Experimental finance

Last updated

The goals of experimental finance are to understand human and market behavior in settings relevant to finance. Experiments are synthetic economic environments created by researchers specifically to answer research questions. This might involve, for example, establishing different market settings and environments to observe experimentally and analyze agents' behavior and the resulting characteristics of trading flows, information diffusion and aggregation, price setting mechanism and returns processes.

Contents

Fields to which experimental methods have been applied include corporate finance, asset pricing, financial econometrics, international finance, personal financial decision-making, macro-finance, banking and financial intermediation, capital markets, risk management and insurance, derivatives, quantitative finance, corporate governance and compensation, investments, market mechanisms, SME and microfinance and entrepreneurial finance. [1] [2] Researchers in experimental finance can study to what extent existing financial economics theory makes valid predictions and attempt to discover new principles on which theory can be extended.

Experimental finance is a branch of experimental economics and its most common use lies in the field of behavioral finance.

History

In 1948, Chamberlin reported results of the first market experiment. [3] Since then the acceptability, recognition, role, and methods of experimental economics have evolved. From the early 1980s on a similar pattern emerged in experimental finance. [4] The foundational work in experimental finance was the work of Forsythe, Palfrey and Plott (1980), [5] Plott and Sunder (1982), [6] and Smith, Suchanek and Williams (1988). [7]

Scientific value

Financial economics has one of the most detailed and updated observational data available of all branches of economics. Consequently, finance is characterized by strong empirical traditions. Much analysis is done on data from stock exchanges including bids, asks, transaction prices, volume, etc. There is also data available from information services on actions and events that may influence markets. Data from these sources is not able to report on expectations, on which theory of financial markets is built. In experimental markets the researcher is able to know expectations, and control fundamental values, trading institutions, and market parameters such as available liquidity and the total stock of the asset. This gives the researcher the ability to know the price and other predictions of alternative theories. This creates the opportunity to do powerful tests on the robustness of theories which were not possible from field data, since there is little knowledge on the parameters and expectations from field data. [8]

Advantages

Financial data analysis is based on data drawn from settings created for a purpose other than answering a specific research question. This results in the situation where any interpretation of the results may be challenged since it ignores other variables that have changed. Traditional data analysis issues include omitted-variables biases, self-selection biases, unobservable independent variables, and unobservable dependent variables. [9]

Properly designed experiments are able to avoid several problems: [9]

Omitted-variables bias: Multiple experiments can be created with settings that differ from one another in exactly one independent variable. This way all other variables of the setting are controlled, which eliminates alternative explanations for observed differences in the dependent variable.

Self-selection: By randomly assigning subjects to different treatment groups, the experimenters avoid issues caused by self-selection and are able to directly observe the changes in the dependent variable by changing by altering certain independent variables.

Unobservable independent variables: Experimentalists can create experimental settings themselves. This makes them able to observe all variables. Traditional data analysis may not be able to observe some variables, but sometimes experimenters cannot directly elicit certain information from subjects either. Without directly knowing a certain independent variable, good experimental design can create measures that to a large extent reflects the unobservable independent variable and the problem is therefore avoided.

Unobservable dependent variables: In traditional data studies, extracting the cause for the dependent variable to change may prove to be difficult. Experimentalists have the ability to create certain tasks that elicit the dependent variable.

Types of experiments

Laboratory experiments

Laboratory experiments are the most common form of experimentation. Here the idea is to construct a highly controlled setting in a laboratory. [9] The use of lab experiments increased due to growing interest in issues such as economic cooperation, trust, and neuroeconomics. [10] In this type of experiments, treatment is assigned randomly to a group of individuals in order to compare their economic actions and behavior to an untreated control group within the artificial laboratory environment. The ability to control the variables in the experiment provides for more accurate assessment of causality. [9]

Controlled field studies or randomized field experiments

Controlled field experiments also randomize treatments but do so in real world applications. Average effects on people's behavior can then be consistently estimated by comparing behavior before and after the allocation. [10]

Natural experiments

A natural experiment happens when some feature of the real world is randomly changed which allows using the exogenous variation due to this change to study causal effects of an otherwise endogenous explanatory variable. Natural experiments are popular in economic and finance research since they offer intuitive interpretation of the underlying identifying assumptions and enable a broader audience to check their consistency, this compared to purely statistical identification. [10]

Main findings

Experimental methods in finance offer complementary methodologies that have allowed for the observation and manipulation of underlying determinants of prices, such as fundamental values or insider information. Experimental studies complement empirical work, particularly in the area of theory testing and development. Exploiting this experimental methodology has revealed some important findings over the past years. These findings could not have been reached by traditional field data analysis alone and are therefore experimental finance’s main contributions to the field of finance: [8] [11]

See also

Related Research Articles

<span class="mw-page-title-main">Finance</span> Academic discipline studying businesses and investments

Finance is the study and discipline of money, currency and capital assets. It is related to, but not synonymous with economics, which is the study of production, distribution, and consumption of money, assets, goods and services . Finance activities take place in financial systems at various scopes, thus the field can be roughly divided into personal, corporate, and public finance.

<span class="mw-page-title-main">Stock market bubble</span> Economic bubble in a stock market

A stock market bubble is a type of economic bubble taking place in stock markets when market participants drive stock prices above their value in relation to some system of stock valuation.

Financial economics is the branch of economics characterized by a "concentration on monetary activities", in which "money of one type or another is likely to appear on both sides of a trade". Its concern is thus the interrelation of financial variables, such as share prices, interest rates and exchange rates, as opposed to those concerning the real economy. It has two main areas of focus: asset pricing and corporate finance; the first being the perspective of providers of capital, i.e. investors, and the second of users of capital. It thus provides the theoretical underpinning for much of finance.

<span class="mw-page-title-main">Technical analysis</span> Security analysis methodology

In finance, technical analysis is an analysis methodology for analysing and forecasting the direction of prices through the study of past market data, primarily price and volume. As a type of active management, it stands in contradiction to much of modern portfolio theory. The efficacy of technical analysis is disputed by the efficient-market hypothesis, which states that stock market prices are essentially unpredictable, and research on whether technical analysis offers any benefit has produced mixed results. It is distinguished from fundamental analysis, which considers a company's financial statements, health, and the overall state of the market and economy.

<span class="mw-page-title-main">Efficient-market hypothesis</span> Economic theory that asset prices fully reflect all available information

The efficient-market hypothesis (EMH) is a hypothesis in financial economics that states that asset prices reflect all available information. A direct implication is that it is impossible to "beat the market" consistently on a risk-adjusted basis since market prices should only react to new information.

<span class="mw-page-title-main">Behavioral economics</span> Academic discipline

Behavioral economics studies the effects of psychological, cognitive, emotional, cultural and social factors in the decisions of individuals or institutions, and how these decisions deviate from those implied by classical economic theory.

Experimental economics is the application of experimental methods to study economic questions. Data collected in experiments are used to estimate effect size, test the validity of economic theories, and illuminate market mechanisms. Economic experiments usually use cash to motivate subjects, in order to mimic real-world incentives. Experiments are used to help understand how and why markets and other exchange systems function as they do. Experimental economics have also expanded to understand institutions and the law.

In psychology and behavioral economics, the endowment effect is the finding that people are more likely to retain an object they own than acquire that same object when they do not own it. The endowment theory can be defined as "an application of prospect theory positing that loss aversion associated with ownership explains observed exchange asymmetries."

Charles Raymond Plott is an American economist. He currently is Edward S. Harkness Professor of Economics and Political Science at the California Institute of Technology, Director, Laboratory for Experimental Economics and Political Science, and a pioneer in the field of experimental economics. His research is focused on the basic principles of process performance and the use of those principles in the design of new, decentralized processes to solve complex problems. Applications are found in mechanisms for allocating complex items such as the markets for pollution permits in Southern California, the FCC auction of licenses for Personal Communication Systems, the auctions for electric power in California, the allocation of landing rights at the major U.S. airports, access of private trains to public railway tracks, access to natural gas pipelines, the allocation of licenses for offshore aquaculture sites, the combinatorial sale of fleets of vehicles, and the application of complex procurements. Plott has contributed extensively to the development and application of a laboratory experimental methodology in the fields of economics and political science.

<span class="mw-page-title-main">John A. List</span> American economist

John August List is an American economist known for establishing field experiments as a tool in empirical economic analysis. He works at the University of Chicago, where he serves as Kenneth C. Griffin Distinguished Service Professor; from 2012 until 2018, he served as Chairman of the Department of Economics. Since 2016, he has served as Visiting Robert F. Hartsook Chair in Fundraising at Indiana University Lilly Family School of Philanthropy. List is noted for his pioneering contributions to field experiments in economics, with Nobel prize winning economist George Akerlof and noted law professor Cass Sunstein writing that "List has done more than anyone else to advance the methods and practice of field experiments." Nobel prize winning economist Gary Becker quipped that "John List's work in field experiments is revolutionary."

The following outline is provided as an overview of and topical guide to finance:

Market microstructure is a branch of finance concerned with the details of how exchange occurs in markets. While the theory of market microstructure applies to the exchange of real or financial assets, more evidence is available on the microstructure of financial markets due to the availability of transactions data from them. The major thrust of market microstructure research examines the ways in which the working processes of a market affect determinants of transaction costs, prices, quotes, volume, and trading behavior. In the twenty-first century, innovations have allowed an expansion into the study of the impact of market microstructure on the incidence of market abuse, such as insider trading, market manipulation and broker-client conflict.

Statistical finance, is the application of econophysics to financial markets. Instead of the normative roots of finance, it uses a positivist framework. It includes exemplars from statistical physics with an emphasis on emergent or collective properties of financial markets. Empirically observed stylized facts are the starting point for this approach to understanding financial markets.

Gunduz Caginalp was a mathematician whose research has also contributed over 100 papers to physics, materials science and economics/finance journals, including two with Michael Fisher and nine with Nobel Laureate Vernon Smith. He began his studies at Cornell University in 1970 and received an AB in 1973 "Cum Laude with Honors in All Subjects" and Phi Beta Kappa. In 1976 he received a Master's degree, and in 1978 a PhD, both also at Cornell. He held positions at The Rockefeller University, Carnegie-Mellon University and the University of Pittsburgh, where he was a Professor of Mathematics until his death on December 7, 2021. He was born in Turkey, and spent his first seven years and ages 13–16 there, and the middle years in New York City.

Robert Mark Isaac is an American academic who uses experimental economics to address basic microeconomic problems. His work has provided new empirical insights for many traditional economic problems, particularly cooperation and collective action problems.

<span class="mw-page-title-main">Robert J. Shiller</span> American economist (born 1946)

Robert James Shiller is an American economist, academic, and author. As of 2022, he served as a Sterling Professor of Economics at Yale University and is a fellow at the Yale School of Management's International Center for Finance. Shiller has been a research associate of the National Bureau of Economic Research (NBER) since 1980, was vice president of the American Economic Association in 2005, its president-elect for 2016, and president of the Eastern Economic Association for 2006–2007. He is also the co‑founder and chief economist of the investment management firm MacroMarkets LLC.

Quantitative behavioral finance is a new discipline that uses mathematical and statistical methodology to understand behavioral biases in conjunction with valuation.

David Alan Easley is an American economist. Easley is the Henry Scarborough Professor of Social Science and is a professor of information science at Cornell University.

<span class="mw-page-title-main">Shyam Sunder (economist)</span> American accounting theorist and experimental economist

Shyam Sunder is an accounting theorist and experimental economist. He is the James L. Frank Professor of accounting, economics, and finance at the Yale School of Management; a professor in Yale University’s Department of Economics; and a Fellow of the Whitney Humanities Center.

Peter L. Bossaerts is a Belgian-American economist. He is considered one of the pioneers and leading researchers in neuroeconomics and experimental finance.

References

  1. ↑ Füllbrunn, Sascha, and Ernan Haruvy, eds. (2022) Handbook of Experimental Finance. Edward Elgar Publishing. https://www.e-elgar.com/shop/gbp/handbook-of-experimental-finance-9781800372320.html
  2. ↑ Lucey, Brian M. (August 26, 2013). A New Journal – Journal of Behavioral and Experimental Finance. http://brianmlucey.wordpress.com/2013/08/26/a-new-journal-journal-of-behavioral-and-experimental-finance/
  3. ↑ Chamberlin, Edward H (1948). "An Experimental Imperfect Market" (PDF). Journal of Political Economy. 56 (2): 95–108. doi:10.1086/256654.
  4. ↑ Sunder, Shyam. (June, 2013). Experimental Finance: Responsibilities of Coming of Age. Society for Experimental Finance, Tilburg University, Tilburg, The Netherlands. http://faculty.som.yale.edu/shyamsunder/Research/Experimental%20Economics%20and%20Finance/Presentations%20and%20Working%20Papers/Tilburg-Jun2013/SEFAddressTilburgJune2013.ppt Archived 2016-03-04 at the Wayback Machine
  5. ↑ Forsythe, R.; Palfrey, T.; Plott, C. R. (1982). "Asset Valuation in an Experimental Market" (PDF). Econometrica. 50 (3): 537–568. doi:10.2307/1912600. JSTOR   1912600. Archived from the original (PDF) on 2014-02-02. Retrieved 2014-01-29.
  6. ↑ Plott, C. R. and Sunder, S. (1982). "Efficiency of Experimental Security Markets with Insider Information: An Application of Rational Expectations Models", Journal of Political Economy, 90(4), 663-698.
  7. ↑ Smith, V. L.; Suchanek, G.; Williams, A. (1988). "Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets" (PDF). Econometrica. 56 (5): 1119–1151. CiteSeerX   10.1.1.360.174 . doi:10.2307/1911361. JSTOR   1911361. Archived from the original (PDF) on 2014-02-02. Retrieved 2014-01-29.
  8. 1 2 Sunder, Shyam (2007). "What Have We Learned from Experimental Finance?". Developments on Experimental Economics. Lecture Notes in Economics and Mathematical Systems. Vol. 590. pp. 91–100. doi:10.1007/978-3-540-68660-6_6. ISBN   978-3-540-68659-0.
  9. 1 2 3 4 Bloomfield, Robert and Anderson, Alyssa. "Experimental finance" Archived 2016-03-04 at the Wayback Machine . In Baker, H. Kent, and Nofsinger, John R., eds. Behavioral finance: investors, corporations, and markets. Vol. 6. John Wiley & Sons, 2010. pp. 113-131. ISBN   978-0470499115
  10. 1 2 3 Sauter, Wolf N. (2010). "Essays on Natural Experiments in Behavioral Finance and Trade". Doctoral dissertation, Ludwig-Maximilians University, München.
  11. ↑ Noussair, Charles N.; Tucker, Steven (2013). "Experimental research on asset pricing". Journal of Economic Surveys. 27 (3): 554–569. doi:10.1111/joes.12019. Archived from the original on 2014-02-03. Retrieved 2014-01-29.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.