{"id":12273,"date":"2014-06-26T17:03:02","date_gmt":"2014-06-26T21:03:02","guid":{"rendered":"http:\/\/n2value.com\/blog\/?p=12273"},"modified":"2014-06-28T09:38:07","modified_gmt":"2014-06-28T13:38:07","slug":"what-medicine-can-learn-from-wall-street-part-3-the-dynamics-of-time","status":"publish","type":"post","link":"https:\/\/n2value.com\/blog\/what-medicine-can-learn-from-wall-street-part-3-the-dynamics-of-time\/","title":{"rendered":"What medicine can learn from Wall Street – Part 3 – The dynamics of time"},"content":{"rendered":"

This a somewhat challenging post with cross-discipline correlations, some unfamiliar terminology, and concepts.\u00a0 There is a payoff!<\/span><\/p>\n

You can recap part 1<\/a> and part 2<\/a> here.\u00a0<\/span><\/p>\n

The crux of this discussion is time.\u00a0 Understanding the progression towards shorter and shorter time frames on Wall Street enables us to draw parallels and differences in medical care delivery particularly pertaining to processes and data analytics. \u00a0This is relevant because some vendors tout real-time capabilities in health care data analysis.\u00a0 Possibly not as useful as one thinks.<\/span><\/p>\n

In trading, the best profit one is a risk-less one. \u00a0A profit that occurs by simply being present,<\/span>\u00a0is reliable, and reproducible, and exposes the trader to no risk.\u00a0 Meet arbitrage<\/a>.\u00a0 Years ago, it was possible for the same security to be trading at different prices on different exchanges as there was no central marketplace. \u00a0A network of traders could execute a buy of a stock for $10 in New York, and then sell those same shares on the Los Angeles exchange for $11. \u00a0If one imagines a 1000 share transaction, a $1 profit per share yields $1000.\u00a0 It was made by the head trader holding up two phones to his head and saying \u2018buy\u2019 into one<\/span> and \u2019sell\u2019 into the other.*\u00a0\u00a0 These relationships could be exploited over longer periods of time and represented an information deficit. \u00a0However, as more traders learned of them, the opportunities became harder to find as greater numbers pursued them. \u00a0This price arbitrage kept prices reasonably similar before centralized, computerized exchanges and data feeds.<\/span><\/p>\n

As information flow increased, organizations became larger and more effective, and time frames for executing profitable arbitrages decreased. \u00a0This led traders to develop simple predictive algorithms, like Ed Seykota did, detailed in part 1<\/a>. \u00a0New instruments re-opened the profit possibility for a window of time, which eventually closed. \u00a0The development of futures, options, indexes, all the way to closed exchanges (ICE, etc\u2026) created opportunities for profit which eventually became crowded. \u00a0Since the actual arbitrages were mathematically complex (futures have an implied interest rate, options require a solution of multiple partial differential equations, and indexes require summing instantaneously hundreds of separate securities) a computational model was necessary as no individual could compute the required elements quickly enough to profit reliably. \u00a0With this realization, it was only a matter of time before automated trading (AT) happened, and evolved into high-frequency trading<\/a> with its competing algorithms operating without human oversight on millisecond timeframes.
\n<\/span><\/p>\n

The journey from daily prices to ever shorter prices over the trading day to millisecond prices was driven by availability of good data and reliable computing which could be counted to act on those flash prices. \u00a0Once a game of location (geographical arbitrage) turned into a game of speed (competitive pressures on geographical arbitrage) turned into a game of predictive analytics (proprietary trading and trend following) turned into a more complex game of predictive analytics (statistical arbitrage) was then ultimately turned back into a game of speed and location (High frequency trading).<\/span><\/p>\n

The following chart shows a probability analysis of an ATM straddle position on IBM. \u00a0This is an options position. \u00a0It is not important to understand the instrument, only to understand what the image shows. \u00a0For IBM, the expected variance that exists in price at one standard deviation (+\/- 1 s.d.) is plotted in below. \u00a0As time (days) increases along the X axis, the expected range widens, or becomes less accurate.<\/span><\/p>\n

\"credit:<\/a>
credit: TD Ameritrade<\/figcaption><\/figure>\n

Is there a similar corollary for health care?<\/span>
\n<\/span><\/p>\n

Yes, but.<\/span><\/p>\n

First, recognize the distinction between the simpler price-time data which exists in the markets, vs the rich, complex multivariate data in healthcare. \u00a0<\/span><\/p>\n

Second, assuming a random walk hypothesis<\/a> , security price movement is unpredictable, and at best can only be calculated so that the next price will be in a range defined by a number of standard deviations according to one\u2019s model as seen above in the picture. You cannot make this argument in healthcare.\u00a0 This is because the patient\u2019s disease is not a random walk.\u00a0 Disease follows proscribed pathways and natural histories which allow us to make diagnoses and implement treatment options.
\n<\/span><\/p>\n

It is instructive to consider Clinical Decision Support tools. \u00a0Please note that these tools are not a substitute for expert medical advice (and my mention does not employ endorsement<\/a>).\u00a0 See Esagi<\/a>l and diagnosis pro<\/a>. \u00a0If you enter \u201cabdominal pain\u201d into either of the algorithms, you\u2019ll get back a list of 23 differentials (woefully incomplete) in Esagil and 739 differentials (more complete, but too many to be of help) in Diagnosis Pro. \u00a0But this is a typical presentation to a physician – a patient complains of \u201cabdominal pain\u201d and the differential must be narrowed.
\n<\/span><\/p>\n

At the onset, there is a wide differential diagnosis.\u00a0 The possibility that the pain is a red herring<\/a> and the patient really has some other, unsuspected, disease must be considered. \u00a0While there are a good number of diseases with a pathognomonic<\/a> presentation, uncommon presentations of common diseases are more frequent than common presentations of rare diseases.<\/span><\/p>\n

In comparison to the trading analogy above, where expected price movement is generally restricted to a quantifiable range based on the observable statistics of the security over a period of time, for a de novo presentation of a patient, this could be anything, and the range of possibilities is quite large.
\n<\/span><\/p>\n

Take, for example, a patient that presents to the ER complaining \u201cI don\u2019t feel well.\u201d\u00a0 When you question them, they tell you that they are having severe chest pain that started an hour and a half ago. \u00a0That puts you into the acute chest pain diagnostic tree.<\/span><\/p>\n

\"Reverse<\/a><\/span><\/p>\n

With acute chest pain, there is a list of differentials that needs to be excluded (or \u2018ruled out\u2019), some quite serious. \u00a0A thorough history and physical is done, taking 10-30 minutes. \u00a0Initial labs are ordered (5-30 minutes if done in a rapid, in-ER test, longer if sent to the main laboratory) an EKG and CXR (chest X-ray) are done for their speed,(10 minutes for each) \u00a0and the patient is sent to CT for a CTA (CT Angiogram) to rule out a PE (Pulmonary embolism). \u00a0This is a useful test, because it will not only show the presence or absence of a clot, but will also allow a look at the lungs to exclude pneumonias, effusions, dissections, and malignancies. Estimate that the wait time for the CTA is at least 30 minutes. \u00a0<\/span><\/span><\/p>\n

The ER doctor then reviews the results (5 minutes)- troponins are negative, excluding a heart attack (MI), the CT scan eliminated PE, Pneumonia, Dissection, Pneumothorax, Effusion, malignancy in the chest. \u00a0The Chest X-Ray excludes fracture. \u00a0The normal EKG excludes arrhythmia, gross valvular disease, and pericarditis. \u00a0 The main diagnoses left are GERD, Pleurisy, referred pain, and anxiety. \u00a0ER doctor goes back to the patient (10 minutes) , patient doesn\u2019t appear anxious & no stressors, so panic attack unlikely. \u00a0No history of reflux, so GERD unlikely. \u00a0No abdominal pain component, and labs were negative, so abdominal pathologies unlikely. \u00a0Point tenderness present on the physical exam at the costochondral junction – and the patient is diagnosed with costochondritis. \u00a0The patient is then discharged with a prescription for pain control.\u00a0 (30 minutes). \u00a0<\/span><\/p>\n

Ok, if you\u2019ve stayed with me, here\u2019s the payoff.<\/span><\/p>\n

As we proceed down the decision tree, the number of possibilities narrows<\/strong> in medicine.
\n<\/span><\/p>\n

In comparison, price-time data – in which the range of potential prices\u00a0increase<\/b>\u00a0as you proceed forward in time.<\/span><\/p>\n

So, in healthcare the potential diagnosis\u00a0narrows<\/b> as you proceed down the x-axis of time.\u00a0 Therefore, time is both one’s friend and enemy – friend as it provides for diagnostic and therapeutic interventions which establish the patient’s disease process; enemy as payment models in medicine favor making that diagnostic and treatment process as quick as possible (when a hospital inpatient).<\/span>
\n<\/span><\/p>\n

We\u2019ll continue this in part IV and compare it relevance to portfolio trading.<\/span><\/p>\n

*As an aside, the phones in trading rooms had a switch on the handheld receiver – you would push them in to talk. \u00a0That way, the other party would not know that you were conducting an arbitrage! \u00a0They were often slammed down and broken by angry traders – one of the manager\u2019s jobs was to keep a supply of extras in his desk, and they were not hard-wired but plugged in by a jack expressly for that purpose! \"trader's<\/a>
\n<\/span><\/p>\n

**Yes, for the statisticians reading this, I know that there is an implication of a gaussian distribution that may not be proven. \u00a0I would suspect the successful houses have modified for this and have instituted non-parametric models as well. \u00a0Again, this is not a trading, medical or financial advice blog<\/a>.<\/span><\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

This a somewhat challenging post with cross-discipline correlations, some unfamiliar terminology, and concepts.\u00a0 There is a payoff! You can recap part 1 and part 2 here.\u00a0 The crux of this discussion is time.\u00a0 Understanding the progression towards shorter and shorter time frames on Wall Street enables us to draw parallels and differences in medical care […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"New Post: What medicine can learn from Wall Street - Part 3 - The dynamics of time http:\/\/wp.me\/p4mtfP-3bX","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2}},"categories":[4,8,2,3,6,5],"tags":[],"class_list":["post-12273","post","type-post","status-publish","format-standard","hentry","category-data-science","category-finance","category-healthcare","category-physician-executives","category-process-analytics","category-workflow"],"jetpack_publicize_connections":[],"aioseo_notices":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p4mtfP-3bX","jetpack_sharing_enabled":true,"jetpack_likes_enabled":true,"_links":{"self":[{"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/posts\/12273","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/comments?post=12273"}],"version-history":[{"count":13,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/posts\/12273\/revisions"}],"predecessor-version":[{"id":12299,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/posts\/12273\/revisions\/12299"}],"wp:attachment":[{"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/media?parent=12273"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/categories?post=12273"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/n2value.com\/blog\/wp-json\/wp\/v2\/tags?post=12273"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}