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Why are the stock markets so much more complicated than theory?

Having discussed financial markets briefly in class, it becomes very apparent that the underlying network exchanges that constitute stock markets are plain and simple. In theory, the network exchange of stocks allows equity to flow from a seller, through an intermediary (a trader) to a buyer. With a multitude of buyers purchasing and sellers offering, the market generally becomes quite liquid, meaning that the opportunities for exchange are abundant. Additionally, as the liquidity of the market increases, the arbitrage opportunities often created by market inefficiencies dissipate and the market approaches equilibrium. In class, stock market equilibrium translated to an equal bid and ask price and thus, no profit for the traders of the securities. However, in the real world, we know that the stock market is vastly different than a set of networks in equilibrium – most clearly demonstrated by the massive revenues that many traders generate today and the volatility in stock prices of SP500 companies.

After listening to our lecture on markets, my immediate question was, “why are the stock markets so much more complicated than theory’’? In pursuit of an answer, I stumbled upon numerous scholarly journals, new articles and nonfiction books – some speaking of complexity theory and efficient market theory and some delving into the intricacies of the numerous financial derivatives that are traded in today’s market. What I realized is that many different people have many different explanations for the same phenomenon – in fact, the reality appears to be that no one truly understands the system.

Though my conclusion may sound defeatist, I think that it offers a more realistic view of today’s markets than many out there. In my pursuit of the answer, I did stumble upon one very interesting discussion. In a speech delivered at a TED speaker series event, Kevin Slavin shows how algorithms are beginning to control the market and offers some insight into some of the complexities of algo-trading (http://www.ted.com/talks/lang/eng/kevin_slavin_how_algorithms_shape_our_world.html). As he notes, nearly 70% of equities are traded via algorithms at speeds faster than the human brain can process. He sheds light on several incidences where the algorithms produced massively imprecise results and yet, we continue to allow algo-trading to dominate the trading volume. In the context of our class discussion, algo-trading gone awry represents a rogue buyer or seller. Imagine if a buyer or seller in the simple network that we depicted instantaneously chose to buy or sell a massive amount of a given security or continued to incessantly purchase many, small amounts of a security despite market indicators. The rapid deviation from equilibrium that would result could be a factor in the kind of volatility that we see today. Perhaps more importantly to my point, the presence of algo-trading increases the complexity of a virtually incomprehensible system.

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