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Cif Vs. Fob: What’s The Difference?
By Martin Hilbert Martin Hilbert Scilit Preprints.org Google Scholar 1, * and David Darmon David Darmon Scilit Preprints.org Google Scholar 2
Received: February 1, 2020 / Revised: April 3, 2020 / Accepted: April 17, 2020 / Published: April 26, 2020
The machine learning paradigm promises to reduce uncertainty for traders by making better predictions through increasingly complex algorithms. We ask for observable outcomes of both uncertainty and complexity at the aggregate market level. We analyzed nearly a billion trades of eight currency pairs (2007-2017) and show that increased algorithmic trading is associated with more complex subsets and more predictable structures in bid-ask spreads. However, algorithmic involvement is also associated with more future uncertainty, which at first glance seems contradictory. At the micro level, traders use algorithms to reduce their local uncertainty by creating more complex algorithmic patterns. This brings a more predictable structure and more complexity. At the macro level, increased overall complexity implies more combinatorial possibilities, and thus more uncertainty about the future. The chain rule of entropy shows that uncertainty is reduced when trading at the level of the fourth digit behind the dollar, while new uncertainty began to emerge at the fifth digit behind the dollar (also called ‘pip trading’). In short, our information-theoretic analysis helps us clarify that the apparent contradiction between reduced uncertainty at the micro level and increased uncertainty at the macro level is a result of the inherent relationship between complexity and uncertainty.
The information revolution has revolutionized not only business, economics, politics and socio-cultural behavior, but also the modus operandi of financial markets. Looking at the hustle and bustle of trading floors just a decade ago and comparing it to the smooth hum of today’s computational trading floors suggests that algorithmic trading has had a major effect on the way financial assets change ownership. In this study, we look for observable characteristics that indicate changes in the nature of trade dynamics over the past decade. We find that overall emerging trade dynamics have become simultaneously more predictable, complex and uncertain. We choose the foreign exchange market for our analysis because it has experienced a clear and definable growth in algorithmic trading.
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In general, traders use algorithmic automation to make their local dynamics more reliable and predictable. A large literature also shows that it makes trading faster, but we are not concerned with that in this study. Precision of prediction is the name of the game of the currently dominant machine learning paradigm . Most digitally automated information processes, including bots, trading algorithms and various forms of artificial intelligence (AI), follow a set of deterministic local rules that respond to programmed instructions or learned patterns. For example, a currency algorithm can buy or sell in response to a set of inputs, which can be predefined (so-called expert systems) or self-learned (so-called machine learning). The algorithm will reliably and predictably buy or sell according to the current version of the step-by-step recipe. Algorithms are defined as “an ordered set of unambiguous, executable steps that define a termination process” . Therefore, by definition, algorithms predictably execute a recipe (given or self-learned) to arrive at an inevitable conclusion that defines their behavior deterministically.
In our analysis, we find evidence that the algorithmic machinery employed in the currency markets involves an additional level of predictable structure for the evolving dynamics of bid-ask spreads. Complicated new subsequences increase the predictable complexity in the dynamics. We show that algorithmic trading is one of the most important explanatory variables for this trend. We also show that algorithmic trading correlates with greater predictability, as it reduces future uncertainty about the next bid-ask spread. However, this only applies if we look at the dynamics from the level of detail at which trading took place ten years ago. By putting on the coarse-grained lenses that traders viewed reality a decade ago, algorithms squeezed all uncertainty out of the market. No more profit is made on the fourth digit behind the dollar. Algorithms replaced surprise with a predictable structure (more complex, but more predictable).
At the same time, there has also been a greater sophistication of trade dynamics over the past decade. Algorithms were used to exploit a more detailed level of reality, where humans cannot reach. At this new level of stock trading, we find an unprecedented amount of predictable complexity and unpredictability at the same time. From the perspective of the fifth digit behind the dollar (where profits are made today), uncertainty is greater than ever before. The algorithmization of currency trading is related to the reduction of uncertainty that was the norm ten years ago, but also to a new and more detailed view of reality, which is much more uncertain.
Our findings suggest that traders are introducing automated algorithms to make their daily routines more predictable and that they would have been successful in taming the markets if the world had remained at the coarse-grained black-and-white level it used to be. In doing so, however, they opened up a new level of unprecedented gray and ultimately made the entire market system less predictable than ever before.
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The paper continues as follows. Based on existing literature, we formulate two complementary hypotheses regarding the changing trade dynamics in the foreign exchange markets. We then propose to quantify market dynamics using the metrics of dynamic systems theory and present three long-standing and complementary metrics. We obtain almost a billion tick-level transactions from eight currency pairs for the eleven years between 2007 and 2017 and calculate the proposed measures for 528 bimonthly periods. We then use simple multiple linear regression analyzes to test our hypotheses about the increasing role of algorithms and our characteristics of the changing trading dynamics. Finally, we interpret our findings using existing propositions and literature from information-theoretic approaches to dynamical systems. Our results demonstrate a methodology to assess dynamic changes caused by algorithms and contribute to ongoing discussions about the effect of increasingly sophisticated algorithmic trading on market dynamics.
Trading markets have been called “the largest and most powerful techno-social system in the world” . The main driver of technological change over the past decade has been the introduction of algorithmic trading (AT). AT can be broadly defined as a set of automated trading strategies that track certain variables in their decision-making process, such as time, price and volume, and other historical and simulated patterns.
Farmer and Skouras  distinguish between three broad groups of trading algorithms. The first are execution algorithms (often called “algos” in the literature). They consist of instructions that allow people to set the parameters for executing trades, such as a certain time frame, volume patterns, risk-adjusted real-time market conditions, relative prices between selected stocks, etc. Their main purpose is not necessarily to make an additional trading profit, but to minimize costs and risks and to ensure reliability in the execution of an order according to a fixed strategy. As simple as “execution algos” are, they automate many different aspects of trading. Ten years ago, an investor who wanted to buy a significant amount of stock had to hire a floor broker to quietly execute the order, using human judgment to buy bits and pieces of the overall trade to keep the stock price from going up . Execution algorithms can buy or sell on a time scale of days or even months. Many of them use machine learning to understand market patterns.
The second group refers to the
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