Experiments in Finance?!
"Finance does not need experiments, because we have lots of data"
Experimentation in finance is both rare and novel, so some justification is in order. The goal of experimentation is twofold.
First, experimentation is meant to evaluate the science behind finance. Indeed, complete scientific validation at some point does require experimentation, otherwise, to quote the late Hannes Alfven (Nobel prize physics), we are "likely to go completely astray into imaginary conjecture.”
Effectively, good experiments require the same as good theory: one needs to isolate a phenomenon, abstracting from complicating and confounding factors. As such, good theory (e.g., in finance: the Lucas model) and good experiments (see our experiments on the Lucas model) may often come over as "irrealistic." That is perfectly all right, because realism is not the goal per se.
The second goal of experimentation is to come to a deeper understanding of the theory. Our own experience with experiments confirms that one cannot appreciate all the ramifications (and the beauty) of the theory without thinking through how one could generate it in the laboratory. To paraphrase the late Richard Feynman (another Nobel prize in physics): "One cannot understand theory if one cannot create it.”read more
Financial risks are a relatively recent phenomenon, evolutionary speaking. As such, the human brain may not have perfectly adapted protocol to deal with it. In a first stage, we would like to know how the brain perceives risks and how it generates decisions in very controlled, yet "ecologically relevant" situations.
We already know quite a bit about reward learning, and how simple but robust temporal difference modeling is hardwired in the human brain (and the brains of monkeys, rats and mice, for that matter). The team has brought another aspect to the forefront in this regard: risk perception and risk learning. These are necessary for any organism that is risk-sensitive (whether risk-averse of risk-loving) or even just attempts to learn to predict stochastic payoffs as accurately as possible.
We are also going beyond reward learning, to study belief formation about events that are not (yet) linked to rewards (or losses), like when you are predicting whether it will snow tomorrow but don't know yet whether you'll be given the opportunity to go skiing (reward) or have to drive on slippery roads with the risk of crashing (loss).
And we study strategic uncertainty, which is what happens when you don’t know what the opponent is going to do yet it matters (think about a soccer player kicking a penalty, or a baseball pitcher trying to mislead the batter). Game theory gives us ideas of how to study this formally. Strategic uncertainty is an important component of what psychologists call theory of mind.read more
Functional: Financial economics, asset pricing, decision neuroscience, computational neuroscience, artificial intelligence, economics, neurobiology, cognitive psychology, computational complexity.
Topical: Leptokurtosis, extreme events, outliers, black swans, risk learning, algorithmic trading, human-robot interface, dark markets, problem solving, intertemporal asset pricing, market bubbles and crashes, asymmetric reasoning.
Anatomical: Anterior insula, fronto-parietal network, attentional network, dorsolateral prefrontal cortex, dopamine, norepinephrine, locus coeruleus, acetylcholine, serotonin, medial prefrontal cortex, anterior cingulate cortex, amygdala.
Socio-architectural: Centralized markets, open book systems, over-the-counter markets, two-sided combinatorial auctions.
Educational: Centralized markets, open book systems, over-the-counter markets, two-sided combinatorial auctions.
Methodological: Experiments, evidence-based policy and rule making.
Experiments on the interaction of humans and robots in a well studied setting, that of Smith, Suchanek and Williams (1998) bubble markets. This project is in a very early stage. Bubbles do continue occurring. Here, we provide the instructions for this experiment.
Experiments based on the work of Duffy, Manso and coauthors. We have run pilot experiments that show how in a dark market, prices aggregate all available information as if in a centralized exchange – only, this aggregation is not common knowledge.
Financial markets generate distinctly non-gaussian outcomes. Evidence is mounting that the noradrenergic system in the brain is hardwired to react to outliers. The reaction is well-adapted to situations where outliers signal regime shifts but it is ill-adapted to outliers in financial markets.
Humans constantly try to outsmart the opponent--they think "theory of mind." This property of human behavior has allowed us to elucidate the computations taking place in the "social" parts of the cortex.
We have also compared this to how non-human primates such as chimpanzees play this game. Surprisingly, chimpanzees play Nash!