Financial and Economic Models used in the Equity and Currency Markets

We often read about how electronic and software based trading is the norm in stock or equity markets as well as currency and other financial markets. Indeed, anyone who is remotely connected with the stock market or for that matter any financial market would know that there are trading systems and trading software that help investors and traders to make decisions regarding which stock or currency or financial product to buy and sell.

Further, even those who are not actively involved in the markets would know that electronic trading has become the mainstay of modern day financial markets.

So, what are these systems and what is the underlying software that they run on? To start with, most trading software and systems are based on financial and economic models that are in turn, grounded in theoretical concepts. For instance, economics students would know that there is a direct correlation between growth and returns.

Similarly, finance professionals would know that there is a correlation between the amount of risk that one is prepared to take and the returns from such risks. This means that any trading system at its core would have to incorporate these and other concepts for the investors and traders to make money.

Without the trading software being based on economic and financial concepts, it becomes difficult to generate returns and sustain trading over the longer term.

Thus, what we have are economic and financial concepts that underpin the trading systems. Further, the models on which the stock and financial trading systems rely on are also underpinned by probability theory, law of averages, and other statistical concepts such as mean variance and median values in addition to ratio and financial statement analysis.

Moreover, the trading models also have to account for complexity and diversity as well as global movements and real time news and updates that can affect the values of the underlying financial assets and stocks as well as currencies. Indeed, this has given rise to sophisticated software that is far beyond any modeling that humans can do giving rise to the term “algorithmic trading” and the speed at which the software calculates has given rise to the term “high frequency trading”.

Having said that, it is not the case that the software and the trading systems are always right or are always better than humans in estimating and accounting for risk. For one, the trading systems tend to be so fast and so deep that any element of uncertainty and risk would result in a behavior that can be classified as an “electronic herd mentality”.

Of course, even before these trading systems were used, human traders were also susceptible to herd mentality and would cause market crashes based on “the smell of fear” when sudden market moving and market shaking events happened.

However, what is different with the electronic trading systems is that they are “lightening fast” and so “complexly deep” that any volatility would not take more than seconds to reflect in the trading behavior.

Thus, what we have are financial markets that are dictated by sophisticated models and powered by advanced technology that can simply make or break the fortunes of traders and investors in short time spans.

Indeed, evidence of this was seen in the aftermath of the bankruptcy of Lehmann Brothers in 2008 that triggered the worst ever losses in stock markets as the financial and economic models at the core of the trading systems could not anticipate how closely integrated and how deeply intertwined the global financial system was and this led to panic and mayhem in global markets.

The point to note here is that despite technological advances and well thought and well designed financial and economic models, it is sometimes better to rely on human decision making and human directed trading as we are not yet at a stage where machines can make the decisions for us all the time.

Indeed, though AI or Artificial Intelligence has advanced, in times of crises such as the one described above, it would be better for humans to take over instead of letting machines dictate and lead to chaos and confusion.

Further, any economic theory or financial model can only do that much in times when political risks are apparent. In addition, it has been found that geopolitical risks are similarly not “priced in” for most of the trading systems and hence, as mentioned above, we must have a balance somewhere wherein there is a “red line” beyond which machines must not be allowed to cross.

Indeed, this is very relevant in the present context as the financial world looks set to be witness to a repeat of the 2008 crisis as most markets have been crashing for the past few months and hence, it is very important not to make the same mistakes that led to market panic at that time.

Therefore, it is the case that in times of volatility, to reduce panic, either the trading systems must be made “intelligent enough” to take proper decisions or let people and humans who the traders and the investors take over the decision are making process.

Moreover, it is also important to note that political and geopolitical risks are factored in as much as possible into the trading decisions rather than relying on what the favorite term of the economists which is “rationality” based theory alone would predict.

To conclude, just as economists are criticized for turning everything into “dry” concepts, the models that dictate trading must factor in “emotions” into the equation as well.


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