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.

History as the Mediator of Meta-Analysis

In regression class I finally understood the difference between mediation & moderation.  A significant break through in the way I thought of variables influencing one another.  The most interesting thought was the relationship between time & meta-analyses. 

During a meta analysis we collect as many studies as possible which experimented on or evaluated  a particular variable.  We then compile these studies, do some rather interesting statistics and postulate a conclusion about the influence of this  particular variable on a dependant variable.  An example would be taking all the studies on Self-Esteem and exploring its influence on the personality trait extroversion.  However, in my work with the psycho-market model, slopes & relationships gain & lose significance over time.  A relationship may be significant one year and non-significant the next.  Time has mediated (or something else) the significance of this relationship.  In running experiments we try to control for "the something else," but we often never control for time.  I propose that the fluctuation of effect sizes & significant/non-significant relationships are mediated by not only standard experimental issues, but time.  I would go as far as saying that time would account for the majority of variance above & beyond standard experimental issues related to the fluctuation of meta analysis data.

Several time & other related errors do occur that need to be addressed.  When conducting a psychological experiment we often forget it was done at a certain period in time, with a particular zeitgeist.  Therefore when conducting meta-analyses we may be combining significant & non-significant time sensitive data which may eliminate any true relationship or produce a type 1 error.  In addition, every experimenter knows of the bias journals have towards significant results & meta-analyses typically only use these results, collecting very few datas from non-significant sources.  Lastly, it's erroneous to think that the knowledge an individual holds about Self-Esteem or Organizational Justice was the same when the study was first conducted to when the meta-analysis was calculated.  Some erudite professors may rebuttal with "This is why we operationalize constructs."  My response to that would be look at the literature on survey responses & actual occurrences of these events. I think one would be hard pressed to find someone who hadn't incorporated their own version of what the variable means.

In short, I would attest that the fluctuation across experiments, evaluating the same variable, is most attributable to time above & beyond standard experimental errors.  The question becomes how can we control for time?  I have outlined some suggestions.

1) Use a linear variable to depict the change of time.

2) Create a zeitgeist construct to be included in the analysis

3) Understanding this may be one variable that is uncontrollable & accepting it's placement within the experimental meta-analytic frame work.  Therefore, not accepting meta-analyses as the end all tell all, as they often are.

Equity Traders & Attribution Theory

Attribution theory, according to Wikipedia (which offers a very accurate definition) defines it as "The theory that is concerned with the ways in which people explain (or attribute) the behavior of others or themselves (self-attribution) with something else." Attribution theory was originally pioneered by Fritz Heider in his conjecture that humans operate in amateur scientist ways; mainly that we gather information and create internal/external causal inferences.  

Research in this area began to unravel the complex mechanisms in which we humans make these inferences.  Most important to us as investors are the mechanics that operate when we are 'depressed' or 'happy'.  Individuals use three grand schemeas to categorize attribunal inferences 1) Internal/External, 2)Stability, 3) Control.  Specifically for depressed  individuals, they attribute failures to: Internal, Stable, and Uncontrollable properties.  An example would be, "I can never pass this test, because I'm dumb and will always be dumb."  Very extreme but makes the point.  In the instance of a success: External, unstable,  and uncontrollable.  An example would be, "I got this A because the teacher likes me and I was lucky."  On the Flip side individuals who are happy respond in a polar way.  For failure attributions are: External, unstable, and uncontrollable.  For success attributions are: Internal, Stable, and controllable.  

It's a very big difference.  The most significant difference to note is that while depressive individuals blame themselves for failure, 'happy' individuals do not.  In the instance of success depressed individuals attribute it to something other than the self while 'happy' individuals take the credit.  On one hand it's very self defeating and on the other very self protective.

How does this relate to traders & How can it help us?  Very simply actually.  Using the Psycho Market Model we need to look for the ways traders attribute their failures & successes during a span of trading time (days, weeks, years, etc).  Are traders looking to blame the market, their system, themselves, or something else?  If we can get at the core of traders attribution styles we can use the plethora of information from entity & learning theory to 'reteach' traders how to make accurate attributions regarding their trading activity.  Accurate attributions will allow traders (if they so choose) to introspect and become meta analytic machines which can recognise their own weakness because they know where the fault lies.  Turning those weaknesses into large profits.

A very interesting variable that does not exist in life are stop loses.  How does this variable interact with attributions of success or failure in trading equities?  Research needs to be conducted in this area to enlighten market participants of their own possible short comings.  

In the coming months I will be developing a traders version of the Attribution Styles Questionnaire originally developed by: Peterson, C., Semmel, A., von Baeyer, C., Abramson, Metalsky G.I., & Seligman, M.E.P. in 1982.

Credit where credit is due:

Dan Ariely  had suggested that if I were to pursue a model that synthesized economics & psychology that I may begin by looking at how traders may vary from 'normal' people.  This is one instance, in which they may.  

References:

Hewitt, A., Foxcroft, D., & MacDonald, J. (2004, November). Multitrait-multimethod confirmatory factor analysis of the Attributional Style Questionnaire. Personality and Individual Differences, 37(7), 1483-1491.

Peterson, C., Semmel, A., von Baeyer, C., Abramson, L.T., Metalsky, G.I., and Seligman, M.E.P. (1982). The Attributional Style Questionnaire. Cognitive Therapy and Research, 6, 287-300.

Attribution theory. (2009, March 18). In Wikipedia, The Free Encyclopedia. Retrieved 22:54, March 18, 2009, from http://en.wikipedia.org/w/index.php?title=Attribution_theory&oldid=278139181

My notes from Dr. Johnson's social psychology class at Hofstra University

Growth is Truely Exponential!

After being rejected by seven I/O PhD programs & two quantitative psychology programs, needless to say, my spirits were low. I desperately tried to make a case for synthesizing psychology & economics as a course of study in these programs. I tried to explain the relevance and the potential impact it may have upon the field of psychology. My words feel upon deaf ears. A retired professor I spoke with, who was the creator of market distribution channels as organized systems, said it best "If you want to make it in academics you have to plow what has already been plowed, just plow it deeper and more scholarly." I've never heard it put more candidly than that and realized that this would not be an easy journey. I began emailing professors again asking for advice, how they started their careers etc. Economists told me it was to psychological and psychologists told me it was to much economics. I spoke with names such as Dan Ariely, Bruno Frey, Alois Stutzer, and Fiona Lee to name a few. The search ended with little to show.

At last though I see some light! The old clinical gears in my head swirl with anticipation! Proliferation is in sight! I recently contacted Philip Pearlman who kindly spoke with me about his ideas regarding the market & psychology. I found someone who I agree with and I think will agree with my ideas as well. I may finally be able to bring the psycho-market model into fruition. In addition, my first Psycho-Market Model Oil Report, taking into account Psychological & Economic factors is coming to completion as well. Also, a PhD student Sayeed & I are going to begin a line of research regarding President Obama's Speeches. Additionally, my consulting work with a Rhode Island Bed & Breakfast, The Spring Seasons Inn has reached its first stage of completion. The analysis of past performance is complete and we will begin the development of customer surveys shortly. Lastly, I'll be graduating with a Masters Degree in Industrial Organizational Psychology from Hosftra University in May.

After much frustration & failure it's nice to stand up and let the scabs heal for a bit. Though I'm not afraid to get bruised again down the journey I am about to travel through. Growth for me has happen so quickly in such a short period of time that it seems unbelievable. Looking back at how miserable I felt at times it all seems worth it and is well noted to keep this in mind when experiencing even more difficult hardships.