frequently-discussed problem. Some people feel they have to trade and get nervous when they don’t. If it’s a consistent problem then a therapist or counselor/coach might help one to feel more at ease during downtime.

      3) Stop adjustments can only be used to reduce
          reduce risk, not increase it

I think this is a recommendation to only narrow stops and never widen them. If one is using a trading system then I see a potential problem with changing the stop in either direction. Trade like you backtest and do not deviate either way.

      4) There are only two kinds of losses: big
          losses and small losses. Given these
          choices, always choose small losses.

I disagree. Not only are “big” and “small” subjective, the occasional large loss may be part of a viable business model.

      5) Don’t anticipate, just participate

I disagree. “Prepare for war in a time of peace.” Anticipate what you are going to do in all cases because if you don’t then when the time arrives to participate you may be like a deer in the headlights.

      6) Buy the strongest, sell the weakest

This does not always apply (e.g. option trading).

      7) Stagger entries & exits

I like this but it also seems to be personal preference. A workable business plan need not stagger.

      8) Look for low risk, high reward, high
          probability setups

I think this is one possible trading style but certainly not the only viable one.

      9) Correlations are for defense, not offense

Pair traders may disagree because they use correlations to make money (offense). I disagree because during market crashes, even non-correlated markets tend to move together.

      10) Be disciplined in risk management and flexible
            in perceiving market behavior

I’m not entirely sure how these two fit together. I agree with the former. Whether risk is managed at entry (limiting position size) or with stops, it should be disciplined because catastrophic loss could occur the one time I’m sloppy. With regard to the latter, I’m not a big believer in forecasting future market moves under any circumstances. I would suggest being disciplined when interpreting market behavior and then apply the plan consistently.

Out of 10 trading rules I agree with 1.5 of them. Not great but could be worse. Your mileage, like mine, may vary.

Statistical Manipulation

Peter Berezin, chief strategist for BCA Research, wrote an article for the September 2016 issue of AAII Journal. Near the end he made some comments about statistics:

     > Statisticians like to say that if you torture
     > the data long enough, it will confess to
     > anything. This old adage is especially relevant
     > to the study of stock market anomalies. First,
     > there is the risk that any anomaly that is
     > unearthed will simply end up being the product
     > of data mining. Second, even if an anomaly
     > turns out to be genuine, there is a risk that
     > it will be arbitraged away once the investment
     > community becomes aware of it.

I argued for an increased use of inferential statistics here and I later relayed the opinion of a financial adviser as to why inferential statistics may be relatively uncommon.

I still believe inferential statistics are useful to offer an apples-to-apples comparison but Berezin makes a good point that statistics may be used to manipulate. We can never be sure of an investigator’s underlying motives and unless we do the research ourselves, we also cannot be sure the statistics were correctly computed.

I do believe we can do a couple things to avoid these statistical issues. Data mining involves searching a large collection of data with the purpose of finding significant results. This should be avoided. Give me an indicator and enough data and I can find a snippet of price action for which the indicator works fabulously (fallacy of the well-chosen example). This is unlikely to be profitable in live trading, however. One way to avoid this involves searching the surrounding parameter space for a high-plateau rather than a spike region of profit.

With regard to market edge being arbitraged out over time, I need to monitor my system and have criteria indicating when it might be broken. Walk-forward analysis can help to keep a strategy current thereby increasing the probability it will work with live trading. I may also monitor total profit/loss and stop trading the system when this value falls below the equity moving average. This should be developed through proper validation methodology.

Fooled by Randomness

William Bernstein, co-founder of Efficient Frontier Advisors and author of several books, was interviewed in the September 2016 issue of the AAII Journal. He made an interesting point that reminded me of Nassim Taleb’s book Fooled by Randomness:

     > …never confuse outcome with process. By that
     > I mean that there is a lot of noise in investing,
     > and it is perfectly possible to have a bad
     > strategy or be a stupid investor… and do very
     > well. People who buy lottery tickets occasionally
     > win. Having a good strategy and not having a good
     > result happens all too frequently as well. The
     > essential thing people really don’t understand
     > about finance is that there is an enormous
     > amount of noise, particularly over the short term.

The take-home message for me is the first line: never confuse outcome with process. The stock market goes up most of the time. It may even go up for years. When it corrects, large sums of money can be lost in short periods of time. One must be careful not to lose all that has been gained or more.

While the market is going up, people are happy with their strategies and impressed with themselves. Many think they have skills and have attained a level of expertise. Remember the old saying “a rising tide lifts all boats,” though, which suggests most stocks rally when the market moves higher. Anyone participating during these bull market periods is likely to make money regardless of strategy. Ego can really come into play when people dramatically increase position size. This inconveniently happens all too often just in time for the next market correction or crash.

Trading system development methodology is designed to determine whether a profitable system is attributable to an effective strategy or simply a matter of luck. The process is designed to screen out the luck factor by studying large sample sizes, maximum drawdowns, employing Monte Carlo simulation, and walk-forward analysis. This is challenging work because it requires coding skills and software know-how in addition to theoretical understanding.

The cost of mistaking random luck for skill worthy of confidence is an increased likelihood of catastrophic loss when the next bear market rears its ugly head: something none of us look forward to.

Financial Secrets (Part 2)

Today I will conclude with excerpts from Dr. Sarah Newcomb’s article in the Sep 2016 AAII Journal.

     > Financial skills are not easily learned when
     > communication is opaque, and experience can be
     > a ruthless teacher. Leaving loved ones to
     > learn about money management ‘the hard way’
     > may mean they learn too late, or not at all.
     > Remember that even a third-tier topic of
     > conversation is perfectly acceptable to
     > discuss with intimate relations. Neglecting
     > to speak out on financial matters with
     > spouses and loved ones may well be an act of
     > avoidance rather than tact. If we want to
     > leave a legacy of strength and stability, we
     > be brave enough to challenge the culture of
     > silence and speak plainly with our loved
     > ones about money [italics mine].

Dr. Newcomb later mentions data from an American Express study:

Dr. Newcomb goes on to say:

     > Silence may be comfortable today, but our
     > spouses and children will feel the impact
     > of our financial legacy when we are gone.
     > Rather than bequeathing confusion and
     > hardship, opening the door to financial
     > discussions can help prepare them for that
     > more difficult transition. The same holds
     > true when your financial picture is bleak.
     > Sheltering loved ones from difficult
     > realities will not help them cope.
     > Speaking the truth, however painful, is
     > more loving than presenting a pretty
     > falsehood.

I suspect Dr. Newcomb has some powerful lessons for all to learn in her words on this topic.

And on the grand scale, I feel the financial industry promises too much while delivering too little. This takes place at a self-perpetuating cost that people happily pay because the opacity of the subject matter blocks them from knowing any better.

Financial Secrets (Part 1)

To me, the phrase “financial secrets” immediately connotes fraud. I would invite you to read some of my previous posts to find out why. Somewhat ironic is the observation that financial details are held with such secrecy in our society today.

Dr. Sarah Newcomb, author of Loaded: Money, Psychology, and How to Get Ahead Without Leaving Your Values Behind (2016) wrote an interesting article on this subject in the Sep 2016 AAII Journal. I will post some excerpts and [minimal] commentary:

     > We don’t talk about money.
     > According to “Emily Post’s Etiquette,” money is
     > a third-tier topic of conversation, putting it
     > a full class above sex and religion in terms of
     > inappropriateness to discuss in mixed or casual
     > company. Especially among those with wealth, the

More taboo a topic than sex?! This is shocking to me.

     > unspoken laws of society dictate that we maintain
     > a dignified silence on this important, but often
     > divisive, topic…
     > …the purpose of etiquette is to avoid any cause
     > for pain, embarrassment or offense. Yet our
     > reticence on financial matters is so pervasive
     > that it extends to those who would often greatly
     > benefit from open communication. In many cases,

Those of you who have been reading for a while know I’m fairly convinced that options are better than stock. I therefore feel like I walk around with a huge secret about what I do—a secret that suggests most of the financial industry is wrong and does a disservice to its clients by not employing options as an investment vehicle. Because we are so hush-hush about money, this is not something I feel comfortable coming right out to say.

     > well-intentioned civility leads directly to the
     > financial harm, or even ruin, of those we care for
     > most.

Namely my parents. For years they have invested with a money manager who I believe has failed them. The upside to keeping silent and letting them do their own thing is that I avoid the burden of losses that could result from the market turning ugly. The downside is watching them throw away money year after year to generate subpar returns.

I will finish this discussion in the next post.

Statistical Wisdom from CHiP’s

Today I feature a lesson on statistics brought to us courtesy of an episode of CHiP’s. The episode “Bio Rhythms” originally aired February 17, 1979.

This particular conversation took place between officers Frank Poncharello (“Ponch,” played by Erik Estrada) and Sindy Cahill (played by Brianne Leary).

      [Ponch] Hey Sindy. Feedback on the bio rhythms,
      right? Looks pretty good I guess, huh?

      [Cahill] Well it’s much too early to tell, Ponch.
      Ask me in a couple of months.

      [Ponch] Yeah but you must have enough to tell
      if the system works…

      [Cahill] Well, most everybody in chronobiology
      agrees that we all do have rhythms: cycles.
      But we don’t know exactly how it works or how
      the date of birth is involved. It’s going to
      take me a long time to run a large enough
      sample to eliminate coincidence. So for
      the moment, nope… I don’t have enough data.

      [Ponch] Sindy uh, listen… I’ve got a special
      interest. I mean, it does look good. Doesn’t it?

      [Cahill] Ponch, “looks good” is not a term we
      use in statistical study.

Many times I have heard traders speak with overconfidence about strategies that produced one or two winning trades.

I have also seen many casual traders extremely eager to pounce on any promising idea they hear, read, or see.

Both examples are germane to the conversation between Cahill and Ponch. Small sample sizes are susceptible to the possibility of fluke or, as Officer Cahill stated, coincidence. We therefore know nothing until we get a larger sample size. Heuristic thinking like confirmation bias is notorious for driving action during this phase and quite often, people circumvent the hard work altogether by not insisting upon (or being aware of) proper statistical validation.

Israelsen on Diversification (Part 3)

I want to offer one further critique of Craig Israelsen’s performance data included in the last post.

As shown in the table, over 15 years the 12-asset portfolio outperformed the 7-, 4-, 2-, and 1-asset portfolio (in that order). But was this a statistically significant result?

In response to my question, Israelsen wrote:

     > The issue of statistical significance pertains to
     > differences among samples that are drawn from a
     > population (inferential statistics). As the
     > different portfolios are not samples, the issue
     > of statistical difference in the returns is not
     > relevant. In other words, the return of the 1-
     > asset portfolio is not the mean return of that
     > type of portfolio, it is THE return of that
     > portfolio. Same logic for the 2-asset, 4-asset,
     > and 7-asset portfolio. In essence, any
     > difference in the returns is material.

I think Israelsen has a good point but I am still uneasy about his numbers. To get the returns presented, I would have to start investing on the exact same day he did. This is highly unlikely.

Alternatively, Israelsen could have created samples by studying rolling periods. This involves calculation of multiple returns over stated time intervals starting on different days. He could calculate a mean and standard deviation of all sample periods, which could then be compared using inferential statistics.

By providing one static return as Israelsen did, I believe he leaves the door open to fluke occurrence. Without knowing how likely different portfolio start dates are to dramatically affect average annual returns, no robust conclusions can be drawn. I believe Perry Kaufman made this same mistake in an article discussed recently.

I also believe Israelsen missed the point of diversification because he did not discuss drawdowns. While diversified portfolios may not result in higher annualized returns, I do believe standard deviation of returns (otherwise known as “risk”) decreases when non-correlated assets are combined.

Put another way, liked hedged portfolios I expect diversified portfolios to “lose” most of the time. This was mentioned in Part 2. However, with lower drawdowns the probability of investors holding positions through the rough times is increased. The worst outcome would be dumping the portfolio and locking in catastrophic loss from a market crash and missing a big market rebound that may be just over the horizon.

Israelsen on Diversification (Part 2)

Today I continue with some “words of wisdom” written by Craig Israelsen in the Feb 2016 issue of Financial Planning magazine.

     > Interestingly, many investors claim to want a low-
     > correlation portfolio that includes ingredients that
     > do not all zig and zag at the same time. But when a
     > few of their portfolio ingredients zag downward
     > while other portfolio ingredients are zigging upward,
     > the investor frets about the underperforming zaggers
     > and becomes angry he owns a fund or stock that is
     > losing money.
     > Many investors talk the talk, saying that they want
     > a low-correlation portfolio, but they can’t—or
     > won’t—walk the walk and actually experience one.

As mentioned a couple times in the last post, I completely agree on an anecdotal level based on what I have heard from multiple investors during casual discussion.

Israelsen continued by providing data for different “levels of diversification” over the last 15 years. He looked at a 1-, 2-, 4-, 7-, and 12-asset portfolio. The 1-asset portfolio was 100% U.S. large-cap stocks. The 4-asset portfolio was 40% U.S. large cap, 20% U.S. small cap, 30% bonds, and 10% cash. The 12-asset portfolio was equally divided into 12 different asset classes. Annualized performance through 11/30/15:

Portfolio performance of various number of asset classes (8-25-16)

I highlighted the best (worst) performance for each column in green (red).

Israelsen writes:

     > Over the three-year, five-year, and 10-year periods…
     > the one-asset class investment… was the best
     > performer. Finally, over the 15-year period, the
     > value of a broadly diversified approach manifested
     > itself with an annualized return of 7.08%…

If you’re a believer in diversification then this sounds like it took 15 years for the noise to shake out and the truth of diversified superiority to become evident.

However, from a statistical standpoint I doubt these numbers prove anything. The 12-asset portfolio performed worst over one, three, and five years. I believe one year is too short to conclude anything. 3-5 years, though? That’s at least intermediate-term. I would consider 10 years to be long-term and the 12-asset portfolio performed second worst over this interval. Given that it performed best over 15 years, I believe we have a set of performance numbers that, considered altogether, are inconclusive.

I also don’t think 15 years tells the whole story. I wonder what portfolio outperformed over 13, 14, 16, or 17 years? If it’s the 12-asset portfolio then Israelsen’s claims are substantiated. Based on the trend of numbers presented, though, I would not be surprised to see a more scattered distribution.

Israelsen writes:

     > …a broadly diversified approach will lag behind
     > when one particular asset class… is on a hot streak.

I don’t like this as a caveat for why the 12-asset portfolio lagged in all but the 15-year time interval. Some asset class is always going to be on a hot streak, which would suggest a broadly diversified approach will always lag. So if you want to choose a loser, make sure to diversify? That’s certainly what it can feel like and this feeds right back to the Israelsen excerpt at the top of this post.

Israelsen on Diversification (Part 1)

While I don’t always agree with his conclusions, I find Craig Israelsen to be a compelling contributor to Financial Planning magazine. In this blog post I address his February 2016 article “Why Not the Best?”

Israelsen writes:

     > Every good planner preaches the glory of diversification…
     > But here’s a reality that is less pleasant to disclose to
     > a client: a diversified portfolio will never be the best
     > performer in any given year…
     > Comparing a broadly diversified portfolio with the
     > best performer of the year is complete nonsense, yet many
     > clients can’t resist doing it.
     > History shows that the best performing investment in any
     > year will be the stock of a company that relatively few
     > people are actually invested in.

I have a couple problems with this last paragraph. First, he gives only two supporting examples. This hardly satisfies the claim that “history shows… in any year.” Two years is not every (“any”) year. Second, in order to be more rigorous he would have to define “relatively few.” Even a large cap stock, for example, has “relatively few investors” in relation to the total number of investors or certainly the total population.

From an anecdotal standpoint, I agree with this paragraph. I cannot count how many times I have heard people say “if only I had invested in XYZ” where XYZ, now significantly appreciated, traded in extremely low volume before the big run-up attracted fame and attention.

Israelsen writes:

     > …every year… the best performer is always a single
     > stock, never a stock mutual fund. A diversified group
     > of equities can never outperform the best-performing
     > single stock… for all practical purposes, the best
     > return is unachievable [italics mine]…

I wholeheartedly agree.

     > But here’s the rub: when we build a broadly
     > diversified portfolio, it will contain some asset
     > classes that do well in the current climate, and
     > some that will be underachievers. That is the real
     > challenge of diversifying: being patient as we watch
     > the various ingredients in our portfolio take their turn
     > being the hero—and the goat. If we’re not careful,
     > our emotions will lead us to chase the heroes… and
     > dump the goats.

Again from an anecdotal perspective, I completely agree.

I will continue with Israelsen’s article in the next post.