Imitation in the Imitation Game

by   Ravi Kashyap, et al.

We discuss the objectives of automation equipped with non-trivial decision making, or creating artificial intelligence, in the financial markets and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best exemplified by a frolicking infant. For this unintentional yet welcome aftereffect to set in a foundational list of guiding principles needs to be present. A consideration of these requirements allows us to propose a test of intelligence for trading programs, on the lines of the Turing Test, long the benchmark for intelligent machines. We discuss the application of this methodology to the dilemma in finance, which is whether, when and how much to Buy, Sell or Hold.



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1 Abstract

We discuss the objectives of automation equipped with non-trivial decision making, or creating artificial intelligence, in the financial markets and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best exemplified by a frolicking infant. For this unintentional yet welcome aftereffect to set in a foundational list of guiding principles needs to be present. A consideration of these requirements allows us to propose a test of intelligence for trading programs, on the lines of the Turing Test, long the benchmark for intelligent machines. We discuss the application of this methodology to the dilemma in finance, which is whether, when and how much to Buy, Sell or Hold.

2 The Circle of Investment

On the surface, it would seem that there is a repetitive nature to portfolio management, making it highly amenable to automation. But we need to remind ourselves that the reiterations happen, under the purview of a special kind of uncertainty, that applies to the social sciences. (Kashyap 2016) goes into greater depth on how the accuracy of predictions and the popularity of generalizations might be inversely related in the social sciences. In the practice of investment management and also to aid other business decisions, more data sources are being created, collected and used along with increasing automation and with attempts to bring greater intelligence in the decision process (Tambe 2014; Bughin 2016; Provost & Fawcett 2013).

Artificial Intelligence (AI) in finance is a topic that has gained increased attention and priority among market participants. This article has been written to be of general interest, since it paints a clear picture and illuminates the basic ideas and techniques, required to make the incubation of intelligence a reality (sufficient technical details are provided but have been relegated to the appendix and other references). The interrelationship of different approaches and the central questions that remain open are also discussed. We consider how automation can be useful for the financial services industry and specifically articulate points regarding the what / how / why of applying AI to investment management.

It is important to bear in mind that due to the game theoretic nature of the financial markets, specifically with regards to investing and trading, improvements in data sources and related technology will only be beneficial, if they are better than competing players (Gupta & George 2016; Barney 1995 discuss why investments alone do not generate competitive advantage and instead firms need to create capabilities that rival firms find hard to match). (Fama 1970) is a discussion of fair games and efficient markets; (Kyle 1985; Foster & Viswanathan 1990) solve for the Nash equilibrium, (Osborne & Rubinstein 1994), when trading is viewed as a game between market makers and traders. (Kashyap 2016) has a proof of trading costs as a zero sum game. For different types of zero sum games and methods of solving them, see: (Von Neumann & Morgenstern 1953; Laraki & Solan 2005; Hamadène 2006). (Bodie & Taggart 1978; Bell & Cover 1980; Turnbull 1987; Hill 2006; Chirinko & Wilson 2008) consider zero sum games in the financial context.

If Alice and Red Queen of the Wonderland fame (Carroll 1865; 1871; End-note 2) were to visit Hedge-Fund-Land (or even Business-Land), the following modification of their popular conversation would aptly describe the situation today, “My dear, here we must process as much data as we can, just to stay in business. And if you wish to make a profit you must process at-least twice as much data.”

We could also apply this to HFT-Land (HFT, High Frequency Trading: Biais & Woolley 2011; Menkveld 2013; Brogaard, Hendershott & Riordan 2014; Cartea, Jaimungal & Ricci 2014; End-note 4) and say: “My dear, here we must trade as fast as we can, just to stay in business. And if you wish to make a profit, you must trade at-least twice as fast as that.”, while reminiscing that the jury is still out on whether HFT is Good, Bad or Just Ugly and Unimportant (Budish, Cramton & Shim 2015 argue that the high-frequency trading arms race is a symptom of flawed market design and that financial exchanges should use frequent batch auctions instead of the currently predominant continuous limit order book; Brogaard, Hendershott & Riordan 2017 highlight that some HFT activity could be harmful for liquidity; in contrast, Li, Cooper & Van Vliet 2017 indicate that high-frequency trading has a liquidity provision effect and improves the execution quality of low-frequency orders; Chaboud, Chiquoine, Hjalmarsson& Vega 2014 suggest that algorithmic trading causes an improvement in the price efficiency of foreign exchange markets. Kirilenko, Kyle, Samadi & Tuzun 2017 study the events of May 6, 2010, that became known as the Flash Crash and show that HFTs did not cause the Flash Crash, but contributed to it by demanding immediacy ahead of other market participants).

A consensus (perhaps the only one) among most participants seems to be that automated trading dominates the markets, though there seems to be a significant amount of debate on the specific problems that require better decision making (for more HFT pros and cons see: Vuorenmaa 2013; Savani 2012

; Hagströmer & Norden 2013); (for the ethical angle see: Davis, Kumiega & Van Vliet 2013; Angel & McCabe 2013; Cooper, Davis & Van Vliet 2016). Efforts at using learning techniques and artificial neural networks, (

Haykin 2004), is not new to finance (Hawley, Johnson & Raina 1990; Kryzanowski, Galler & Wright 1993; Wong & Selvi 1998; Bahrammirzaee 2010; Cartea, Jaimungal & Kinzebulatov 2016); but the limited success should inform us to learn from the mistakes and try to make some fundamental alternations to our approach and perspective.

Any attempt at creating artificial financial agents can benefit immensely from the knowledge that has been accumulated using models in which the participants are not fully rational and their imperfect decisions have helped to explain financial phenomenon, which is the field of behavioral finance (Barberis & Thaler 2003). Kumiega & Van Vliet 2012 consider the behavioral aspects of algorithmic trading; (Wang, Keller & Siegrist 2011) show using surveys on risk perceptions of investment products that respondents perceived easier-to-understand products as less risky, which was likely driven by the familiarity bias (Huberman 2001 provides compelling evidence that people invest in the familiar while often ignoring the principles of portfolio theory; also see: Fox & Levav 2000; Seiler, Seiler, Harrison & Lane 2013). (Brown & Cliff 2005; Shu & Chang 2015; Au, Chan, Wang & Vertinsky 2003; Chung, Hung & Yeh 2012) discuss the influence of investor sentiment on financial markets.

This background of the financial landscape, prompts us to not focus on improved decision making specific to narrow problems but to uncover the general principles that might be necessary for increased intelligence. Once we outline these foundation principles, we provide individual testable hypothesis, though we need to remind ourselves that greater the coherence between the components, better the intended outcome.

To the best of our knowledge, this is the first known instance of a modified Turing Test for trading or investment decision-making (section 4.1). We also discuss an improvement of Searle’s Chinese Room (a key argument in AI) that is applicable for portfolio management(section 4.3). The financial services industry is among the leading investors in AI; hence the answers / clues provided in this paper to the problems of buying and selling assets can be immensely applicable outside finance, to a wide cross-section of the business community.

3 A Profitable Benchmark for Brainpower

The problem of automation, with non-trivial decision making, or designing intelligence artificially for application in the financial markets, can be a rather trivial task, depending on which trader’s brainpower acts as our gold standard. As a first step, we recognize that one possible categorization of different fields can be done by the set of questions a particular field attempts to answer. Since we are only the creators of different disciplines, but not the creators of the world in which these fields need to operate, the answers to the questions posed by any domain can come from anywhere, or, from phenomenon studied under a combination of many other disciplines.

Hence, the answers to the questions posed under the realm of automated trading (AT) or AI, can come from seemingly diverse subjects, such as, physics, biology, mathematics, chemistry, marketing, finance, psychology, economics, music, theater and so on. As we embark on the journey to apply the knowledge from other fields to automated portfolio management, (APM), we need to be aware that APM is “Simply Too Complex”, since all of time, portfolio management has just been about beating a benchmark (and many times, this benchmark is all about taking profits and avoiding losses, easier defined than done, as any portfolio manager or trader would reckon). The complications are mainly to select the right standards to compete with. To facilitate a reference point, for the rest of the article, we define automated portfolio management as below.

Definition 1.

Automated Portfolio Management (APM) or Artificial Intelligence in Portfolio Management (AIPM) is the ability to connect elements of previously attained information to effect a portfolio management decision. Nothing lasts forever and hence, no decision is good forever; but the longer a decision serves its purpose, the greater the intelligence involved in making that decision.

With this definition, it should become clear that AIPM requires the ability to collect pieces of information and to connect them towards a decision making goal. Since decisions are not going to be valid indefinitely, we need to continue to use these abilities to effect later decisions or improve upon decisions already made. We use the term agent below to refer to our creations, which are expected to display intelligence. (Russell & Norvig 2016) is a comprehensive discussion of the concept of an intelligent agent. (Wooldridge & Jennings 1995) discuss the most important theoretical and practical issues associated with the design and construction of intelligent agents. (Hand 1998) makes a case for the tools of statistics in the arms race for collecting and mining data towards the goal of better decision making.

3.1 Automation for What Sake?

To be precise, this is not about automation to barter for the Japanese drink (though, that seems like a wise exchange, in terms of financial value, and might have been attempted many times before). Below, we make a case for why automation is not only unavoidable, but necessary for modern portfolio management. We adapt certain core concepts from (Kashyap 2017), which considers the goal of creating intelligence for the purpose of intelligence alone, and tailor it to the nuances of dealing with uncertainty in the financial markets.

A central aspect of our lives is uncertainty and our struggle to overcome it. Over the years, it seems that we have found ways to understand the uncertainty in the natural world by postulating numerous physical laws. The majority of the predictions in the physical world hold under a fairly robust set of circumstances and cannot be influenced by the person making the observation, and they stay unaffected if more people become aware of such a possibility. In the social sciences, the situation is exactly the contrary. (Popper 2002) gave a critique and warned of the dangers of historical prediction in social systems. A hall mark of the social sciences is the lack of objectivity. Here we assert that objectivity is with respect to comparisons done by different participants and that a comparison is a precursor to a decision.

Assumption 1.

Despite the several advances in the social sciences, we have yet to discover an objective measuring stick for comparison, a so called, True Comparison Theory, which can be an aid for arriving at objective decisions.

The search for such a theory could be compared, to the medieval alchemists’ obsession with turning everything into gold. For our present purposes, the lack of such an objective measure means that the difference in comparisons, as assessed by different participants, can effect different decisions under the same set of circumstances. Hence, despite all the uncertainty in the social sciences, the one thing we can be almost certain about is the subjectivity in all decision making. This lack of an objective measure for comparisons, makes people react at varying degrees and at varying speeds, as they make their subjective decisions. A decision gives rise to an action and subjectivity in the comparison means differing decisions and hence unpredictable actions. This inability to make consistent predictions in the social sciences explains the growing trend towards comprehending better and deciphering the decision process and the subsequent actions, by collecting more information across the entire cycle of comparisons, decisions and actions.

Restricted to the particular sub-universe of economic and financial theory, this translates to the lack of an objective measuring stick of value, a so called, True Value Theory. This lack of an objective measure of value, (hereafter, value will be synonymously referred to as the price of a financial instrument), makes prices react at differing degrees and at varying velocities to the pull of different macro and micro factors. Another feature of the social sciences is that the actions of participants affects the state of the system, effecting a state transfer which perpetuates another merry-go-round of comparisons, decisions and actions from the participants involved. This means, more the participants, more the changes to the system, more the actions and more the information that is generated to be gathered. Hence perhaps, an unintended consequence of the recent developments in technology has been to increase the complexity in our lives in many ways.

The dynamic nature of the social sciences, where changes can be observed and decisions can be taken by participants to influence the system, means that along with better models and predictive technologies, predictions need to be continuously revised; and yet unintended consequences set in (Kashyap 2016); and as long as participants are free to observe the results and modify their actions, this effect will persist. (Simon 1962) points out that any attempt to seek properties common to many sorts of complex systems (physical, biological or social), would lead to a theory of hierarchy since a large proportion of complex systems observed in nature exhibit hierarchic structure; that a complex system is composed of subsystems that, in turn, have their own subsystems, and so on.

This might hold a clue to the miracle that our minds perform; abstracting away from the dots that make up a picture, to fully visualizing the image, that seems far removed from the pieces that give form and meaning to it. To help us gain a better understanding of the relationships between different elements of information, we might need a metric built from smaller parts (Kashyap 2016 has a summary of these mathematical tools), but gives optimal benefits when seen from a higher level. Contrary to what conventional big picture conversations suggest, as the spectator steps back and the distance from the picture increases, the image becomes smaller yet clearer.

3.2 More Minds versus Some Machines

We currently lack a proper understanding of how, in some instances, our brains (or minds; and right now it seems we don’t know the difference!) make the leap of learning from information to knowledge to wisdom. With no disrespect to any adults, it would not be entirely wrong to label children as better and faster learners than adults. (Holt 2017) shows that in most situations our minds work best when we use them in a certain way, and that young children tend to learn better than grownups (and better than they themselves will when they are older) because they use their minds in a special way, which is a style of learning that fits their present condition.

Problem 1.

Perhaps, the real challenge is to replicate the curiosity and learning an infant displays (Reio Jr, Petrosko, Wiswell & Thongsukmag 2006 discuss the measurement and conceptualization of curiosity; also see: Loewenstein 1994; Loewy 1998; Berlyne 1954; 1966). Intellect might be a byproduct of Inquisitiveness, demonstrating another instance of an unintended yet welcome consequence. Collecting new pieces of information is akin to curiosity in our agents from a software or financial perspective. The emphasis here is on broad information sources, that might not be seemingly related to finance, since many of the best decision makers (inside and outside of the financial realm) have diverse reading habits and eclectic tastes.

If ignorance is bliss, intrusion might just be the opposite and bring misery. As the saying goes, Curiosity Terminated the Cat and … (The movie Terminator should tell us about other unintended consequences that might pop up in the AI adventure: Cameron & Wisher 1991). This brings up the question of Art and Science in the practice of asset management (and everything else in life?); which are more related than we probably realize, “Art is Science that we don’t know about; Science is Art restricted to a set of symbols governed by a growing number of rules”. This frame of mind and approach to seeking knowledge could be termed, Science without Borders, but combined with the Arts. While the similarities between art and science, should give us hope; we need to face the realities of the situation. Right now, arguably, in most cases, we (including computers and intelligent machines?) can barely make the jump from the information to the knowledge stage; even with the use of cutting / (bleeding?) edge technology and tools. This exemplifies three things:

  1. We are still in the information age. As another route to establishing this, consider this: Information is Hidden; Knowledge is Exchanged or Bartered; Wisdom is Dispersed. Surely we are still in the Information Age since a disproportionate amount of our actions are geared towards accumulating unique data-sets for the sole benefits of the accumulators.

  2. Automating the movement to a higher level of learning, which is necessary for dealing with certain doses of uncertainty, is still far away.

  3. Some of us missed the memo that the best of humanity are actually robots in disguise, living amongst us.

Hence, it is not Manager versus Machine (Portfolio Manager vs Computing Machine or MAN vs MAC, in short; for a discussion of investment analysis, portfolio management and the building blocks of modern computers, see: Reilly & Brown 2002; Elton, Gruber, Brown & Goetzmann 2009; Bodie, Kane & Marcus 2011; Perrier, Sipper & Zahnd 1996; Davis 2011; Amir, Ben-Ishay, Levner, Ittah, Abu-Horowitz & Bachelet 2014; Thompson, Gokler, Lloyd & Shor 2016; End-notes 6, 7, 9). Not even MAN and MAC against the MPC (Microsoft Personal Computer: Freiberger & Swaine 1999; Manes & Andrews 1993; Carlton & Annotations-Kawasaki 1997; Wonglimpiyarat 2012; End-notes 10, 11, 12)? It is MAN, MAC and the MPC against increasing complexity! (Also in scope are other computing platforms from the past, present and the future: Williams 1997; Leuenberger & Loss 2001; Ceruzzi 2003; Zhang, Cheng & Boutaba 2010; End-notes 13, 14, 15, 16). This increasing complexity and information explosion is perhaps due to the increasing number of complex actions perpetrated by the actors that comprise the financial system. The human mind will be obsolete if machines can fully manage assets and we would have bigger problems on our hands than who is managing our money. We need, and will continue to need, massive computing power to mostly separate the signal from the noise. In this age of (Too Much) Information, it is imperative for Man and Machine to work together to uncover nuggets of knowledge from buckets of nonsense.

4 Buy Low / Sell High / Hold Now

To be clear, the requirement from the agent can be something simple, like giving advice on a financial strategy. In this case, the inputs can simply be the time series of numbers and the output can be just a Buy, Sell or Hold indication, since all of finance through time has involved only these three simple outcomes. The complications are mostly to get to these three results, which the agent can conjure up in its own way. But its interface with the external world, need not be anything too involved.

The formal mathematical elements are discussed in (Kashyap 2017 has a road-map for creating intelligence). These quantitative measures can be applied across aggregations of smaller elements that can aid the AI agent by providing simple yet powerful metrics to compare groups of entities and provide a relative valuation of which ones are better candidates for acquisition and /or liquidation.

4.1 Acing the Trader Turing Test

The Turing Test (TT) developed by Alan Turing (Turing 1950; French 2000 chronicles the comments and controversy surrounding the first fifty years of the TT; End-note 17), is a test of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation is a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel such as a computer keyboard and screen so the result would not depend on the machine’s ability to render words as speech (Turing originally suggested a teleprinter, one of the few text-only communication systems available in 1950). If the evaluator cannot reliably tell the machine from the human, the machine is said to have passed the test. The test does not check the ability to give correct answers to questions, only how closely answers resemble those a human would give.

Let us now consider an example of imitation in the imitation game (also known as, the Turing Test), which was also a recently released movie about the role of Alan Turing in the second world war (You 2015). The actor in the movie, Benedict Cumberbatch (Porter 2014), does a marvelous job portraying the real Alan Turing (though this is a subjective evaluation, if someone disagrees, termed a disbeliever, then it would be fair to state that they now have the responsibility of doing a better role play). The manner in which Benedict Cumberbatch plays the main character in the movie, Imitation Game, leads us to state the Real Enigma of the Imitation Game as: Which Alan Turing is the More Convincingly Brilliant Mathematician? (Figure 1; End-note 3). This question merely inquires as to whether, Alan Turing or Benedict Cumberbatch, would pass a stage test for actors who had to convince the audience they were mathematicians. To go into length on how Benedict Cumberbatch (or any disbeliever, forced to turn into a better actor) accomplished this, would require another paper or a few books of their own (Hagen 1991); the short answer would be that, an actor believes that he can play the part he is chosen to play, which is what an agent chosen to display intelligence, must first be made to believe. This is about not about dishonesty or deception, it is about belief and confidence. True confidence comes when we admit we don’t know something and we are willing to try as discussed in section 4.4.

Figure 1: The Real Enigma of the Imitation Game: Which Alan Turing is the More Convincingly Brilliant Mathematician?

The Turing test for a trader would involve less interaction than the regular Turing test, but it would require constantly assessing some quantitative measures. We could use a combination of commonly used portfolio management measures such as the Sharpe ratio, max draw-down or maximum loss amount, Value at Risk and so on. (Cartea & Jaimungal 2015) propose risk metrics to assess the performance of HFT strategies that seek to maximize profits from making the realized spread where the holding period is extremely short (fractions of a second, seconds, or at most minutes; (De Prado & Peijan 2004) measure the loss potential of hedge funds; (Fung & Hsieh 2004) is a model of hedge fund returns based on dynamic risk-factor coefficients; (Evans 2004) discuss investor attitudes toward risk, expectations of future portfolio returns, and behaviors in the use of risk metrics; (for more discussions on financial performance measurement and risk, see: Bacidore, Boquist, Milbourn & Thakor 1997; Ferguson & Leistikow 1998; Donefer 2010; Giot & Laurent 2003; Treleaven, Galas & Lalchand 2013).

An often omitted criteria that needs to be considered when administering the Turing test is the ability, or, the level of skill, of the person conducting the test. Surely, different individuals are satisfied with different levels of impersonation. In a trading context, a perfect role model for our agent to mimic, would perhaps be Michael Douglas from the movie Wall Street (Formaini 2001; Paddock, Terranova & Giles 2001). To be precise, the automation, would certainly fail to capture the many nuances to being Michael Douglas. But all it would need to do is exhibit his confidence when making financial decisions (this could be captured by how accurate the confidence intervals from probability theory given by the agent are: Rao 1973; Cramér 2016).

When we see any drama, play or movie that depicts the life of any real trader, (while reminding ourselves that movies might not be real, but real life can become movies: The Wolf of Wall Street is a great example, Belfort 2011; End-note 18); different people are satisfied with different levels of acting ability. We all know, that the person playing the role in the theatrical version is not the same, as the person, that is being enacted. But in many cases, (perhaps, in most cases, when it is well produced), we leave feeling satisfied with the result of the replication. The lesson for us here is this: how far does the test administrator need to go, to believe that the computer program perfectly duplicates a human trader or is able to surpass the quantitative benchmarks that have been set.

Attempts to chronicle what makes a good trader usually result in many interesting and varied findings. A consensus would perhaps be around the fact that better traders are better risk managers and focus their efforts on more actively looking after the downside since the upside usually takes care of itself. This results in reduced variance of the Profit & Loss (P&L) of the portfolio. Another viewpoint is that good traders can make bad trades and bad traders can make good trades, but over many iterations, the good traders end up making a greater number of good trades and hence we provide some distinctions between good and bad traders (trades) in section

4.2 (Kashyap 2014).

4.2 Good Traders, Bad Trades … Ugly Repercussions

  1. The factors that dictate a good trader or a bad trader depend on the Time Horizon and the Investment Objective. The time horizon can be classified into short term, medium term and long term. The investment objective can be conservative or aggressive. While there are no strict boundaries between these categories, such a classification helps us with the analysis and better identification of traders.

  2. Any trader that fulfills the investment objective and time horizon for which his trades are made is a good trader. Otherwise, he / she (it, in the case of artificial agents?) is a bad trader.

  3. On the face of it, we can view good traders as the profitable ones and bad traders as ones that lose money. But where possible, if we try and distinguish between proximate causes and ultimate reasons, it becomes apparent that good traders can lose money and bad traders can end up making money.

  4. As discussed in sections 2, 3.1, due to the nature of uncertainty in the social sciences: the noise around the expected performance of any security; our ignorance of the true equilibrium; the behavior of other participants; risk constraints like liquidity, concentration, unfavorable Geo-political events, etc. implies we would have deviations from our intended results. The larger the deviation from the intended results, the worse our trader is.

  5. What the above implies is that, bad traders show the deficiencies in planning (estimation process) and how we have not been able to take into account factors that can lead our results astray. It is true that due to the extreme complexity of the financial markets, the unexpected ends up happening and we can never take into account everything. We just need to make sure that the unexpected, even if it does happen, is contained in the harm it can cause. The good thing about bad traders (trades) is the extremely valuable lessons they hold for us, which takes us through the loop or trials, errors and improvements.

  6. We then need to consider, how a good trader can lose money. When we make a trade, if we know the extent to which we can lose, when this loss can occur and that situation ends up happening, our planning did reveal the possibility and extent of the loss, hence it is a good trade.

  7. The bottom line is that, good traders (trades), or bad traders (trades), are the result of our ability to come up with possible scenarios and how likely we think they will happen.

4.3 Mexican Chihuahua Trading Korean Bonds under a Mush-Room

John Searle’s thought experiment (Searle 1980; Preston & Bishop 2002 has a collection of essays on this crucial challenge; End-note 19) begins with this hypothetical premise: suppose that artificial intelligence research has succeeded in constructing a computer that behaves as if it understands Chinese. It takes Chinese characters as input and, by following the instructions of a computer program, produces other Chinese characters, which it presents as output. Suppose, says Searle, that this computer performs its task so convincingly that it comfortably passes the Turing test: it convinces a human Chinese speaker that the program is itself a live Chinese speaker. To all of the questions that the person asks, it makes appropriate responses, such that any Chinese speaker would be convinced that they are talking to another Chinese-speaking human being. This was originally phrased as: “Searle supposes that he is in a closed room and has a book with an English version of the computer program, along with sufficient paper, pencils, erasers, and filing cabinets. Searle could receive Chinese characters through a slot in the door, process them according to the program’s instructions, and produce Chinese characters as output”.

The question Searle wants to answer is this: does the machine literally "understand" Chinese? Or is it merely simulating the ability to understand Chinese? Searle calls the first position "strong AI" and the latter "weak AI". Searle writes that "according to Strong AI, the correct simulation really is a mind. According to Weak AI, the correct simulation is a model of the mind." He also writes: "On the Strong AI view, the appropriately programmed computer does not just simulate having a mind; it literally has a mind."

Searle was in fact against the notion of strong AI, which is that human minds are, in essence, computer programs. That is an appropriately programmed computer with the right inputs and outputs, would thereby have a mind in exactly the same sense human beings have minds. All mental activity, is simply the carrying out of some well-defined sequence of operations, frequently referred to, as an algorithm.

Searle’s example has had a profound impact on the discussions related to AI for the last many years. However, as a counter argument let us consider, instead of an American (John Searle) juggling with Chinese characters he has no clue about in a closed room, using instructions in English, a language he understands; what if it was a Mexican Chihuahua making decisions on a portfolio of Korean bonds, (the inputs it receives are numbers or time series of prices, other variables and perhaps other information related to the financial markets in Korean characters), sitting under a giant Mush-Room (perhaps, having devoured the mushroom and hence being influenced by it in ways, that we do not quite yet comprehend, but for the purposes of this test, the effects are only beneficial. For the hallucinogenic effects of mushrooms see: Schwartz & Smith 1988), and giving out the right answers back in the form of Korean characters (the output it provides could simply be buy, sell or hold characters in Korean or a full summary or justification of the investment decision as well) and has a remarkably good track record in terms of risk and returns, but only barks in response to everything else.

Does it matter, whether the Chihuahua is only using certain training it has been given, to use rules to arrange Korean characters as excellent investment advice, or, whether it is the Mushroom causing the miracle or something else? For all practical purposes, the Chihuahua is a great portfolio manager, that simply does not speak the same language as we do. We do not understand its barking nor does it understand the voice tones we produce, or maybe it pretends that it does not understand (it can be argued, though we won’t continue this line of reasoning, that we understand less of what dogs say, than what dogs understand of what we say; who is more intelligent then?). For simplicity and for rhetorical reasons, let us just say that the effects of the mushroom, last for as long as the Chihuahua is alive, or, until we are still interested in asking it questions about portfolio management using Korean Characters?

To substantiate this counter viewpoint, does it really matter if we are simply using rules to trade or if we are actually understanding how the investment choice was arrived at, if we completely believe that we understand the solution and merely use rules to arrive at the solution? This is not about being dishonest, or, passing lie detector tests; since, if we believe we can generate profits and if we are able to consistently generate profits, it does not matter, how we got the profits, since we should now be deemed intelligent enough, as we have come up with the profits.

4.4 I Don’t Know, A Great Answer

(Taleb 2007) in his landmark book, the Black Swan, talks about the unread books in the personal library of legendary Italian writer, Umberto Eco, and how over time, this unread collection gets larger. Hence, it would not be incorrect, to say that, there is more that, we don’t know, than, what we know; the more we know, the more, there will be to know. But that should not stop us and the agent, from trying to seek the answers, or, even from making a guess, as a starting point.

Hence, an answer admitting, “I Don’t Know” is a great answer in most situations. When we design any system or model, especially in AI, questions and answers are important, since that is the primary way to assess the presence of intelligence. But what becomes more important are our definitions and assumptions. To supplement our definition of intelligence, we provide the following cardinal assumption,

Assumption 2.

The knowledge that has been accumulated over time is lesser than the knowledge that is yet to gathered. With this assumption, an answer of “I Don’t Know” becomes, not just a correct answer, but it is an invitation to the person asking the question to teach the agent how to answer the question.

So the agent is always learning, and the reason is, simply, due to what we discussed before: we don’t know most things and hence, the learning, usually never stops. If the person asking the question is not satisfied with the answer, he or she, now has a responsibility to teach the agent, to improve upon the answer produced. A failure to create intelligence in any agent is a failure on the part of the teacher in finding a teaching methodology, appropriate for the agent. This also implies that:

Criterion 1.

Creating intelligence is not only about writing software code, it is about having the best teachers that humanity has produced, being available to teach the later generations, be it human or machines. In a trading context, to create intelligent trading machines, we need, not just the best programmers, but we also need the best traders, portfolio strategists and risk managers, to teach the agents, how to come up with investment ideas, implement them and manage the resulting risk.

We now consider the fundamental question of whether we need complicated models or merely stronger beliefs. We state this as our essential doctrine.

Criterion 2.

The intelligent agent has to believe that it has the ability to learn, and the confidence to request lessons regarding answers, that it is unable to generate satisfactorily.

Combining confidence with the great answer, which follows from our assumption 2, we get a better answer, which is “let me try”.

When an agent is not learning, it should ideally be teaching (other agents or anyone else). Perhaps because, teaching and learning are highly interconnected and the best way to learn is to teach. A realization that the roles of students and teachers, are constantly getting interchanged, originates from a belief that, everyone has something to teach, to everyone else. When we are teaching, we are also learning from someone else, when we are learning, we are really teaching ourselves. To be clear, although, most of us probably know this, learning does not just represent, reading textbooks, or, doing assignments, though, these are important components of learning. Learning can happen, when we are doing anything, that we enjoy doing. This can be built into the reward system of the agent, so that it accumulates points for aspects that it likes. Different agents could be made to like different things, so that we build a random enjoyment component that learns from different activities.

Efforts at learning and teaching, usually end up confronting two monsters: Confusion and Frustration, both of which, though, scary and ugly to begin with, can be powerful motivators, as long as, we don’t let them bother us. Confusion is the beginning of Understanding. Necessity, is the mother of all creation / innovation / invention, but the often forgotten father, is Frustration. What we learn from the story of, Beauty and the Beast, is that, we need to love the beasts to find beauty. Hence, if we start to love these monsters (Confusion and Frustration), we can unlock their awesomeness and find truly stunning solutions.

Hence, our agent has to remain confident and ask questions, when it does not have an answer. This can also be stated as,

Solution 1.

Life for an intelligent agent is all about having confidence and the right teachers and /or students.

4.5 Merry-Go-Round of Trials, Errors and Revisions

Usually, on our first attempt to answer any question, we may not get the correct, or, the best answer. This is where, the trial and error part, kicks in. But once, we start somewhere, we learn from our mistakes and improve upon our explanations. In this Question & Answer context, we define any question as a good question and a good answer as something that we only think of later or something we find after a few iterations of trial and error.

(Young 2009) is about trial and learning in a social or economic game theory setting (Gibbons 1992). A person learns by trial and error if he occasionally tries out new strategies, rejecting choices that are, erroneous, in the sense that they do not lead to higher payoffs. In an economic game, however, strategies can become erroneous due to a change of behavior by someone else, triggering a search for new and better strategies. In economics, it is insightful to establish conditions under which the Nash equilibrium property (Nash 1950) can be established. But in real life, equilibrium is a dynamic, constantly changing state due to the subjectivity in all decision making and the differing perceptions of the individuals involved and hence the trial and error never ceases.

In all efforts at creating intelligence, we make an unstated assumption that human beings are capable of intelligence. But, we are not born intelligent (maybe we are, but, perhaps, we just don’t know). It takes years of nurturing and tutoring for us to become intelligent and we display different abilities and aptitude for different things, or the intelligence of different individual could be in different skills. How could we then have expectations that something, that we deem not to have the capacity for intelligence, has to become intelligent in a relatively short span of time. This holds a strong message for us that, to create intelligence artificially, might require years of training for an agent.

In a typical classroom, some kids end up with more conventional forms of intelligence in comparison to others and as assessed by our benchmark or measure, due to creating more connections and retaining the relevant bits of information they receive. Hence, we would expect a similar sort of situation when trying to create AI, we need to start with a group of agents, with different parameters and let them wander around and see what innate abilities they pick up. Accordingly, we need to further those skills that were naturally acquired. The circle of trial, error and corrections needs to be happening constantly.

5 A Journey to the Land of Unintended Consequences

We have discussed the intuition for why we need the best teachers and not just the best computing science designers in creating automated trading systems. An unintended consequence of establishing curiosity and confidence in an agent, expected to become intelligent, might well be intelligence. We have considered why, even though we wish to create intelligence and make the agent pass tests of intelligence, the gift of intelligence might be something from the realm of the unintentional. The mathematical tools and formal elements of what such an endeavor might require, which includes models of diffusion, distance measures and dimension reduction, among other things are discussed in detail in (Kashyap 2017 has a how to guide for creating intelligence) and briefly mentioned in Appendix 6.

Algorithm 1.

We summarize the formal model, (mathematics being the language of ceremoniously concise precision) using words in the dictionary as below. Each following point could also be implemented as separate computer programs with different parameters that govern the behavior of the agent. Testing could be done independently and varying parameters can be used for combining the elements below as well.

Remark 1.

We could consider each point below as a component that can be tested as a separate scientific hypothesis (relevant references are given in the corresponding sections); but surely, greater the consonance between the ingredients that encapsulate the below concepts, better the overall outcome.

  1. Information collection is approximated using the Bass model of diffusion (section 6.2), which is used extensively in marketing to study the adoption of new products. Collecting new pieces of information is how we mimic curiosity in our agents.

  2. Information accumulated is stored and periodically the elements gathered are compared to establish how strong the connections between the elements are. When solving problems, we want to rely on bits of information, that are not too closely related, but that are also not significantly unrelated. This requires a trial and error approach, with different parameters across multiple agents, as discussed in section 4.5.

  3. To perform the comparison in step 2, we will need to use the Bhattacharyya Distance and Johnson-Lindenstrauss Lemma (sections 6.3, 6.4).

A glimpse, of what a journey towards the land of unintended consequences holds, can be seen, by reminding ourselves that all profits (P&L) generation, is but an unintended consequence. Although, to be precise, traders, do want to intentionally make profitable trades, but the exact new trades that end up becoming profitable are unintentional; they stumble upon it, as they wander around the financial landscape, putting on new positions and managing their risk.

Success is, a very relative term. In the extreme case, which is relevant in finance, sometimes, one person’s success (profit) could be someone else’s failure (loss). That being said, to triumph in creating automated strategies and almost everything else, it is important to know where we are, and start the journey towards, where we want to be. This happens by putting on trades, under the guidance of a better trader, who will tutor the agents on why certain suggestions it has made need to be revised. Such lessons need to be reflected in the knowledge store of the agent.

An unintended consequence of taking the first step on a journey, means that the percentage of the distance left to be traveled reduces from infinity to a finite number. So once we start the trip, it becomes manageable immediately. The subjectivity in how we compare things, means that the benchmark for automated trading will be constantly changing, which means, we need our agents to keep on learning, just as we need to do the same, as well.

6 Appendix A: From Words to Symbols, A Curious and Confident Trader Model

6.1 Notation and Terminology for Key Results

  • , the Bhattacharyya Distance between two multinomial populations each consisting of categories classes with associated probabilities and respectively.

  • , the Bhattacharyya Coefficient.

  • is the Bhattacharyya distance between and normal distributions or classes.

  • is the Bhattacharyya distance between two multivariate normal distributions, where .

  • is the Bhattacharyya distance between and truncated normal distributions or classes.

  • is the Bhattacharyya distance between two truncated multivariate normal distributions, where .

6.2 Bass Model of Diffusion for Information Accumulation

Collecting new pieces of information is the behavioral parallel we draw to creating curiosity in our agents. We model collection of information, using the Bass Model of Diffusion, which is used extensively in the marketing field to model the adoption of new products by consumers. One of the simplest forms of the Bass model and also the original one from the pioneer (Bass 1969; End-note 20) can be written as,


, is the change of the installed base fraction or the likelihood of purchase at time .

, is the installed base fraction.

, is the coefficient of innovation.

, is the coefficient of imitation.

Sales is the rate of change of installed base (i.e. adoption), that is, multiplied by the ultimate market potential . This is given by,

We view a new product being adopted as being equivalent to the agent collecting the adopter, which is the new piece of information that got collected. This could represent the time series of prices, or the balance sheet strength of a company, or any other element of information that could be useful towards investment decisions. Though the information need not just be restricted to finance, but could be anything related (or seemingly unrelated) and potentially useful to provide a vivid summary of the world we live in and eventually contribute to an uptick in the overall intelligence.

As alternative models, we could use models used in economics for the spread of rumors (Banerjee 1993 has a discussion of information transmission processes, which for our purposes are similar to information collection processes) and herd behavior (Christie & Huang 1995; Chiang & Zheng 2010; Chen 2013; Muñoz Torrecillas, Yalamova & McKelvey 2016 are about herding behavior in financial markets). (Banerjee 1992) is a sequential decision model in which each decision maker looks at the decisions made by previous decision makers in taking her own decision, showing that the decision rules that are chosen by optimizing individuals will be characterized by herd behavior; i.e., people will be doing what others are doing rather than using their information.

6.3 Bhattacharyya Distance for Information Comparison

We use the Bhattacharyya distance (Bhattacharyya 1943; 1946) as a measure of similarity or dissimilarity between the probability distributions of the two entities we are looking to compare. These entities could be two information sources, two securities, groups of securities, markets or any statistical populations that we are interested in studying. The Bhattacharyya distance is defined as the negative logarithm of the Bhattacharyya coefficient.

The Bhattacharyya coefficient is calculated as shown below for discrete and continuous probability distributions.

Bhattacharyya’s original interpretation of the measure was geometric (Derpanis 2008). He considered two multinomial populations each consisting of categories classes with associated probabilities and respectively. Then, as and , he noted that and

could be considered as the direction cosines of two vectors in

dimensional space referred to a system of orthogonal co-ordinate axes. As a measure of divergence between the two populations Bhattacharyya used the square of the angle between the two position vectors. If is the angle between the vectors then:

Thus if the two populations are identical: corresponding to , hence we see the intuitive motivation behind the definition as the vectors are co-linear. Bhattacharyya further showed that by passing to the limiting case a measure of divergence could be obtained between two populations defined in any way given that the two populations have the same number of variates. The value of coefficient then lies between and .

We get the following formulae (Lee and Bretschneider 2012) for the Bhattacharyya distance when applied to the case of two uni-variate normal distributions.

is the variance of the th distribution,

is the mean of the th distribution, and

are two different distributions.

The original paper on the Bhattacharyya distance (Bhattacharyya 1943) mentions a natural extension to the case of more than two populations. For an population system, each with random variates, the definition of the coefficient becomes,

For two multivariate normal distributions, where

and are the means and covariances of the distributions, and . We need to keep in mind that a discrete sample could be stored in matrices of the form and , where, is the number of observations and denotes the number of variables captured by the two matrices.

6.4 Dimension Reduction before Information Comparison

A key requirement to apply the Bhattacharyya distance in practice is to have data-sets with the same number of dimensions. (Fodor 2002; Burges 2009; Sorzano, Vargas and Montano 2014) are comprehensive collections of methodologies aimed at reducing the dimensions of a data-set using Principal Component Analysis or Singular Value Decomposition and related techniques. (Johnson and Lindenstrauss 1984) proved a fundamental result (JL Lemma) that says that any

point subset of Euclidean space can be embedded in dimensions without distorting the distances between any pair of points by more than a factor of , for any . Whereas principal component analysis is only useful when the original data points are inherently low dimensional, the JL Lemma requires absolutely no assumption on the original data. Also, note that the final data points have no dependence on , the dimensions of the original data which could live in an arbitrarily high dimension. We use the version of the bounds for the dimensions of the transformed subspace given in (Frankl and Maehara 1988; 1990; Dasgupta and Gupta 1999).

Lemma 1.

For any and any integer , let be a positive integer such that

Then for any set of points in , there is a map such that for all ,

Furthermore, this map can be found in randomized polynomial time and one such map is where, and is a matrix in which each entry is sampled i.i.d from a Gaussian distribution.

(Kashyap 2016) provides expressions for the density functions after dimension transformation when considering log normal distributions, truncated normal and truncated multivariate normal distributions (Norstad 1999). These results are applicable in the context of many variables observed in real life such as stock prices, trading volumes, sales or inventory levels, and volatilities, which do not take on negative values. We also require the expression for the dimension transformed normal distribution, since it is a better candidate to model returns. which could take on negative values. A relationship between covariance and distance measures is also derived. We point out that these mathematical concepts have many uses outside the domain of finance.

7 End-notes

  1. Numerous seminar participants suggested ways to improve the manuscript. The views and opinions expressed in this article, along with any mistakes, are mine alone and do not necessarily reflect the official policy or position of either of my affiliations or any other agency.

  2. The Red Queen’s race is an incident that appears in Lewis Carroll’s Through the Looking-Glass and involves the Red Queen, a representation of a Queen in chess, and Alice constantly running but remaining in the same spot.

    "Well, in our country," said Alice, still panting a little, "you’d generally get to somewhere else, if you run very fast for a long time, as we’ve been doing."

    "A slow sort of country!" said the Queen. "Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!"

    This quote is commonly attributed as being from Alice in Wonderland as: “My dear, here we must run as fast as we can, just to stay in place. And if you wish to go anywhere you must run twice as fast as that.”

  3. Most people when posed the question: “Is Benedict Cumberbatch a mathematician of extraordinary ability?”, would answer in the negative. This answer comes about, without most of us having met him, or, knowing whether he has been studying mathematics secretly for years but not having obtained any formal degree in the field; again highlighting, how we jump to conclusions. Unlikely as it seems, it is still probabilistically possible that he might be an exemplary mathematician. It is worth pointing out that most of us have this belief about Mr. Cumberbatch, without even knowing what his educational background is; though in this case, there are no surprises since, a quick search on Wikipedia or Google Benedict Cumberbatch, WIkipedia Link will reveal that his many years of formal training have been in acting.

  4. HFT, High Frequency Trading, Wikipedia Link In financial markets, high-frequency trading (HFT) is a type of algorithmic trading characterized by high speeds, high turnover rates, and high order-to-trade ratios that leverages high-frequency financial data and electronic trading tools (Aldridge 2013). While there is no single definition of HFT, among its key attributes are highly sophisticated algorithms, co-location, and very short-term investment horizons. HFT can be viewed as a primary form of algorithmic trading in finance (End-note 5). Specifically, it is the use of sophisticated technological tools and computer algorithms to rapidly trade securities. HFT uses proprietary trading strategies carried out by computers to move in and out of positions in seconds or fractions of a second.

  5. Algorithmic Trading, Wikipedia Link Algorithmic trading is a method of executing a large order (too large to fill all at once) using automated pre-programmed trading instructions accounting for variables such as time, price, and volume to send small slices of the order (child orders) out to the market over time. They were developed so that traders do not need to constantly watch a stock and repeatedly send those slices out manually.

  6. Portfolio Manager, Wikipedia Link A Portfolio Manager is a professional responsible for making investment decisions and carrying out investment activities on behalf of vested individuals or institutions. The investors invest their money into the portfolio manager’s investment policy for future fund growth such as a retirement fund, endowment fund, education fund and other purposes (Bodie, Kane & Marcus 2011). Portfolio managers work with a team of analysts and researchers, and are responsible for establishing an investment strategy, selecting appropriate investments and allocating each investment properly towards an investment fund or asset management vehicle.

  7. Universal Computing Machine, Wikipedia Link

    In computer science, a universal Turing machine (UTM) is a Turing machine (Minsky 1967; End-note

    8) that can simulate an arbitrary Turing machine on arbitrary input. The universal machine essentially achieves this by reading both the description of the machine to be simulated as well as the input thereof from its own tape.

  8. Turing Machine, Wikipedia Link A Turing machine is a mathematical model of computation that defines an abstract machine, which manipulates symbols on a strip of tape according to a table of rules. Despite the model’s simplicity, given any computer algorithm, a Turing machine capable of simulating that algorithm’s logic can be constructed (Sipser 2006).

  9. Computer, Wikipedia Link A computer is a device that can be instructed to carry out sequences of arithmetic or logical operations automatically via computer programming.

  10. MAC or Macintosh, Wikipedia Link The Macintosh (pronounced as MAK-in-tosh; branded as Mac since 1998) is a family of personal computers designed, manufactured, and sold by Apple Inc. since January 1984.

  11. Personal Computer, Wikipedia Link A personal computer (PC) is a multi-purpose computer whose size, capabilities, and price make it feasible for individual use.

  12. MAC vs MPC, Wikipedia Link Apple Computer, Inc. v. Microsoft Corporation, was a copyright infringement lawsuit in 1994 in which Apple Computer, Inc. (now Apple Inc.) sought to prevent Microsoft and Hewlett-Packard from using visual graphical user interface (GUI) elements that were similar to those in Apple’s Lisa and Macintosh operating systems. Mac vs PC also refers to the rivalry between the two companies to dominate the personal computer market.

  13. History Computing, Wikipedia Link The history of computing is longer than the history of computing hardware and modern computing technology and includes the history of methods intended for pen and paper or for chalk and slate, with or without the aid of tables.

  14. Computing Platform, Wikipedia Link A computing platform or digital platform is the environment in which a piece of software is executed. It may be the hardware or the operating system (OS), even a web browser and associated application programming interfaces, or other underlying software, as long as the program code is executed with it.

  15. Cloud Computing, Wikipedia Link Cloud computing is shared pools of configurable computer system resources and higher-level services that can be rapidly provisioned with minimal management effort, often over the Internet. Cloud computing relies on sharing of resources to achieve coherence and economies of scale, similar to a public utility.

  16. Quantum Computing, Wikipedia Link

    Quantum computing is computing using quantum-mechanical phenomena, such as superposition and entanglement. A quantum computer is a device that performs quantum computing. Such a computer is different from binary digital electronic computers based on transistors. Whereas common digital computing requires that the data be encoded into binary digits (bits), each of which is always in one of two definite states (0 or 1), quantum computation uses quantum bits or qubits, which can be in superpositions of states.

  17. Turing Test, Wikipedia Link The test was introduced by Turing in his 1950 paper, "Computing Machinery and Intelligence", while working at the University of Manchester (Turing, 1950; p. 460). It opens with the words: "I propose to consider the question, ’Can machines think?’" Because "thinking" is difficult to define, Turing chooses to "replace the question by another, which is closely related to it and is expressed in relatively unambiguous words." Turing’s new question is: "Are there imaginable digital computers which would do well in the imitation game?" This question, Turing believed, is one that can actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that "machines can think". Since Turing first introduced his test, it has proven to be both highly influential and widely criticised, and it has become an important concept in the philosophy of artificial intelligence (Russell & Norvig 2016).

  18. The Wolf of Wall Street (2013 Film), Wikipedia Link The Wolf of Wall Street is a 2013 American biographical black comedy crime film directed by Martin Scorsese and written by Terence Winter, based on the memoir of the same name by Jordan Belfort. It recounts Belfort’s perspective on his career as a stockbroker in New York City and how his firm Stratton Oakmont engaged in rampant corruption and fraud on Wall Street that ultimately led to his downfall.

  19. Searle’s Chinese room thought experiment, Wikipedia Link The Chinese room argument holds that a program cannot give a computer a "mind", "understanding" or "consciousness", regardless of how intelligently or human-like the program may make the computer behave. The argument was first presented by philosopher John Searle in his paper, "Minds, Brains, and Programs", published in Behavioral and Brain Sciences in 1980. It has been widely discussed in the years since. The centerpiece of the argument is a thought experiment known as the Chinese room.

  20. Bass Model of Diffusion, Wikipedia Link The Bass Model or Bass Diffusion Model was developed by Frank Bass. It consists of a simple differential equation that describes the process of how new products get adopted in a population. The model presents a rationale of how current adopters and potential adopters of a new product interact. The basic premise of the model is that adopters can be classified as innovators or as imitators and the speed and timing of adoption depends on their degree of innovativeness and the degree of imitation among adopters. The Bass model has been widely used in forecasting, especially new products’ sales forecasting and technology forecasting. Mathematically, the basic Bass diffusion is a Riccati equation (End-note 21) with constant coefficients.

  21. Riccati Equation, Wikipedia Link

    In mathematics, a Riccati equation in the narrowest sense is any first-order ordinary differential equation that is quadratic in the unknown function. In other words, it is an equation of the form

    where and . If the equation reduces to a Bernoulli equation, while if the equation becomes a first order linear ordinary differential equation. The equation is named after Jacopo Riccati (1676–1754) (see Riccati 1724).

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