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Karen the “Supertrader” (Part 2)

Last time, I explained the delivery of Karen “the Supertrader” Bruton to the trading masses. On May 31, 2016, the SEC made a press release about the 34-page complaint.

Here are some excerpts:

     > Each month, Hope [Advisers, which is owned by Bruton]
     > caused the Funds to make certain “Scheme Trades” that
     > had the purpose and effect of realizing a large gain
     > in the current month while effectively guaranteeing
     > a large loss would be realized early the following
     > month. In essence, these trades continuously
     > converted any realized losses into realized gains in
     > the current month, and losses which would be realized
     > in subsequent months, except that they would be
     > continually deferred by the Defendants engaging in
     > additional Scheme Trades. The Defendants…
     > intentionally sized the Scheme Trades such that the
     > Funds realized a profit every month… without the
     > fraudulent Scheme Trades, Hope would have received
     > almost no incentive fees from at least October 2014
     > through the present. Instead, Hope extracted millions
     > of dollars in incentive fees. In recent months,
     > Hope has been using the Scheme Trades to avoid
     > realization of more than $50 million in losses, while
     > still earning large monthly incentive fees…

     > Of the 10% incentive fee paid to Hope by the HI [Hope
     > International] Fund, Bruton divided the fee nearly
     > equally among herself and two other Hope employees,
     > after paying salaries of other employees and expenses
     > for Hope…

     > If those unrealized losses had been factored in, as
     > required by the operating agreement, Hope would have
     > been entitled to no fees. Instead, Hope collected
     > over $1 million worth of fees during that period…
     > Bruton paid herself a significant portion of the fee
     > income…

     > For example, in October of 2014, Hope experienced
     > massive trading losses as a result of volatility in
     > the market… [Her funds] collectively ended the month
     > with unrealized losses of approximately $100 million,
     > most of which resulted from the October trading losses.
     > Nevertheless, Hope reported to investors that the
     > Funds had millions of dollars’ worth of “realized”
     > gains in October and collected incentive fees of more
     > than $600,000… Despite the massive trading losses
     > in both Funds that month, Bruton caused Hope to pay
     > her over $220,000 in early November 2014 for her
     > October “performance…” The Defendants never told
     > the Funds’ investors about the Scheme Trades… The
     > Defendants never told the Funds’ investors that
     > Hope was causing the Funds to make trades for the
     > primary purpose of avoiding realization of losses.

I will continue in the next post.

Karen the “Supertrader” (Part 1)

Tom Sosnoff introduced the financial world to Karen “the Supertrader” Bruton in a series of interviews broadcast on Tasty Trade (TT), a financial education network. The nickname reflects her supposed windfall success with options.

As of the date of this writing, it’s not hard to find Karen’s interviews or related discussion on the Interwebs. YouTube has them as does the TT website. The first was done on May 31, 2012. Two subsequent interviews were done on February 11 and July 23, 2014. I follow several trading forums to stay apprised of the “latest and greatest” on trading TV and it’s not a leap to say that in the world of retail traders, Karen the “Supertrader” has achieved legendary status.

Tom Sosnoff founded thinkorswim (TOS) brokerage and has always been an outspoken advocate of the retail trader. After TOS was purchased by TD Ameritrade in 2009, Sosnoff co-founded TT. He has made numerous speaking appearances in different cities over the years where a series of presentations are given amid a festive backdrop of food, drink, and goodies. Despite his seemingly crazy schedule, Sosnoff answers e-mails personally (short and sweet but I believe it’s him).

Sosnoff has cast the rallying cry “Power to the People!” upon the average retail trader. With evidence to support his relentless critique of the financial industry, he encourages people to assume control of their own investing/trading. I believe many “newbies” have taken the leap because of Sosnoff’s efforts and I feel he is to be commended for his effort. This is not to say that he doesn’t profit from potential conflicts of interest but that is beyond the scope of today’s post.

Without question, I think most of Karen the “Supertrader’s” popularity stems from Tom Sosnoff himself. When a rockstar like that puts someone up on a pedestal as a virtuoso success and interviews her about the millions she has made and her humble beginnings, people are sold, persuaded, and inspired.

I will discuss Karen’s alleged wrongdoing in the next post.

Where Do I Start?

The following question was posted in an investment group I follow:

      > I was hoping someone might be kind enough to meet with
      > me for dinner or drinks and show me the basics. I’m
      > looking to get started investing but I have no idea where
      > to start so it would be really great to get some advice.

Anyone who knows me would not be surprised to see me jump at the opportunity to talk about investing WHILE BEING TREATED TO DINNER AND DRINKS. Are you kidding me? I could talk about this stuff all day long for free and enjoy it. Dinner and drinks is just “icing” (figuratively, of course, since dessert does not seem to be included).

But then I got to thinking: what exactly is she asking for and does the question even make sense?

I know one thing: regardless of the teaching, any pupil may or may not make money in the markets. This theme runs extensively through my writings. In a recent post, for example, I quoted Garrett Baldwin who basically said consistent and accurate forecasting of future prices does not exist. For the same reason, nobody can guarantee profits.

This leads me to believe that education may be the only guarantee anyone can make in this space. I can teach her about stocks and investment vehicles: what they are, what it means to trade them, and how to trade them. I can teach her how to do the math to determine whether she is profitable. None of this guarantees she will make money, though: a singular fact that should be part of any introductory education.

For this reason, clarifying her question might be helpful. If she is looking for hints about my Holy Grail trading system or hoping to get me to spill my secret altogether over one too many beers then she will be disappointed. I think many people believe the Holy Grail does exist and finding it is just a matter of getting the proper education. If only it were that easy…

This is certainly not to say that education has no value. Some people search endlessly for the Holy Grail. They attend a plethora of investment seminars, buy “education” packages, and subscribe to black box trading systems. These are the people who never got the memo about the nonexistent Holy Grail and for them, this one lesson alone could have saved countless sums of time and money.

Stop at the Equity Curve (20) (Part 3)

The end-of-trade PnL equity curve filter looked promising but mark-to-market (MTM) is what I would encounter in real-time.

To do this, I replicated a previous backtest from 5/1/2008 through 3/16/2009 and tracked daily account value. The 239 days between 6/17/2008 and 2/4/2009 was the shortest elapsed time to get four exit signals.

I expected the MTM equity curve to largely parallel the equity curve generated previously. I expected some evidence of future shift since the end-of-trade PnL (spreadsheet) approach counts the final trade PnL on the date of trade inception:

End-of-Trade PnL vs. MTM Daily (unfiltered) (7-14-16)

The future shift is evident along with much more volatility in the MTM daily curve. With regard to filter efficacy:

Efficacy of 20-SMA MTM Equity Curve Filter (7-14-16)

I was disappointed with MTM daily’s performance with the 20-SMA trade filter. It worked well until Feb 2009 when it suffered a very large drawdown (DD). This was the maximum DD seen with the MTM daily equity filter and a larger DD than seen without! This suggests winning (losing) days were (not) filtered out: a dangerous combination.

A closer look at the data shows the MTM daily equity filter was more active than the end-of-trade PnL filter. The former (latter) generated 13 (4) signals. Of the 13 signals, five or two lasted one or two days, respectively. This suggests a large number of whipsaws because such a filter will be effective by signalling a big market decline to remain on the sidelines for an extended period. The MTM filter was particularly ineffective in March 2009 as the strategy in the market on March 2 to lose $220K and out of the market on March 4 to miss a $175K gain.

The graph shows the MTM equity curve filter did about $200K better than no filter at all. However, with all the whipsaws I am not convinced it is any more effective. I would need a longer MTM backtest to be convinced.

Backtesting made it clear why the MTM daily filter was so much more volatile. The end-of-trade PnL method only gains or loses the value of one trade per day. Although this is big money for the largest losers, on big down days the MTM daily filter method loses big money on all of the open trades.

While the end-of-trade PnL filter was quite effective, as I suspected it is very artificial and looks nothing like the MTM daily filtered equity. If I want to continue studying an equity curve approach then I should backtest over a longer period. Alternatively, I could look to price action of the underlying for DD minimization.

Stop at the Equity Curve (20) (Part 2)

Consider this a manuscript in reverse. Having detailed the problems and limitations last time, today I will present the eye-popping, artificial results.

Here are the total return and drawdown numbers:


Here are the overall system statistics:


The filter clearly did a good job of removing losing trades. I highlighted the numbers that really tell the difference. The filter removed 607 trades but the % wins increased from 88% to over 95%, the overall standard deviation was reduced by 42%, and the average trade was 76% higher!

The Profit Factor went from solid to a ridiculous 7.38. This number is high enough to be worrisome. Aside from the caveats I mentioned last time, no slippage was factored into the stops so I know performance would not be this good.

The next step is to backtest several months and mark-to-market daily. I can even make sure to input the same trades as used last time. I want to see how the shape of the equity curve compares and see if significant differences exist between drawdowns.

If that ends up similar then the next step will be to study long puts. With the MA undercut 30 times, I have many instances to study in order to see how a delta-neutral position fares.

Once that’s done, I need to investigate the parameter space for MA period.

Stop at the Equity Curve (20) (Part 1)

What happens when the equity curve is used as an indicator to be in or out?

To study this, I used my -0.10/3x spreadsheet with 3,798 trades. As an arbitrary starting point, I used a 20-SMA (current day included) to be calculated after the close. When necessary, I waited one whole trading day to take action.

What’s particular about this approach is the methodology by which I have simplified the equity calculations but logistics must be considered when transitioning to real-time application. The final gain/loss registered at trade close is added to the account at trade inception. This creates the appearance of more continuity in the spreadsheet than might actually be experienced.

Consider a big down day that pushes multiple trades to maximum loss. The spreadsheet shows the total equity and MA after each trade is tabulated but in reality they would all be closed at once. This could significantly reduce the efficacy of the risk-management tool. From a mark-to-market perspective, it is conceivable that cumulative losses on many trades might drive equity below its moving average before any single trade actually closes for max loss. I’m afraid the only way to compare the two would be to backtest again and track daily equity in this manner. Hopefully it will become clear after a small sample.

When I traded naked strangles, I anecdotally noted great potential savings were I to have exited positions after incurring the greatest daily loss over the last several weeks and staying out of the market entirely until I saw a weekly equity gain. This makes me think there might be something useful about monitoring EOD equity vs. the moving average but I also wonder if this might be accompanied by more whipsaws.

Aside from timing issues, another issue requiring careful consideration is the position neutralization itself. Slippage would be horrific if I tried to close an inventory of legs especially if I had to replace them a few days later. I might be better off creating a delta-neutral position by purchasing NTM options and holding until the equity curve crosses above once again.

Having said all this, I’ll run through the results in my next post.

Financial Advisers: Quite the Unsavory Lot? (Part 2)

Today I will continue my discussion of the abstract by Egan et al. and then conclude with some additional comments about the article.

     > Firms that persistently engage in misconduct coexist with
     > firms that have clean records… differences in consumer
     > sophistication may be partially responsible for this
     > phenomenon: misconduct is concentrated in firms with
     > retail customers and in counties with low education,
     > elderly populations, and high incomes.

This is shocking. In so many cases, people hire financial professionals precisely because they do not feel educated in this domain. For the industry to focus its fraudulent efforts on the low-hanging fruit is absolutely despicable.

And yes, I did write “the industry.” I highlighted the failure to break down numbers between the financial and insurance industries. We know it’s not more than 7% for either, based on the sample, and despite their having a five-fold greater chance of committing misconduct again, they are able to continue working.

     > Our findings suggest that some firms “specialize” in
     > misconduct and cater to unsophisticated consumers…

This is a serious indictment.

     > Firms that employ more employees with records of
     > misconduct are also less likely to punish additional
     > misconduct.

They also said firms more likely to hire advisers previously disciplined had higher incidences of misconduct. This all suggests misconduct to be related to a firm’s organizational culture. The worst firms in terms of percentage disciplined for misconduct were Oppenheimer & Co., Inc. (19.60%), First Allied Securities, Inc. (17.72%), and Wells Fargo Advisors Financial Network, LLC (15.30%). Conversely, the best firms were Morgan Stanley & Co. LLC (0.79%), Goldman, Sachs & Co. (0.88%), and BNP Paribas Securities Corp. (1.17%).

One potentially confounding variable is the definition of misconduct. Many cases involved the suitability of investments. For example, a high front-load, aggressive mutual fund would not be appropriate for an elderly client. In 2015, the President’s Council of Economic Advisors reported on bad [unsuitable] advice without categorizing it as misconduct. The Council estimated such conflicts of interest to cost working-class and middle-class families up to $17 billion per year. I would definitely consider this misconduct but I don’t know whether it was included for purposes of the article.

To summarize, roughly 12 out of 13 advisers have no records of misconduct. Because of the one in 13 who do, I would strongly suggest anyone employing such individuals to run a background check first. Two places to start are the Financial Industry Regulatory Authority’s (FINRA) BrokerCheck database and the SEC Form ADV, which may be accessed here.

Financial Advisers: Quite the Unsavory Lot? (Part 1)

Whenever money is involved, it seems like fraud is not too far behind. Mark Egan, Gregor Matvos, and Amit Seru have recently added fuel to the fire with a 61-page article published Feb 2016.

Egan et al. write:

     > [from] Luigi Zingales in his American Finance Association
     > presidential address: “I fear that in the financial sector fraud
     > has become a feature and not a bug.” This perception has been
     > shaped by highly publicized scandals that have rocked the
     > industry over the past decade. While it is clear that egregious
     > fraud does occur in the financial industry, the extent of
     > misconduct in the industry as a whole has not been
     > systematically documented.

I am now going to discuss the abstract.

     > We construct a novel database containing the universe of financial
     > advisers in the United States from 2005 to 2015, representing
     > approximately 10% of employment of the finance and insurance
     > sector. Roughly 7% of advisers have misconduct records. Prior

7% seems astonishingly high to me but I would offer two caveats.

First, while this research is significant in assigning hard numbers for perhaps the first time, I don’t have a point of reference. Pharmacists steal narcotics, physicians commit insurance fraud, and lawyers violate due process guidelines. Most any profession that has an ethics board (Realtors too) has dealt with significant misconduct. I really need to see these numbers from other industries to have a better context for the current article.

The second caveat is that our authors have lumped the finance and insurance sectors together. We therefore don’t know to what extent the two separately contribute. This seems like a significant qualification of many conclusions we might possibly take from the article.

     > offenders are five times as likely to engage in new misconduct
     > as the average financial adviser. Firms discipline misconduct:

With a rate of recidivism that high, why are these advisers even allowed to remain a part of the industry once they have been found guilty? Maybe if this research has significant impact they no longer will. Then again, since nobody knows if this is more a comment on the financial or insurance industry, perhaps nobody should take it seriously at all.

     > approximately half of financial advisers lose their job after
     > misconduct… of these advisers, 44% are reemployed in the
     > financial services industry within a year…

I would think lifetime bans for misconduct could go a long way toward cleaning this up.

I’ll continue in the next post.

The Pseudoscience of Trading System Development (Part 5)

My last critique for Perry Kaufman’s system is its excessive complexity.

I believe the parameter space must be explored for each and every system parameter. For this reason, I disagree with Kaufman’s claim of a robust trading system. He applied one parameter set to multiple markets but I don’t believe successful backtesting on multiple markets is any substitute for studying the parameter space to make sure it’s not fluke. Besides, I don’t care if it works on multiple markets as long as it works on the market I want to trade.

When exploring each parameter space, the analysis can quickly get very complicated. I discussed this in a 2012 blog post. In Evaluation and Optimization of Trading Strategies (2008), Robert Pardo wrote:

     > Once optimization space exceeds two dimensions
     > (indicators), the examination of results ranges
     > from difficult to impossible.

To understand this, refer back to the graphic I pasted in Part 2. That is a three-dimensional graph with the subjective function occupying one dimension.

For a three-parameter system, I can barely begin to imagine a four-dimensional graph. The best I can do is to imagine a three dimensional graph moving through time. I’m not sure how I would evaluate that for “spike” or “plateau” regions, though: terms that refer to two dimensional drawings.

For a better visualization of what I’m trying to say here, this video shines light on Pardo’s words “difficult to impossible” (and if you can help me spatially understand the video then please e-mail).

Oh by the way, Kaufman’s system has seven parameters, which would require an eight-dimensional graph.

At the risk of repetition, I will say once again that Kaufman is not wrong. Just because it doesn’t work for me does not mean it is wrong for him.

And that is why I have pseudoscience in the title of this blog series. That is also why I use the term subjective instead of objective function. A true science would be right for everyone. Clearly this is not. It’s wrong for me and it should be wrong for you but it is never wrong for the individual or group who does the hard work to come up with and develop it.

The Pseudoscience of Trading System Development (Part 4)

Today I want to continue through my e-mail correspondence with Perry Kaufman about his April article in Modern Trader.

Kaufman e-mailed:

     > are not particularly sensitive to changes
     > between 90 and 100 threshold levels, so I
     > pick 95… Had it done well at 95 and badly
     > at 90, I would have junked the program.

Junking the system and not writing an article on it would be the responsible thing to do. He can claim to be that person of upright morals but it doesn’t mean I should trade his system without doing the complete validation myself. I’m quite sure that even he would agree with this.

In response to my comments, Kaufman sent a final e-mail:

     > I appreciate your tenacity in pursuing this…
     > I’m simply providing what I think is the basic
     > structure for something valuable. I had no
     > intention of proving its validity scientifically…
     > I’m interested in the ideas and I will then
     > always validate them before using them. For
     > me, the ideas are worth everything.

Kaufman essentially absolves himself of any responsibility here. He did nothing wrong in the article but I, as a reader, would be wrong to interpret it as anything more than a collection of ideas. It may look like a robust trading system but I have much work to do if I am to validate it and adopt it as my own.

What can give me the confidence required to stick with a trading system is the hard work summarized by the article but not the article itself. If the sweat labor is not mine then I am more likely to close up shop and lock in maximum losses when the market turns against me because paranoia, doubt, and worry will come home to roost. When I do the hard work myself (or with a team of others to proof my work) then I have a deeper understanding of context and limitations, which is what gives me the confidence necessary to stick with system guidelines.

If this sounds anything like discretionary trading then it should because I have written about both in the same context. It’s also the same reason why I recommend complete avoidance of black box trading systems.