Experimental finance

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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 international markets 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

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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.

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

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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.

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<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. He is Professor of Neuroeconomics at the University of Cambridge.

Botond Kőszegi is an economist and a professor at Central European University.

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.
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