Alex PredaKing’s College London

Does finance belong to the social sciences? Apparently yes—academic discipline is located mostly

in business schools Journals and professional organizations; grant giving

bodies are in the social sciences But: more and more is taught in mathematics

departments Neuroscience is making inroads Impact of big data: computational power of the kind

needed is found in the natural sciences mostly or only Do we witness a shift from a social science to a

natural science? Do we still need social scientists in finance then?

The institutional positioning of disciplines in the academia: depends on the type of data available, of the tools available for collecting and processing this data, as well as on the way in which the investigated field is organized

This means: changes in data, tools, and the investigated field can bring changes in problems, theoretical propositions, and can shift disciplines within academic fields

Thesis: we are witnessing a disciplinary shift of finance from a social science discipline to an information science-behavioral discipline

Shift is supported by: changes in our understanding of what “data” is and of conceptual problems; institutional changes in markets

Three phases in the disciplinary evolution of finance, broadly speaking:

1. as a long term decision making science (1950s-late 1970s)

2. as a decision making science under heterogeneous temporal conditions (late 1970s-mid-1990s)

3. as an information-behavioral science (mid-1990s-ongoing)

Early history of creating a “science of the stock market” in the late 19th century (concerned with the properties of financial contracts)

Late 1950s/ 1960s: finance emerges as an academic discipline in business schools (US)

Focused on specific problems: properties of financial contracts and allocation problems (ultimately decision making problems)

Theoretical propositions depend on the kind of data available and also on the particular market structure/ market actors at the time. Since we are looking at allocation problems, these issues are not independent of who does these allocations/ under what conditions

Central problem: long term decision making—portfolio allocation/ asset pricing

This helps explain the lack of concern with time in the efficient market hypothesis—i.e., decision making is time insensitive

The theoretical view is a-temporal There is only one “market” with a defined set

of properties

What kind of market actors are confronted with such allocation problems and what kind of institutions support these actors?

Long term decision making makes sense when markets are dominated by institutional actors (pension funds, mutual funds)

Decisions are periodical Price and volume data are collected at larger

intervals (daily) and are constructs (e.g., closing prices)

When the EMH and the CAPM emerge, in the early 1960s, that is exactly the situation

Financial markets are dominated by institutional investors

Price and volume data are not collected in real time

Constructs such as closing prices play a significant role

First phase: low frequency data homogeneous actors acting long term (order

flow is not a conceptual issue) clearly structured field of investigation (the

stock exchange, where information is incorporated into prices)

One “market” (regulated environment with controlled and limited cross market transactions)

Second phase: decision making science under heterogenous temporal conditions

1980s: growing influence of cognitive psychology upon financial theory

leads to the formation of an alternative view: behavioral finance, focused on proving the empirical limits of the theoretical tenets formulated in phase one

At first sight: one discipline gains influence upon another

Yet, what else happens in the 1980s? Data collection changed by technology Markets diversify (options markets take off) US securities traded outside the US—issues of

arbitrage Types of actors diversify: in the late 1980s,

individuals can transact by touch tone phone Temporal horizons diversify: not everybody is

acting long term The notion of “one market” comes under attack

In the 1980s, new kinds of conceptual problems emerge in finance:

Noise—it is not per chance that the concept emerges in the mid 1980s

Informed/ uninformed investors (first paper published in 1993 only)

Arbitrage Herding

What is broadly perceived as an empirical attack on the tenets of rational decision making can be seen as the consequence of changes in the available data, but also of changes in market actors and institutions

Criticism of behavioral finance for failure to develop a homogeneous theoretical frame can be seen as the consequence of dealing with heterogeneity

Since the mid-1990s/ early 2000s: advances in real time data collection technologies

It becomes possible to record tick by tick data High frequency data become increasingly

available, together with the computing power needed to analyse it

More market fragmentation and diversification Advances made by neuroscience led to the

hypothesis that human behavior is reducible to neurochemical processes: decision making is seen by some as a neurophysiological issue

The term econophysics is coined in 1996 Physics and mathematics departments start

offering courses in financial mathematics New problems emerge: The structure of the order flow Volatility Rare events

it seems that finance is moving away from a social science (about decision making) towards an information/ natural science

High performance computing is necessary for analysing large datasets—usually HPC is located in natural science departments (social scientists do not get access easily)

Interdisciplinary teams are needed for the analysis

Neurophysiological experiments with traders

Is there any room left for social scientists? With the advent of trading automation, and

with the advances in brain science, apparently not

Still: finance as an academic discipline had behavior and decision making at its core, first in the form of a benchmark model, then in the refutations of this model

Behavioral issues are omnipresent when analysing large data, whether it’s the structure of the order flow or liquidity in the order book

The issues remain: What do we learn about (social) behavior

from finance? How can we develop a theory of decision

making in finance from large data? What sort of theory should that be?

Growing acknowledgment that social relationships play a role in decision making (networks)—see social trading

Growing acknowledgement that communication plays a role

Decision making as socially structured (networks plus communication)

Yet we know very little about these structures

We need not only large datasets, but also large, integrated datasets—which integrate various types of data, in order to analyse decision making together with social relationships/ networks and communication

Example: identifying behavioral clusters in the order flow

Data from brokerages: not only transaction by transaction, but also demographics and financial asset data

We still know every little about who trades when and what, and how transactions relate to demographic and financial features

Key assumption: human behavior is patterned, hence so is trading behavior

Such patterns should be identifiable in the order flow Move away from the assumption of a unique,

universal behavioral pattern Move away from the assumption of ‘biased’ behaviors Move away from the assumption of “one market”

Evolution of finance as a discipline Going away from the social sciences? No, but new forms of collaboration and data

integration are needed