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In January 2009, I moved from Los Angeles to New York City to begin a new career in finance. My former business partner made a similar move in 2006 and in three short years was making more than $300k per year as a financial planner for JP Morgan Chase. She gave me plenty of advice and I was happy to take it. I slept on her couch for my first two weeks in the city.

In February, amid job interviews, French finance firm AXA Advisors found my resume on a job posting website and offered to sponsor me in the three big state licensing exams necessary to sell investment products; the Life, Accident, & Health Insurance exam, the Series 7, and the Series 66. I accepted and began studying.

March saw the Dow Jones Industrial Average close below 6,700, its lowest point in more than a decade, the same month I passed my first exam.

At the end of April, while studying for the Series 7, my friend at JP Morgan Chase abruptly announced she was moving from New York City back to Southern California to buy a house.

In the weeks that followed, I began to question my new career path, no longer having my finance insider friend in the city. A short while later, the New York Post reported that my friend and former business partner was arrested by the NYPD’s identity theft task force. She stood accused of duplicating a client’s ATM card and over the course of a year and a half, gently spending $110k.

I remained in New York for most of the rest of 2009. During that time, I spent my free time chasing women, taking photographs, and making the following discoveries in the field of astrobiology.

I. History

Throughout the history of financial analysis, economists have long sought the elusive primer that would unlock a better understanding of the repetitive cycles of boom and bust that have both spurred and hobbled world financial markets.

Beginning in the 19th century, British economist William Stanley Jevons, attributed as the first mathematical economist, noted that market crashes occurred on average every 10.45 years, in the years 1701, 1711, 1720, 1732, 1743, 1753, 1763, 1772, 1782, 1793, 1805, 1815, 1825, 1836, 1847, 1857, and 1866, respectively.

Jevons also noted a correlation between collected observational data of sunspots by astronomers who began counting sunspots telescopically in 1610.

Jevons believed two facts to be certain: first, that market crashes were tied to fixed cycles. Second, that these cycles were either correlated to or caused by solar activity, specifically sun spot cycles, rather than news events or corruption (for example, consider recent headlines blaming Countrywide, Lehman Brothers, Bear Stearns, etc for ushering in our current economic downtown). Jevons published a minor paper on the subject in 1878, “Commercial Crises and Sun Spots” in Nature.

Jevons would, centuries later, prove to be close in both respects, but when another crash failed to occur at the end of his forecasted 10.45-year interval, his conclusions were criticized by colleagues, and his reputation as an economist never fully recovered during his lifetime.

Paris physician and statistician Clément Juglar was another early contributor to business cycle theory. Juglar again noticed the apparently cyclical nature of economic expansion and collapse and in turn made flexible assertions. Juglar’s data suggested that the period of prosperity within an economic cycle lasted between six and seven years, followed by a crisis, with the subsequent crash and liquidation taking between one and two years to “run its course.” Juglar consistently compared the rise and fall of markets using medical metaphors, robust markets being “balanced and healthy,” “sickly” markets having a period of “necessary fever” to purge their “diseased” elements.

Juglar’s work, first published in 1856, is exhaustive and detailed, highlighting specific proximal determinations with each particular collapse while also noting an overarching connection between events. Preeminent Juglar scholar and market economist Daniele Besomi, writes, “if crises are not to be taken as a disconnected individual occurrences, but have some features in common and tend to recur cyclically, there must be a common explanation of the phenomenon, in spite of the different circumstances affecting different economic systems at different times.”

The Juglar Cycle, relatively flexible, remains a term in use in modern technical stock trading, among a great many other tools for statistical analysis. Both Jevons and Juglar found relative notoriety for their ability to look past the news of the moment and seek a factual, predictive primer, something beyond the whims and folly of investors and speculators. Their phraseology includes terms like “the psychology of the public,” “prices that fall like an avalanche,” and “the evil consequences of some disturbance, whether internal or external,” hinting at a bigger picture that was somehow at-remove, or as yet undocumented.

Since then, statisticians and economic scholars have tried their hand at better understanding business cycle theory. Most, if not all, have failed. No modern business cycle theorist has achieved the level of financial success, predictive accuracy, or international infamy, as American financial analyst Martin A. Armstrong. After analyzing the history of financial panics from 1683 through 1907, Armstrong noted that on average, a market crash occurred every 8.6 years. In 8.6 years there are 3,141 days, and for those of you familiar with pi, there does indeed appear to be some superficial significance, or at the very least, a coincidence, to his discovery. Again, like Jevons, Armstrong sought to pin down a fixed number to his particular cycle theory.

Armstrong then began designing a computer-generated predictive model based upon exchange rates and historical economic data. He called it the Economic Confidence Model. He built a company around his forecasting model. Armstrong’s Economic Confidence Model was able to predict, to the day, the October 29th, 1987 market crash, but by this time, he was already a legend, billing clients as much as $10,000 per hour for his financial consultation.

In the late 1970s, Armstrong rose in stature among Wall Street insiders garnering audiences with deep pockets and cozy political connections, among them Margaret Thatcher. With great success grew great paranoia and among those whom Armstrong feared was the CIA, who allegedly followed him and had his phones tapped. In 1999, Armstrong was raided by the FBI, charged with defrauding Japanese investors in an elaborate Ponzi scheme, and has since spent several years in jail, currently incarcerated in Fort Dix, New Jersey.

However accurate Armstrong’s predictive models may have been, his company is now broken up and his methods remain secret as he awaits release from prison following a contempt conviction. In a 2009 piece published in the New Yorker, writer Nick Paumgarten demonstrated that whatever clever insight with which this talented theorist may have been gifted has given way to an all-consuming paranoia, as Armstrong has claimed in an interview to be the target of assassination attempts while in prison and that his predictive cycles are somehow tied to “dark matter,” with no evidence given in his defense.

In the following paragraphs, I will assert that although these perceptive and intelligent individuals were close to gleaning a better understanding into business cycle theory, they fell short due to a lack of access to important data from sophisticated scientific instrumentation. Only in the last eight years, since the European-financed Solar Heliospheric Observatory (SOHO) and the Solar Dynamics Observatory (SDO) have begun collecting and transmitting data back to Earth, has science begun understanding that our sun is a variable star.

Clouds of magnetized plasma called coronal mass ejections build up and are expelled at wildly unpredictable intervals. Solar irradiance, as we will soon examine, is extremely variable within the solar cycle. The solar cycle itself, thought by astronomers for centuries to be fixed at 11 years, is now understood to vary between 9 and 12 years, with no as yet proven model for predictability. Science, in short, has only in the last decade understood how to effectively observe our parent star, the Sun.

But what, if any, connection might there be between solar variability and human behavior? Basing the unfounded claims of historical figures like Jevons, Juglar, and Armstrong do little to instill confidence in research scientists and other academic contributors. Likewise, Wall Street analysts are rightfully wary to accept solar observational data as a method of investment strategy. If a connection between the two does exist, how might we bridge the divide?

II. OBSERVATIONS, DISCOVERY, & DATA

The first two pieces of evidence I offer to support my claim, that solar variance influences human behavior, are two data sets, collected over similar time periods.

Figure 1. The Dow Jones Industrial Average graphed between January 1997 and June 2010.

Figure 2. Solar Irradiance data collected by SOHO between 1996 and 2009. ^[5]

Figure 3. An overlay of the two data sets.

These two charts were the seed that began my research into solar dynamics and human behavior. Though the two data sets are gathered from extraordinarily different sources, there seems an isomorphic correlation, if not entirely causal connection, between each graph, with peaks and troughs in each set pairing up nicely with each other, at multiple points of correction across the more than twelve year period.

As you should also have noticed, there exists a 1+ year lag from the solar data to the Dow Jones data. Most notably the extreme drop that occurred in early March 2009 in the Dow Jones actually occurred in the solar irradiance data set in early January 2008. In short, if there is a causal connection, solar irradiance data may predict stock market performance more than one year in advance.

This one-year difference between variations in the velocity amplitude of solar irradiance and its manifestation (the Dow Jones) appears to express itself in a similar fashion as surface air temperature’s peak in the later afternoon during the course of a 24-hour day, rather than at noon when the sun shines most directly over the Earth’s horizon. Similar too, the hottest month of the year is not June, when the sun angle is most direct in the Northern Hemisphere, but in August. SOHO collects data from geosynchronous orbit, roughly 22,300 miles above the Earth, meaning that if our sun is interacting with the Earth, there may be additional astrobiological processes ongoing as solar irradiance enters Earth’s atmosphere, and is absorbed and reflected by its surface.

The temptation is to label this apparent predictive correlation as a causal factor, i.e. solar irradiance variability causes changes in human behavior, as evidenced in the stock market. This, however, might yet be too bold a claim. The direct causal connection seems to be not in human behavior, but in the effect on human mood.

Consider as additional supporting evidence this brief summary of an October 2010 arXiv-published paper from Indiana University. Using a dataset mined from Google Analytics, a three-person research team reported a connection between one of the six mood categories that Google classifies among the language of tweets on the social networking website Twitter. Specifically, more than 9.8 million tweets were collected from 2.7 million users between February and December of 2008. The team looked strictly at tweets that indicated mood, i.e. “I feel,” “I don’t feel,” “I am feeling,” “I don’t feel,” “I’m,” “I am,” and “makes me.”

Once these “mood tweets” were filtered for spam, and broken down into six distinct categories, a unique correlation began to emerge.

“We find an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the Dow Jones Industrial Average.”

The Indiana University team used bivariate Granger causality, a statistical formula that tests the relationship between two variables, to determine that the tweets categorized as “calm,” correlated most closely with the Dow Jones, predicting market performance 2 to 6 days in advance.

In fact, it was the publication of this paper that lead me to put off work on the book I am writing about my own discovery, made in 2009, in order to offer this more concise piece for consideration (this paper represents a condensed version of the first two chapters of my book). Still remaining to be answered, are questions concerning the biological, or perhaps more specifically neurological, connection between solar irradiance and human mood. Which precise neurons or groups of neurons within the human brain are affected by solar irradiance?

The work of neuroscience has for decades claimed that free will is largely an illusion, that decisions are made from deep within the neural net, before our consciousness of them arises. But where and how do we develop empirical experiments to properly observe this purported connection? Modern neuroscientists rely on patients with pharmacologically intractable epilepsy to carry out experiments on the human brain, yet the vast majority of human experimentation remains forbidden on moral grounds.

There may yet exist, as did at the time of Jevons, Juglar, and Armstrong, limitations of modern technology to prove or better understand this astrobiological connection. Still, certain aspects of human biology that we do currently understand might help add perspective to this apparent connection.

One oft-cited effect that has been and continues to be vigorously attacked by the mainstream scientific community is any purported connection between human behavior and the moon phase cycle. Below is a transcription from a live discussion at Amazing Meeting 6 by astrophysicist Neil deGrasse Tyson:

“Behavior and full moons. This one is classic. People acting crazy, the moon pulls the tides, the tides are made of water, the human body is mostly water. Therefore the moon must affect the human body.

“Until you actually do the math, and when you do the math, you can ask the question, what is the tidal force of the moon on your cranium? How about that calculation? You could do that calculation.

“The tidal force of the moon on your cranium: if that were severe, if could be messing with you. So, it turns out if you’re one of these people who sleep with a lot of pillows and one of the pillows is leaning on your head overnight, the pressure from that pillow on your head, is a trillion times greater, than the tidal force of the moon across your cranium, but no one talks about the force of down pillows on your behavior.”

Though not exactly an empirical scientific refutation of the “lunar effect,” Tyson’s argument typifies the attitude that modern science has toward any claim of a connection between the moon phase and human behavior.

Rather than presenting a litany of statistics from hospital emergency room admissions or police reports, as some might use to counter this argument, I will offer one simple assertion and two charts to support it.

I assert that the effect of the moon upon human behavior has been mis-categorized as being tidal or gravitational in origin. While there is indeed a gravitational connection between the Earth’s oceans and the moon, I offer evidence that the gradual increase and decrease of reflected solar irradiance during the entire course of the moon phase is the legitimate causal factor behind the “lunar connection.”

Additionally, I contend that human biology, specifically the human ability to reproduce, has evolved to be organized around local, gradual shifts in solar irradiance as it is reflected from our moon. In short, while the sun is quite obviously the sole origin of charged particles within our solar system, our moon acts like a flap on the wing on an airplane. Just as a flap can make a wing climb or descend, so too does the moon phase shift the speed and intensity of solar irradiance over the moon phase cycle, with the full moon reflecting the most solar irradiance and the new moon reflecting the least.

Figure 4. An illustration of the Standard Days method of female fertility.

Figure 5. An illustration of the moonphase cycle.

If oral contraceptive formulations are not being taken (i.e. the Pill), the human female menstrual cycle adheres, with near-congruency, to the moon phase cycle, with the full moon corresponding to the most fertile fraction of the menstrual cycle and the new moon corresponding to the least fertile fraction.

There appears to be a distinct connection to the moon phase in human reproductive anatomy. However, moon-related gravitation has little impact on human biology, as Dr. Tyson earlier elaborated upon. Therefore, there must exist some more significant, less understood determinant behind the moon phase and human biology, i.e. reflected solar irradiance.

This is a fresh perspective on a well-known connection. This novel theory needs further evidence, beyond the human connection, to lend additional support. If solar irradiance is interacting with matter on Earth, even via subtle or difficult to detect means, surely there would have to exist a further basis of proof.

Very recently, one aspect of that necessary basis of proof has emerged. As detailed in the Stanford Report on August 23rd, 2010, research teams from both Stanford and Purdue, working with radioactive isotopes, made remarkable, independent discoveries with respect to scientific understanding of the decay of those isotopes.

Students have long been taught that the decay of radioactive isotopes, like that of carbon-14 whose decay is used to date ancient artifacts, was constant. School books the world over use carbon-14 as a guidepost to reconstruct the prehistory of life and culture on Earth. The assumption was, and still officially is, that carbon-14 decays at a constant rate over time.

The team at Stanford, working with radioactive isotopes to generate random numbers, discovered something unique. Long-term observation of the decay rate of silicon-32 and radium-226 appeared to show seasonal variation. Their respective decay rates were slightly faster in the winter of the northern hemisphere than in the summer. It is worth mentioning that the Earth is 3 million miles closer to the Sun in the winter, than in the summer, due to its slightly oblong orbit around the Sun. The team initially believed its equipment to be faulty, until a larger piece of the newly emerging puzzle came into view.

On December 13, 2006, the sun sent a solar flare blazing toward Earth. At Purdue, nuclear engineer Jere Jenkins was measuring the decay rate of manganese-54, a short-lived isotope used in medical diagnostics. Jenkins observed that the decay rate of his sample dropped slightly during the flare, a decrease that started about a day and a half before the flare actually occurred.

This observation was noted in the middle of the night at Purdue, meaning that whichever particle that originated from the sun had to pass through the surface and core of the Earth, into his lab. Among the few particles known to behave in such a manner are solar neutrinos: extremely light, almost mass-less, particles that were thought to have little interaction with matter. Jenkins, additionally, noted that the variations in decay rates were highly unlikely to have originated from environmental influences on their instruments.

The data in evidence, thus far, points to a unique conclusion, that the sun is connected to and in biological contact with radioactive isotopes on Earth. Neutrinos are not yet known to be the provable cause of these changes in decay rates, but the case could be made that some particle or other natural process, as yet unobserved, may offer evidence of the interaction that has been both central to several of my assertions and as yet elusive. Astrobiology is, as a point of fact, an emerging, poly-disciplinary field of science.

Our parent star, the Sun, appears to determine, with great energy and influence, multiple aspects of human biology, hence the moniker Parent Star Theory. Still to be understood is how unique our Sun is, relative to other stars in the Milky Way Galaxy and how unique our planet is, relative to other planets in our solar system. How might solar irradiance interactions play out at greater distances within our solar system, or in other solar systems, whose stars differ in galactic position, overall size, intensity, age, and composition?

III. Implications

My initial impulse after making that first discovery was to write a book. Once I discussed my discovery with members of the scientific community, the oft-heard response was, “Why don’t you play the stock market?” Indeed, as it was taking a relatively long time to find support for my discovery, I also placed an online advertisement soliciting a technical trader to build a computer model around SOHO’s solar irradiance data that could predict stock market performance.

Alas, the team at Indiana University has demonstrated that there exist far easier means to predict stock market performance, and upon reading their paper, I decided to abandon my own quest for easy financial glory and offer my findings in the name of science and a better understanding of the natural world.

Still, significant implications for the stock market could yet arise from these observations. The data collected and transmitted back to Earth by the VIRGO radiometer on SOHO is calculated and graphed by Dr. David Hathaway at NASA’s Marshall Space Flight Center in Huntsville, Alabama. If a definitive causal connection is established, Hathaway’s solar irradiance data could be used to predict or better forecast the next global financial collapse when solar cycle 24 comes to an end, in the next 9 to 12 years.

Also, a clearer picture of the human brain’s susceptibility might be ascertained, specifically in the field of neuroscience, whose body of work researchers, spurred in large part by the experiments of Dr. Benjamin Libet in the 1980s, have offered as proof that free will is largely an illusion.

An understanding of the astrobiological connection between our parent star, the Sun, and our home planet, the Earth, remains in its delicate infancy. At very least, this discovery warrants the emerging perspective of our Sun and the Earth as elements of a vastly larger, less understood biological system. Biology and nature do not simply give out where Earth’s atmosphere ends. Though some important answers remain unknown, this growing field of science should now be better armed to ask clearer, more direct questions.