A goodybye to Blogger but hello to AndrewIlardi.com

Recently, I've moved this blog to AndrewIlardi.com. Where I will post information regarding psychology, statistics, the stock market & there ever interchanging relation to one another. You can visit my new blog here for the newest posts from Andrew Ilardi. There are additional pages to peak into the other areas of my work. Check out the new site here.

What will happen to this blog?

Well I've always wanted a space to post about recipes, alternative medicine, and green living ideas, so this blog will now be reserved for that.

A History of Unemployment

Today I decided to create box plots of the United States Unemployment data dating back to 1948 to the most current month of October 2009. Here are the results... (made using R)

If you are new to box plots this website (NetMBA) does an excellent job of explaining it. The abridge version is this: the bolded line is the median, the rest of the lines which create the "box" represent 1st to 3rd quartiles. Any dot/circles outside the box represent outliers. The graph should be read as in January the median unemployment from 1948-2009 was 6.3%.

There is a nice little wave pattern that begins in January, at it's peak, then starts it's decline till May then buckles back up in June then declines as we move towards December and of course repeats. These data do suggest we have months where unemployment is highest (January) and lowest (October). It also suggests that some months have more variability (February having 1.62 standard deviation), while other have wider ranges (June).

I think this says a lot about our economy. During Quarter 4, (Holiday season) when most profits are made, unemployment is low because these business need to meet the higher demand. Consequently, they no longer need these workers after the holiday season, which may account for the step median decline in the unemployment rate from December to January.

What does this mean in our current economic climate? WATCH OUT! January numbers may be VERY interesting...

Other unemployment musings:

Obama calls for new ideas for creating jobs

Analysis of Obama's jobs summit

Unemployment claims drop for 5th week in a row

5th Times a Charm?

Above are the 5 major "Dow Theory" indicators I check for major trends: VIX, Dow Jones Industrial Average, S&P500, Oil, & NASDAQ. Notice each graph has 3 - 5 major bounces on a single trend line.

What's the concern? NASDAQ is the only index who has broken its major trend and and it will now be utilize as resistance. Additionally, oil can not break above $80.00 a barrel. Furthermore, the VIX is at a pivotal price point. All this information tells me there will be a correction soon. As the DOW begins to fall towards it's 5th bounce it most likely will break through. The S&P will follow suit breaking through it's 6th bounce on the major trend line.

Since March the market, as I describe it, has been drinking Red bull and Monster Energy drinks daily. It's time to come down from this surgery caffeinated high... Psychological studies have shown that humans love to watch people fall from the top, I don't think the market is an exception here. This is one of the psycho-market theories.

Why Are The Poor So Spend Happy?

We're in the mist of the mid-day trading session and I can't help but to noticed the amount of advancing stocks. The DOW is up, the S&P 500 is up and NASDAQ is advancing (http://finviz.com/). Normally this correlation doesn't startle me in today's market, however after reading the poor economic numbers that blew past analyst estimates, it does.

International Trade smashed through census estimates at -32.5 billion coming in at -36.5 billion. The U.S. year over year exports dropped 3.4%. Natural gas inventories dropped to 25 billion cubic feet from 29 bullion cubic feet. Finally, consumer sentiment plummeted! Annihilating census estimates of 71.0 coming in at 66.0. However, the market is up! (http://www.bloomberg.com/markets/ecalendar/index.html)

I relate this to bad psychological defense mechanisms. There is no fancy title for this pathology, plain and simple, it's over compensation (some may call it reaction formation [that's a fancy title]). In other words, since the reality of the situation is to harsh to accept (consumers feel terrible, inventories are down, large possibility of low holiday spending) we create an alternate reality/feeling that is much easier to accept. In this case, poor economic numbers indicate an unacceptable reality so the market is now over compensating by buying up more assets. Unfortunately, this is not the TRUE reality of the situation, only a pathological defense.

In short, stocks going down would be an appropriate response to such bad news, stocks going up after such bad news, is simply pathological.

Framing Effects on Micro Accounts - Where's your Edge?

Abstract

Trading micro accounts can be an arduous task, but it's my belief that the majority of that demand is psychological. Two factors play the largest role: Inherent resource scarcity notions & framing effects. To abate these effects we can use concrete financial information to combat inherit resource scarcity notions, while using the same information to battle the negative frame. This places the trader in a positive mind set making him/her more likely to choose probabilistically, a less risky choice, but also even if he/she does frame the trade negatively they are more likely again, to choose to the less riskier decision. Read on to discover how...

       If you've ever traded an account under 10,000, under 5,000 I know you can still taste the fear of trading with such minuet funds. Where one mistake, large enough, can end your trading career. I recently attended a talk Philip Pearlman gave regarding psychology of market participant behavior. One epic example resonated so deeply within me I was inspired to write this blog. The topic was resource scarcity. It immediately dawned on me (trading a micro account myself) how resource scarcity combined with framing effects are a lethal match, but one that can be remedied.

       Resource scarcity is something we no longer have a concept of, as Dr. Pearlman pointed candidly towards multiple platters of chicken wings that went untouched. Run out of resources? Are you crazy? Not in this life time. But this is a very valent concept for traders, always! Resource scarcity is as deeply embedded in us today as it was our ancestral brotheren (ever find yourself over loading your dinner plate at a family function or eating extremely quickly for no reason?). Your cash, your leverage is finite, as is oil & drinkable water, you just may not feel like it is. These resources can be depleted, some will just take longer than others. Though our resources are finite we can lose without losing it all, while building a renewable source of resources (eg farming).

       The next concept is framing effects. Amos & Teversky first discovered framing effects in the early 1980's with a simple study suggesting that when a problem is framed positively versus negatively (gain vs lose) we make different choices. To summarize a very complex study which you can read here, the main results were as follows. When a situation is framed positively we choose a less risky decision. When a situation is framed negatively we choose a more risky decision. In the example below you would be given these sets and asked to choose from first, either 'A' or 'B', then 'C' or 'D' for set 2. The results of the studying were staggering: 20% of people choose 'B' while 92% choose 'D' (the more risky choices). Reversing that, 80% choose 'A' in in set 1, while 8% choose 'C' in set 2 (the less risky choices). Furthermore, similar patterns held for varying amounts and probabilities. I modified this example from Econoport.org. For further inquiry a great paper is Levin I, et al 1998 paper, "Not All Frames are Created Equal." Notice how how set 1 contains the word 'gaining,' while set 2 makes use of the word 'losing'.

Set 1

Gamble A: A 100% chance of gaining $3000.
Gamble B: An 80% chance of gaining $4000, and a 20% chance of gaining nothing.

Set 2

Next, you must choose between:
Gamble C: A 100% chance of losing $3000.
Gamble D: An 80% chance of losing $4000, and a 20% chance of losing nothing.

       Here's the kicker...The very essence of the micro account is negative! Here's why. We're consistently told from the "experts" control your loses, control your loses and the other famous saying... control your loses. This banter negatively frames all accounts, but especially micro accounts because this is all we are told. What compounds this effect is that one wrong move can send you packing, perhaps even from your house. The anxiety that is produced through this notion also negatively frames your account. Lastly, you may also be unsatisfied with your micro account further compounding this negative frame. You may feel an anxiety, a lingering feeling that you need it to grow larger to some unknown amount, or that your gains aren't large enough. A negative frame combined with strong feelings of anxiety form a solid basis for why so many new traders and those with micro accounts take huge risks and get wiped out of the game. Veteran trader Shineguy said this in his interview with Howard Lindzen, "One big lose, that's all it takes."

       How can you remedy this idea? Drawing on The Cognitive Theory of Noise (CTN), talked about here by Phil Pearlman, we can establish a prescription. Abating these feelings negative feelings towards ones account by cognitive exercises. Just as Dr. Pearlman said in his talk, "You're not going to tell a guy who walked into your office saying, I just got divorced, fired, and am comtemplating sucided, to just snap out of it." There are two issues that must be address your hard wired conceptions of resource scacity & your negative feelings towards your account.

       To address resource scarcity in trading you'll need to really grasp the bearings and take inventory, so to speak, on your financial standing. This is included but not limited to such things as:

• How much are you willing to lose on each trade, but also how much are you willing to gain before you exit the trade?

• Given your current strategy how many loses can your account with stand before you have to throw in the towel?

• What are my long term return on investment goals for the year, for the month?

Notice that for each negative there is a positive. Now that you're armed with the facts regarding your resources you can effectively combat the hazardous notions produced by this hereditary function. We can now use this information to ameliorate the negative frame, by now speaking in terms of positives. If by chance we happen to revert back and frame the account negative we will choose the less risky decision. Lets use an example to illustrate how this all plays out.

       Lets say Joe Trader has 5,000 in his account and is unwilling to leverage his 5k into 15k. Joe is willing to take a $50 lose on each trade but is looking for a $150 move each time (a 1/3 risk/reward ratio). If Joe were to get stopped out of every trade he make at least 100 trades. Joe proceeded to approximate a number for his yearly ROI and found four great sources: national saving account average, the average return on the Dow Jones Industrial Average, Nasdaq, and S&P 500. Joe could then develop a number based on competing market assets. Joe wants his return to be higher than all these assets, otherwise he would just purchase those assets directly. Joe established a 10% ROI goal for the year. With 100 trades to make and a 10% return goal; Joe is challenging his biology with accurate knowledge of his resources framing them now in positive terms. Here is the trick, now that Joe knows he had 100 trades to make he uses this number in conjunction with $50.00 lose per trade. If you notice this is choice 'C' when a situation is framed negatively. The underwhelming majority chose 'D', but you won't anymore! The next step is critical, when joe places a trade he says to himself "I have X (100) trades to make, with a potential gain of $150. I will set a stop lose of $50." Here joe has now framed everything positively. This increases his chances of taking less risk probabilistically, but also forces him, if he happens to slip "off the wagon," to think in terms of choice 'C', (A definite lose, not a probabilistic one).

       The moral of the story is to begin thinking (framing) in terms of positives, not negatives. We use concrete financial information to combat the inherit resource scarcity notions, while using the same information to battle the negative frame. This places the trader in a positive mind set making him/her more likely to choose probabilistically, a less risky choice, but also even if he/she does frame the trade negatively they are more likely again, to choose to the less riskier decision.

Non-state of the Twittersphere

HubSpot’s State of theTwittersphere report, released Wednesday June 10, produced metrics based on 4.5 million twitter accounts. Arstechnica.com did a great job of summarizing the findings (accurately if I may add), but did not analyze these findings. The State of the Twittersphere report, in my opinion, is plagued by one ill metric which confounds important results. “Active” versus “Inactive” twitter users.

HubSpot defined “Inactive” as a twitter user who has less than 10 followers and less than 10 friends and less than 10 updates (All three criteria must be met). Twitter is a service based on a temporal design. In order words, as the famous saying goes, “What are you doing right now?” By defining an active user as someone who has less than ‘X’ criteria eliminates the very essence of the twitter model, time. An active user, as defined by HubSpot, may be a member 3 or 6 months old who has abandoned his account, but still met the criteria. It may have behooved Hubspot to create a criteria based on time rather than activity, to model the very heart of twitter’s service.

With that said, Hubspot produced ill metrics regarding average tweets (.97 tweets per day), life time tweets (average user tweeted 119.34), and following to follower ratio (.7738 OR about 8/10). According to Hubspot, we have more followers than people we follow (about 8/10). Take a look at any celebrity profile for some anecdotal evidence (Ludacris, Perez Hilton, Ashton Kutcher). What’s interesting are the population metrics stating that 44.5% follow 1 or more people and 47.29% have 1 or more follower. Firstly, they never took out “inactive users” (even according to there own erroneous definition). These could be people who joined the service 2 years ago and have not used it since. A user who tweeted 89 times, meet all of there criteria for active, but hasn’t logged on in nine months. Secondly, because they didn’t classify active & inactive while computing these ‘global’ metrics, you can not calculate a true metric of which users are activity reading tweets (following). They calculated a ratio of about 8/10, but this ratio is inaccurate, because of the non-temporal nature of their “active” user definition. What this means is that even though they say for every 10 followers you follow 8 people. Those 10 followers may be dead users who have not read any of your tweets since they last logged on 11 months ago.

What does this matter? It’s important to understand their definition of “active” is not temporal, which is the model twitter has thrived on. It’s also important to understand that the population metrics may include users who have not logged onto the service in quite some time. Furthermore, their “active” user metrics are plagued by the same ill “active” user definition. This translates into a publishing issue, in that a person may have 1 million followers but only some unknown percent are actually reading those tweets. Several questions remain in that: How do you define active? How do you measure whose reading your tweets? And lastly, How do you measure if users are extracting entertainment value from your tweets? Even though you may have 1,500 followers 20 may only be listening.

Synthesizing Psychology & Market Participant Behavior

Recently I had the pleasure of teaming up with Philip Pearlman to establish a theory involving clinical prescription for irrational market participant behavior, The Cognitive Theory of Noise (CTN).  Pearlman is currently an investor in the popular stocktwits.com, a contributor to Jim Cramer's thestreet.com, and a former successful hedge fund manager with 50 million in assets under management.  Pearlman began the development of CTN while working towards his doctorate in Clinical Psychology. As I was finishing my masters’ work in Industrial Organizational Psychology, I contacted Pearlman regarding his interest in synthesizing psychology and market behavior. Needless to say, we both understood the power of such an amalgamation.  

Though the Cognitive Theory of Noise (CTN) is still developing, the main premise as Pearlman had laid out here is looking at behavioral economic phenomena in its true framework of social psychological. Furthermore, because of clinical psychology’s kinship to social psychology CTN was nascent. Aaron Beck developed an empirically validated theory of treatment for abnormal behavior in society. It is with this accord that CTN can describe abnormal versus normal market participant behavior, but most importantly provide tools for clinician, trainers, and the like to implement prescription. In other words, provide treatment for irrational market behavior.

Over the next coming months Perlman and I will be posting examples, further advancing CTN. In the next week I will be posting a descriptive model of the Gambler’s Fallacy within the CTN framework, in tandem with providing prescription to help ameliorate its effect during trading.