What is My Opponent Holding?

Having spent far more hours playing poker than sanity allows, I like to think that I have more than money to show for it. I hope that I have come away with lessons that apply to business, investment, and life in general. That may be a stretch, but I cling to that illusion.

I am constantly trying to figure out why the best players are that good. It’s easy enough to see why the bad players are so bad. They bluff too often, call too often, don’t raise to protect their hands, don’t control their emotions, and have no understanding of position. You can become a mediocre to good player simply by addressing these problems, but great players have something else. As a matter of fact, even the great players still have some of these problems on an occasional basis.

Something else makes them great. I don’t think it is that the best players have more heart, more brains, or more luck, though everything does go easier when you have more advantages on your side. I think what separates the great players is their awareness that poker is a relative game. The great players spend most of their attention focusing on what their opponent has. Bad players often don’t think at all about the opponent’s cards, good players think about them at the time of decision, and great players think about them even when they are not in the hand. Every time you show a hand down, you are giving a great player another weapon to beat you with.

The great player knows that there is no greater advantage in poker than knowing your opponent’s cards. We could all be poker millionaires if we knew what our opponents have and they don’t know what we have. One of the best examples of this was a couple of years ago in the main event at the World Series of Poker. Sammy Farhar called a massive pre-flop raise early on the first day with only a pair of deuces. He knew that the nervous amateur making the bet would not have made that bet with anything other than a pair of aces. When a deuce came on the flop, Sammy got the rest of the amateur’s chips. I’m don’t think that the implied odds that he got really justified Sammy’s call, but know just exactly where he stood was a powerful incentive that he could not resist. He made it pay off.

Daniel Negraneau is always talking about what his opponents have when he is heads-up in a hand. I am sure that he only refrains from discussing what other people have in their hands in other situations because that would be outside the etiquette of the game, but I am sure he is thinking about it all of the time. I find Daniel the most instructive of the leading pros to watch for just this reason. Listen to the questions that he asks himself.

Why did he bet that much? Why did he check? What did that card do for him? How likely is it that my opponent is bluffing? Is this a guy who only bets the nuts? How nervous is this person about this bet? How many chips does this opponent have left? What is this opponent likely to do if I raise?

Daniel doesn’t always come up with the right answers and he’s quick to notice when he is wrong, but he is always asking and answering the questions. That gives him many chances to be right than he would have if he never thought of those questions.

It’s just like that in business and investing. Asking all of the right questions doesn’t guarantee that you will win, but it does increase your chances dramatically. There are never any guarantees, but there are lots of edges if you look for them.

Just spend a few minutes thinking about the last few years at some companies disappeared.  They disappeared not because they came up with the wrong answers; many disappeared because they simply didn’t ask the right questions.


The Anatomy of a Quantified Trading Process

A quantified trading system is a system that can be automated, but it is not necessarily an automated system. A quantified process is precisely defined, uses numeric inputs, and produces measureable outputs.

Each quantified trading system starts with a Profit Plan. A profit plan is a theory that describes the actions that will be followed under the plan and explains how those actions will be converted into profit. Never forget that your profit plan is a theory. If it works, it is a good theory; if it doesn’t, it’s not. Theories of profit rarely become laws.

All theories start with observations. For example, you might first observe that stocks tend to bounce back after a sell-off. That makes you look closer, and you find this behavior seems to be a lot more prevalent in stocks that are doing well than in stocks in the general process of tanking. Your conceptual theory might then become “If a stock is doing well overall, you can make money buying on the dips Dump them as soon as the price bounces back.”

That’s a theory, but not a quantified theory. You can make it a quantified theory, as Connors and Alvarez did, by adopting numbers that define “doing well overall”, “the dips”, and “bounces back”. I am sure there are a lot of traders out there that did well enough with this rule with no quantification, but quantification brings two specific advantages. Once a theory becomes quantified, it can be executed by a computer, and once it can be executed by a computed, it can be tested exhaustively.

The process that Connors and Alvarez define on page 62 of Short Term Trading Strategies That Work is a good example of what is needed.


1. The S&P 500 Index is above the 200-day moving average

2. The 2-period RSI of the S&P 500 Index closes below 5.

3. Buy the S&P on the close.

4. Exit when the S&P closes above its 5-period moving average.

Step number one checks to see if the S&P is “doing well overall”. Is the 200-day moving average the best test of this? You can test for this. (We did, and found that every long-term average was almost equally effective so 200 is as good a number as any.)

Step number two is a quantified definition of how to spot a dip. You can now test whether this works, Is this the best indicator? You don’t know, but you can now test it against other alternatives. Quantification does not start with the assumption that you know the right numbers; it starts with the assumption that you can test and discover the right numbers.

Finally, step four quantifies what a bounce back is. Is the 5 period average the right indicator? Again, you can test for that.

A quantified system has rules that can be automated and tested. If you are automating a quantified trading system, keep in mind that the ability to run tests is likely to be the most important contribution of such a system.

High Probability ETF Trading: 7 Professional Strategies to Improve Your ETF Trading

If you are using any kind of a system based on technical analysis, you need to own and read this book as well as the earlier work by the same authors (Larry Connors and Cesar Alvarez), Short-Term Trading Strategies That Work. Both books are clear and understandable, and both lay out approaches that really do work.

The difference between the work of  Connors and Alvarez and almost everyone else is that they show you exactly how to APPLY commonly available statistical tools.  Everyone who has spent a couple of hours watching CNBC knows what the VIX is, for example, but it is just noise to the vast majority of people.  Connors and Alvarez provide explicit instructions that enable the application of this number to buying and selling decision making..

Take the 200 day moving average, for example.  A fine number, easily available from a number of sources, but specifically how should it affect your trading strategy?  Connors and Alvarez use this measure very simply and directly.  If the target security is above the 200 day moving average, only consider long moves.  If it is below that average, only consider short moves.

This is simple and measurably effective.  You can test any of the strategies in any of their books with and without this rule, and the difference is clear.  The strategies work when this rule is included; they don’t when it is not.

This book introduces their approach to short selling and money management tactics. The short selling tactics are a logical extension of their long strategies, but the money management is a new wrinkle. Like most of their presentation, it’s not fancy but it does offer a quantified approach for averaging into a position. The best aspect of everything that these books offer is a solid foundation for quantification because without that foundation, experimentation is useless and progress is uncertain.

The only quibble  that I have with either book is over the definition of the word “strategy’. As they say more than once in this book, it’s all about selling into overbought conditions and buying oversold securities. One strategy for which they present several effective tactics. I’m not knocking this. It’s a good strategy, and they make it better when they only buy in healthy markets and sell in overall weak markets.

They present a lot of useful statistics about a wide variety of indicators, but they are only looking for indications of overbought or oversold markets. The indicators they suggest are pretty reliable and work effectively when used as they suggest, but they are not different strategies. For example, you could not diversify by using several of their different strategies because they all tend to lead you into the same trades. That is, security A is overbought or oversold. Several indicators may reveal this, but that does not mean that there are several different opportunities.

A really good book makes you ask questions. For example: Which indicator is the best? Is there a combination of these indicators that is more effective than any single indicator on its own?

Connors and Alvarez also present a variety of indicators to tell you when to get out of a position. Each of these indicators point to the market no longer being oversold or overbought. Notice that the strategies are not looking to profit further in that direction. The end-of-day aspect of the exit rules may be trying to catch a little of that over-bounce. They make a point of saying that you shouldn’t get out during the day if you hit the target for that day. I suspect they are right, but that is one of the few assertions that they do not back up with statistics.

Engineering is all about gathering and evaluating measurements, then putting those measurements to work.  This book, like its predecessor, provides a good hands-on introduction to the basics of financial engineering.

How To Foster the Starting of New Businesses

The only government program that I have seen that really ever seemed to do any good as far as creating new businesses and new jobs was the plan that allowed people to take their unemployment compensation and use it to start a new business. I am sure that most of those businesses failed because most new businesses fail, but some surely succeeded and employed people. Everything else I see coming out of the government really is not much help to the really small business person.

First, the government needs to stop doing things that get in the way. In particular, the government needs to stop getting in the way of new businesses trying to raise capital. For example, the changing of a “qualified investor” was made five times as stringent during the last year of the Bush administration. Maybe it was time to do this if you believe that the old law made sense because inflation has surely changed the notion of what is a comfortable nest egg, but the old law limits the right of ordinary Americans to be capitalists as much as any law on the books.

Making this law more stringent is a huge case of Nanny-state. You can’t foster new businesses, i.e. encourage people to take risks, by saying that they are unqualified to take them. The government does far more to prevent people from investing in legitimate new businesses that should have a beneficial effect for the whole society than it does to put people like Bernie Madoff out of business. Whether or not you believe that protecting people from themselves is a legitimate role of government, you have to conclude that the current rules and processes do not do everything they can to promote new business. Let’s take a look at what else they could do.

Create a Special Tax Break for Providers of Start-Up Capital

Should the guy who makes a hundred grand betting on Joe’s Widgets pay the same tax as the guy who made a hundred grand on Bank of America? Not if you want people to be eager to participate in new ventures. Is this fair to Bank of America, Microsoft, and IBM, to name a few? Not really, but they don’t need start-up capital. The cost to them is small and likely is a less than appropriate tax on their hugeness. Do you want create more small businesses or cater to the status quo? If you want a vibrant economy, you better not be catering to the status quo.

I’d like to see the first hundred percent return on such investments be tax free. That is, if you put up a million dollars to get Joe’s widgets started, you would not pay any tax on the first two million you took back out after Joe hits it big. That is, you don’t get taxed on getting your money back, and you don’t get taxed on the first matching win. I’d go even further and tax only fifty percent of the next hundred percent, and only seventy-five percent of the next hundred percent.

I don’t think that this is overly generous since most new businesses fail without ever making a profit for the investors, but it is a heck of a lot better than the deal angel investors get today. (They call them angels because they require so much faith.) The only way to get more money into these operations is to change the risk/reward profile. Most investors are not math-impaired.

Should investors pay a higher tax rate if they made their money from an organization that got bailed out by the government? They should, and actually they already are because the government is taking a big cut.

Problem/Opportunity Funding

Our country faces many problems and many opportunities. There is even occasionally some degree of consensus on what is a problem and what is an opportunity. I think the government and/or private agencies need to be able to support new businesses that can demonstrate that they are solving a problem for the community or opening an opportunity that will increase the prosperity of the community. The problem with such programs are that they disrupt the status quo, but a healthy capitalist economy is one that fosters change. Change is always going to come, and it will run you over if you are not ready for it on every level.

Foster Competition

It was a sad day when Packard went out of business, but it wasn’t devastating. Packard was just one of many auto companies, and they failed all the time. I’m not sure how much GM and Chrysler ended up with, but we could have started hundreds of new car companies at one hundred million dollars a shot. Wouldn’t that be a better use of our money than giving it to people who have proven they can’t manage a company? Wouldn’t that provide more jobs for auto workers? Wouldn’t that ensure that we would offer the world a broader choice in automobiles? Wouldn’t that mean that all cars would get better. The answer to all of these questions has to be yes if you believe in capitalism.

The auto bailout is exactly the wrong thing to do if you want to solve the problem. If it works, we end up with companies that should have failed and which are almost certain to face the same kind of problem in the future. If it doesn’t work, we are out billions and still have the same economic problems we were trying to prevent. A future with fifty car companies is a future far less prone to economic stresses than a future with three.

Redefine Small Business

A Company with $250 M in revenues is not a small business that needs help from the government. By the time a company is generating $10M a year in revenue, it should be able to stand on its own and raise capital on its own. A company needs the most help when it is just a business plan, or a business that is attracting attention and needs a little help to get to the next level. These are the companies that will increase employment, but they are also the companies that have the hardest time getting money from the banking industry. We need a government-guaranteed loan program that focuses on really small businesses that need to get to the point where they can inspire faith.

The most explosive growth of business in the micro-capitalism sphere, and there is a huge gap between that level and the level where the government will even recognize you. We either need to come up with a private solution to this, or the government ought to jump in.


I believe that we can make American business young again with just a few simple changes in our tax law. If we let everyone use their own judgment about these bets, it will help even more.

The government shouldn’t need to participate beyond that, but if it does stick its nose in, the focus needs to be on solving the problem, not in protecting the status quo – the path of least resistance that government always follow.

Nobody Saw It Coming

It doesn’t matter what you are talking about – the dot com crash, the subprime crash, or the tulip bubble. The years after such a crisis are marked by protestations that nobody saw the crash coming. In fact, those protestations are almost always false. True, almost everyone did drink the Kool-Aid, but there were always those that said: “No thanks!”

One of the good things about CNBC is that it creates a record of these events. They know who drank the Kool-Aid and who did not, and they have it on tape. When Peter Schiff was running around telling people that the subprime bubble was the biggest bubble of all time, both CNBC correspondents and their guests howled with derisive laughter. The Daily Show was kind enough to put together a collage of these interviews, and you can see that he was telling the straight story and was being treated like he was a total clown by people who were being total clowns themselves at the time.

Peter may not be the most charming person you ever saw on TV, but even if he had been wrong, he would have deserved significantly better treatment. Given the fact that he was right and his detractors were nothing less than fools, you would think there would have been quite a few public apologies, but I guess Wall Street doesn’t work that way. While lots of people on Wall Street are wrong most of the time, admitting in public that you were wrong is rather exceptional behavior.

There’s always a Peter Schiff, the guy who has it right. He’s always going to be the one who says the bubble will burst, and the guys who have it wrong will be saying, “that will never happen.” The guys who have it wrong are almost always hoping that it will never happen, but most of them don’t believe in never any more than you or I do. They mean “That just can’t happen while I am making all of this money.”

You should pay close attention whenever you hear that phrase or anything like it on CNBC, The more times you hear it said about any possibility, the more likely it is to happen. Wall Street should keep a “That will never happen” Index so that we could all track these events. They wouldn’t be talking about it if it wasn’t a possibility, and the more they talk about it, the more likely a possibility it has become.

Does that mean that any particular disaster will happen in a predictable time frame? Not necessarily, there are a lot of People that always say things like Peter Schiff, and most of them are wrong far more often than they are right. However, if the doomsayers are talking about your favorite investment or something that directly affects your favorite investment, you do need to sort through the doomsayers with an open mind. You don’t just want to say “That will never happen” or you soon may be saying “Nobody could have seen this coming.”

The Odds are Not Immutable

Some people grow up with the idea that the odds are the odds even though it turns out that this is rarely true.  In almost every situation there are ways to adjust the odds.

Let’s look at a situation dominated by the odds, a single-table poker tournament.  A coomon form has nine players.  The winner gets fifty percent of the purse, the second-place finisher gets thrity percent, and the third place finisher gets twenty percent.  The house usually collects a fee of around five to ten percent for running the game.

If the odds dominate, every nine games on average you will have one first, one second, and one third.  If you are playing for one hundred dollars, you are out ninety bucks at this point if the house is taking a ten percent cut.

Obviously some people lose far faster than this and some people make money.  Both the big losers and the big winners have changed the odds.  The difference is that the winners know how they are changing the odds; the losers don’t.

Aggression and lack of aggression is part of the problem in a no-limit or pot limit tourney.  In the early part of the tournament, you want to be aggressive when you have the best hand, and you want to fold when you don’t.  The most important way to change the odds in this kind of event is to make sure that you are almost always one of the final six.  If luck dominates from that point on, you will get one first, one second, and one third out of every six games.  Now you should be making a few hundred dollars every nine games.

Note that this doesn’t always work.  Sometimes you can’t be in the final six because you run into bad luck.  Bad things can always happen, but if you play only the very vest hands when everyone is still in the game, you are going to fold most of the time.  In most of these events, there are usually some people with no patience.  Show a little yourself, and you should be in the final six ninety percent of the time.

Once you are in the final six, your next goal should be to change the odds of finishing in the final three.  If you have acquired a lot of chips, you can get to the final three by continuing to play only the best hands, but that is not always the best strategy.  Sometimes you want to raise with mediocre or even relatively poor hands.  This actually increases your odds.

Take a game like Omaha High Low, for example,  Some hands have significant advantages and some have serious deficiencies, but most hands are almost even bets against almost almost all other Omaha hands.  If you have a hand that is only forty percent against one other hand and only twenty percent against two other hands, you don’t have a calling hand.  However, depending on your opponents, you may have a raising hand.

When you raise, you win one hundred percent of the hands where your opponents do not call.  If you get them to fold half of the time, and win forty percent of the time when they call, you are going to win seventy percent of the time.  If they reraise, that’s another proposition.  If you call, you are usually up against a better hand.

The odds are also changed by who your opponents are and how they play.  Sometimes you are up against oppoents that always fold if you raise.  Raise these people often.  When they finally call, it doesn’t matter because they don’t have any chips left.

Sometimes you are up against people who always call.  When you call a raise, you need to win the hand with your cards.  That means that callers will only win as many hands as their cards allow.  That demonstrabbly is the path for a slow steady losing pattern because of the house fees, but it changes your odds too because it limits the effects of those raises.

You have to be a lot more selective about the hand syou raise with when you face a calling station.  Raising against them has the same odds as calling, but your odds on winning the tournament are increased dramatically when you know hwo is a calling station and who isn’t.  When you know you have the best hand, bet.  Don’t try to slow-play these players.  They won’t bet into you.  You make the most when you make them call.

As you can plainly see from these examples, two simple risk management procedures can transform a mediocre losing player into a mediocre winning player.  The game offers hundreds more.  Discover and apply them.

Real life and those gambles we call investments are much more complex than a game of poker.  That complexity means that there are far more opportunities for you to institure simple risk management procedures that tilt the table in your direction.  You want the chips to fall towards you, don’t you?

Common Sense Risk Management

Taleb is right when he talks about risk management failing so dramatically in part because of its reliance on statistics, but the problem is neither statistics nor statisticians.  That’s a lot like blaming car manufacturers because people have auto accidents.  They might be the cause sometimes, but most of the time they are the fault of someone who bought the car and misused it.  Statisticians know the difference between ninety-nine and one hudred percent; non-statisticians muddy the line between four to one and a sure thing.

Most of the real problem was really that common sense was not applied.  Basic risk management is almost all common sense.  It begins with asking what can go wrong.  Next, list each risk clearly and completely.  Third, develop a plan to prevent or at least mitigate each risk.

I can’t believe that this is new information to anyone, but it is apparent that this process was either skipped or sabotaged in case after case.  This failure was most spectacular in banking and insurance, i.e., risk management professions.  Since it is hard to believe that they skipped the process entirely, we need to understand where they sabotaged it.

I think statistics was used to sabotage the process.  Many people looked at statistics that said that a bet would win ninety or even ninety-nine percent of the time, and said “That will never happen.”  I heard it myself dozens of times, and what may be most surprising, that same people who were burned by that leap of faith continue to be the same people who are still uttering those words.

If you are a risk manager of any kind and ever say that something will never happen, you are not the guy for the job.  If you can imagine it happening, it almost certainly can happen.  That doesn’t mean that the the fantasized disaster can or will happen, or that you can do anything to prevent the disaster or mitigate the effects.  However, it is certain that you cannot come up with a plan ot deal with the disaster unless you treat it as a real threat and make a plan.

Note that you can recognize a catostrophic risk and still do nothing about it.  At some level that is okay.  For example, if you sell property insurance in Florida, you should recognize that it is a possibility that one hurricane could wipe out Florida — take it right off the map.  Obviously, that would oxer-extend the resources of all of the insurance companies in Florida, and they would almost certainly fail without stunning balance sheets and geographical diversification.

You can’t stop such a hurricane, at least with our present technology.  If the hurricane cuts a wide enough swath through your customer base, geograhic diversification will help a little.  The insurance industry has withstood some major blows, but so far the hurricanes have remained small enough so that only a few companies actually failed.  If the U.S. got hit with a 250 knot storm that hit the country for ten days, insurance companies wouldn’t be the only organizations to fail.

Has it ever happened?  To the best of our knowledge, it has not happened on this planet.  However, similar events do occur constantly on both Venus and Jupiter so that we know that physics isn’t standing in the way.  There is a non-zero chance of it happening here, but when the chances are small enough and the possible remedies are so ineffectual that you simply have to accept that risk.  A financial disaster at that point would probably be insignificant given the scope of the natural disaster.

Many people recognize risk know how to mitigate that risk, and do nothing about it.  People who drive without insurance and people who do not back up the work they’ve done on a computer are two examples that spring immediately to mind.  The issue here is cost.  They don’t want to spend either the money or the time.

Again, deciding not to spend the money or the time to mitigate any risk may still be a valid decision, but if you are a risk manager, you need to document the risk as well as the reason why the mitigation plan was too expensive.  You should review each risk categorized this way regularly because conditions change.

An obvious example of this is buying stocks on margin.  If you buy stocks on margin at ten bucks a share, it is very expensive to buy ten dollar puts to cover them.  Prohibitively expensive, in fact.  However, if the stock moves fifty dollars a share, puts with strikes of twenty dollars or less will be extremely cheap.  If I was forced into a position where I had no practical option but to buy stocks on margin (usually a good position, I would review the cost of puts on a monthly and maybe even weekly basis.  But I digress …

Risk management is almost never about statistics.  Statististics are used to justify risk management decisions,  but no risk management plan should need statistics as a defense.  Ask the question:  What can go wrong?  Answer the question.  Deal with the answers.  That’s all it takes.