Lectures on artificial intelligence for financial applications related to trading.
These videos demonstrate how to graph equity, bond, and energy markets using volatility and yield curve structures.
The United States spends multiple times more on defense and overseas operation than any other country. Even though these defense expenditures are important, and I do think well spent, it is important to remember that there are tradeoffs with such spending decisions. There are positive externalities with these defense expenditures, especially in aeronautics technologies. But these expenditures also have the negative effect of not having funds available for internal infrastructure projects and increased educational spending, albeit that the educational spending should be for the students and not for the pensions of the teachers’ unions. The main point relative to government spending is that there are finite resources and that such unwise decisions usually result in a less productive and growing economic condition. Government involvement overseas has produced a certain level of crowding out of domestic investment.
Geithner shows the main events in the financial crisis of 2008 in is book. What is import to notice is the steady decline in the S&P 500 before the Lehman crisis. This particular period before the crisis shows the financial stress building up from the BNP Paribas redemption issues to Fannie and Freddie conservatorships. Then the shock it with the fall of Lehman and the steady mean reversion from the signing of the Recovery Act. From a chaos theorist’s perspective the pre-Lehman signs in figure 29 were fractures that did not cause system failure by themselves but began to propagate to a threshold level that lead to a total system failure. It is my option that if we did not initiate quantitative easing that the recovery would have been for longer in the United States and far more painful in terms of unemployment and availability of credit. But it is not well know how developed economies will fare in a zero bound rate policy, e.g. Japan’s long standing stagnate economic situation as of this writing. When policy makers start to raise taxes to fill budgetary holes or when central banks start to raise interest rates there are strong headwinds fighting against such moves, which leads to lower economic growth. For example, Abenomics had to reverse course on tax policy because of recessionary concerns as of November 2014. Something similar might happen in Europe and the United States when central banks start to raise interest rates, forcing the policy makers to reverse course.
In terms of household debt, it was extremely high in 2008. Remember the middle class has been losing their percentage of national income since 1980’s. To maintain their life style middle class households started working multiple jobs, spouses entered the work force, used credit cards, and took home equity loans out. This middle class debt dynamic led to extreme household debt.
The Reagan and Thatcher doctrine of liberalization of capital markets and fiscal restructuring did help produce the great bull market of the 20th century, but at a cost. It was through this doctrine that deregulation of financial markets started.
The next big wave of deregulation of the financial industry and the adoption of the North American Free Trade Agreement (NAFTA) was in the 1990’s. This new wave of deregulation was spearheaded by the Clinton administration and the Federal Reserve Chairman.
What was so unique during the 1980’s and 1990’s that would lead to the financial crisis of 2008? Relaxed financial regulation usually causes over speculation. Financial regulation can take many forms. For example, capital flows in and out of a country can be regulated. Another type of financial regulation is the leverage ratios of banking institutions and trading margins. Another important dynamic is free trade agreements with other nations, which disrupts labor markets with the countries participating in the free trade agreement. Within these free trade agreements are free flow of capital and goods between nations. But not only did deregulation of financial markets help cause the financial crisis of 2008, but also the lack of regulation on new financial engineered products.
The derivatives market exploded exponentially in the 1990’s and 2000’s. Simple derivative markets are energy and bond markets. But more complex derivative markets were mortgage backs securities (MBS) and the close cousin—the credit default obligation (CDO). As MBS’s became more popular to reduce risk in the mortgage markets, CDO’s began to be used for reducing the default risk of the MBS owner. This complex web of derivative were not well regulated and led to over speculation in these markets many times larger than the actual annual size of the economy. This buildup of speculation in the derivatives markets eventually collapsed like all other over speculative markets in the past. The financial regulators were not equipped to survey the economic landscape and visualize the actual systemic risk building up in the financial sector.
I would like to present a few comments of the over confidence in the great moderation in the United States. The great moderation was during the tenure of Federal Reserve Chairman Greenspan. With low inflation and good economic growth the USA experienced a certain hubris that the financial engineering of Wall Street and the Federal Reserve’s fine tuning approach to interest rates could maintain a stable economy. Within this mindset, whenever the economy seemed to slow down the FED would just lower interest rates to increase aggregate demand—leading to further inflation of the speculative bubble in financial assets. Part of the orthodoxy of the Federal Reserve was that markets are efficient and that financial institutions would be rational agents. Of course markets are somewhat efficient but are clearly not rational. The failure of leading economists within the Federal Reserve and in the US government did not apply the lessons learned within the behavioral finance and economic fields of research, i.e. market participates’ fear and euphoria can outweigh any logic. In short, with the combination of deregulation of financial markets, lack of new oversight in complex engineered financial products, and the over reliance of Federal Reserve market intervention the system was primed for a major nonlinear event we all now call the crash of Lehman.
Since housing prices were not in balance with true affordability, the creation of exotic mortgages were used to keep the loan machine going. What was the incentive for the creation of these exotic mortgages? Banks no longer kept the actual loan on their balance sheet. What was going on was the securitization of mortgages and passing the actual loan risk to the buyers of a MBS. The banks received loan processing fees and MBS packaging. Therefore there was, and still is, an incentive to just process as many loans as possible without fully appreciating the systemic risk that their securitization actions were building up. This is a classic example of a feedback loop pushing the financial system into speculative equilibrium only to lead to a serious financial correction.
When too many of these types of loans from banks are going into construction of commercial or residential properties signs of an unhealthy economy emerges. This unhealthy economy is caused by over building properties and not concentrating on long term endeavors, such as loaning to actual businesses to increase production or investments in research and development. The big problem the banking industry has is that they want to loan out with as little risk as possible; therefore they usually do not have incentives to loan out to actual job creating businesses, but to property construction. Construction of property is a job creating endeavor, but only short term and I am not sure we need more major building constructions and strip malls in America. What we do need is better infrastructure and more research and development to increase our economic productivity in the long term. As can be seen in the S&P Case–Schiller composite index record housing prices coincided with record household debt. When combined, the ability to absorb a financial shock at the household level is diminished and that the risk models from the Wall Street banks and MBS holders did not fully capture the systemic risk building up in system. The key takeaway is that just because a financial engineered product can pass risk to another party there are still negative feedback loops that affect all financial participants.
What were some of the causes of the 2008 crisis? We will start a more in-depth analysis, primarily from Geithner (2014) as source material. I would suggest there were five main causes of the crisis, but there are other important causes not covered in this writing. The following five major causes are: (a) record income inequality, (b) record household debt, (c) relaxed financial regulation, (d) decline of household discretionary income, and (e) an over extension of overseas operations. With these five major causes, a confluence of factors became magnified causing the economic system to collapse. These dynamics can be explained through chaos theory.
Any one of the five major causes most likely would not bring down the whole financial system, but when added to together there are special nonlinear dynamics exhibited. As more aspects of a system are defined there are more degrees-of-freedom. With a system of high degrees-of-freedom, unexpected system dynamics result due to strange chaotic attractors. This is true even with a totally deterministic system, whereby no random functions are present in the system. In financial markets there are stochastic variations making this type of system a nondeterministic system—leading to even more strange chaotic attractions. When a certain threshold is reached a new system dynamic emerges. Let’s look at some details of the five major causes of the crisis.
In terms of record income inequality, Piketty(2014) showed that income inequality is cyclical. This cycle of income inequality in the United States can be shown over the last hundred years or so. One can conclude that very similar income inequalities exist as of this writing compared to the late 1920’s. The first big drop of income inequality was after the 1929 stock market crash. But the biggest drop in income inequality was during World War II. After the war it took approximately 4 decades for the income inequality in the United States to rise. A big part of this rise was deregulation of markets coupled with low taxation policies initiated in the Reagan and Thatcher era. More of the executive compensation, especially starting in the 1990’s, is through stock options—leading to exponential grown in income for the managerial class of workers. We see this in the average income of the executive staff of a publicly held company earning much more that the average worker—the multiple might be as high as 400 times more within some companies.
Higher income inequality might be great for a selected few in society, but it seems that this social dynamic is not healthy for the economic ecosystem. Case-in-point, from the 1940’s through the 1970’s income inequality remained stable at about 33 percent of national income for the top 10 percent of the wealthiest in the United States. During this period the middle class was growing and aggregate demand was strong. But as income inequality increased to nose bleed levels the middle class had lost approximately 15 percent of national income—primarily due to the lack of capital to participate in asset price appreciation. It is hypothesized that increased income inequality helps feed stock market bubbles, and therefore deeper stock market corrections. In chaos theory we describe this dynamic as a chaotic attraction; when one part of the system is out of equilibrium with another part of system. In this case too few people are controlling too much of the national income and leading to unhealthy economic ramifications.
Podcast on Hypercubes by Paul Cottrell.
Video lecture on Paul Cottrell's dissertation.
Paul Cottrell's lecture on his dissertation research.
The financial crisis of 2008 was the worst financial crisis since the Great Depression of the 1930’s. Many books have been written on why the 2008 crisis happened. Irwin (2013) stated that central bankers knew that the housing bubble was a serious problem, but the central banks’ policy makers failed to have the imagination to understand the confluence of events that could magnify and bring down the whole global economy. In Geithner (2014), lack of firepower to bailout institutions in the banking sector was lacking in the early stages for the crisis. In terms of risk models, in Deman(2011), quant models are based on assumptions and those assumptions can become invalid over time—leading to inappropriate pricing of assets and risk assessments. Others have suggested that over indebtedness in the public and private sector was the cause of the decline of developed economies (Ferguson, 2013). The point of this book is not to comprehensively explain the crisis of 2008, but to suggest that economic systems evolve and emergent properties result, albeit some good and some bad properties. It is fair to say that the lack of imagination and the true risk of contagion are good starting points. We need better tools for assessing risk in the financial system, whereby we can evaluate endogenous and exogenous risk.
The financial crisis of 2008 resulted in the threat of total global financial collapse, whereby even the assumptions of free market capitalism and democracy were seriously questioned. This was a dangerous time and still is as of this writing. For example, European countries are still floundering economically, especially in the peripheral nations of the European Union. European countries tied to the Euro currency no longer have the ability to adjust their own monetary policy, and in that respect have lost economic sovereignty and self–determination. The problems are not just in Europe. There seems to be a global trend of a zero interest rate policy in developed nations, which is new to central bankers’ experiences. Is it possible that monetary policy that created the great moderation has led to new monetary dynamics that the central banks will have a hard time modulating. For example, when the United States of America starts to eliminate quantitative easing and the economy starts to wobble, will the Federal Reserve reverse their tightening policy? It is not at all clear that the central banks are in control of zero interest rate policy epochs. For example, in an inflationary situation the central banks can just raise rates, which they have a lot of room to do so—but might led to further erosion of aggregate demand. In extreme increases in aggregate demand the central banks can modulate the economy. This is not the case in a deflationary condition where the rates are already zero bound. What can the central banks do if the aggregate demand continues to erode with zero bound rates? The only thing that the central bank can do is severe asset purchases of all kinds—even ketchup.
The financial crisis also involved massive financial bailouts of banks, larger corporations, and citizens. For example—even though Lehman was not bailed out—General Motor, AIG, Chrysler Motors, and many banking firms were. Even with the bailouts, the money markets were not functions properly and a severe recession ensued. In the United States quantitative easing was initiated by purchasing many different asset backed securities but other countries were forced into austerity policies, especially in the European region—creating more financial harm than good. This austerity was promoted by Germany and the IMF when countries, such as Greece, were in trouble of defaulting on their sovereign bonds. At the time of this writing the ECB is starting to adopt a different stance and invoking a lender-of-last-resort policy regime and asset purchase buyer.
What can surfaces provide us? Surfaces provide an easier way to interpret data, especially big data. Surfaces can be used in conjunction with 2D visualizations for additional confirmation. For example, surfaces of the market can be used with normal 2D price curves to get a better understanding of the market’s dynamics. Therefore surfaces can fill in gaps of understanding that lower dimensions do not seem to reveal.
As for the theoretical framework, chaos theory is used because of the emerging properties that the market seems to exhibit. Complexity science—which is a branch from chaos theory—highly suggests emergent behavior of nonlinear systems, such as the financial markets. We can envision three different state–spaces composing of buyers and sellers. In first state–space the population is heterogeneous but equally distributed, whereby influence by neighboring traders are considered minimum. In second state–space the populations of buyers and sellers are still equal, but the traders of similar types are coalescing into segregated masses. Within this second state–space you can see that a phase transition is about to take place but it is not clear if buyers or the sellers will triumph. In technical trading this can be seen clearly in a Bollinger band pinch, whereby a big move either up or down can result. Lastly, in the third state–space we see that a certain population type has overcome the other population and exhibits a severe herd effect. It is important to mention that this herd effect is also present in second state–space example but only local to its community.
What is topographic finance? Topographic finance is the study of financial and economic systems in multiple dimensions, whereby three–dimensional and four–dimensional graphic technologies are used. This new financial analysis methodology provides insight that lower dimensional visualization cannot capture. Topographic finance can be used for trading and financial analysis. In figure 16 a lower dimensional visual representation is presented.
A typical financial chart has price and time. In addition to price and time, the volume of trades at a particular time frame can be represented. As you can see there is very little understanding in this example about volatility. Many traders use multiple indicators on their price chart, but the ease of reading these charts can be difficult at times, especially in fast moving conditions. There must be a better way to represent market movements in a higher dimensional way. The solution to this representational problem is the use of topographic finance.
Many option traders use volatility surfaces to understand the non–linear dynamics to the option they are trading. An option’s volatility surface contains volatility, time to expiration, and strike price. A volatility surface shows time decay of an option. There must be a way to utilize the concept of the option’s volatility surface for non–option financial products. Of course there is a solution to this problem via the utilization of topographic finance.
One can use a three–dimensional graph to see the topology of the three variables of interest. Usually this is done using the following: time, log return, and volatility. But other three–dimensional configurations can be constructed, e.g. time, correlation to another asset, and the 200-day moving average of price. It is quite easy to envision the vast amounts of different variable configurations that an economist or financial analyst can make to get a better understanding of the complexity of the non–linear dynamics of the financial markets.