See alsoOutline of finance: § Financial math- ematics; § Mathematical tools; § Derivatives pricing.
Mathematical tools Derivatives pricing
8.1.4
See also
• Computational finance
• Quantitative Behavioral Finance
• Derivative (finance),list of derivatives topics
• Modeling and analysis of financial markets
• Technical analysis
• International Swaps and Derivatives Association
• Fundamental financial concepts - topics
• Model (economics)
• List of finance topics
• List of economics topics,List of economists
• List of accounting topics
• Statistical Finance
• Brownian model of financial markets
• Master of Mathematical Finance
• Financial economics
8.1.5
Notes
[1] Johnson, Tim. “What is financial mathematics?". +Plus Magazine. Retrieved 28 March 2014.
[2] “Quantitative Finance”. About.com. Retrieved 28 March 2014.
[3] Bachelir, Louis.“The Theory of Speculation”. Retrieved 28 March 2014.
[4] Lindbeck, Assar. “The Sveriges Riksbank Prize in Eco- nomic Sciences in Memory of Alfred Nobel 1969-2007”. Nobel Prize. Retrieved 28 March 2014.
[5] Brown, Angus (1 Dec 2008). “A risky business: How to price derivatives”. Price+ Magazine. Retrieved 28 March 2014.
[6] Karatzas, Ioannis; Shreve, Steve (1998). Methods of Mathematical Finance. Secaucus, NJ, USA: Springer- Verlag New York, Incorporated.ISBN 9780387948393. [7] Meucci, Attilio (2005). Risk and Asset Allocation.
Springer.ISBN 9783642009648.
[8] Taleb, Nassim Nicholas(2007). The Black Swan: The Impact of the Highly Improbable. Random House Trade.
ISBN 978-1-4000-6351-2.
[9] “Financial Modelers’ Manifesto”. Paul Wilmott’s Blog. January 8, 2009. Retrieved June 1, 2012.
[10] Gillian Tett (April 15, 2010). “Mathematicians must get out of their ivory towers”.Financial Times.
[11] Svetlozar T. Rachev,Frank J. Fabozzi, Christian Menn (2005). Fat-Tailed and Skewed Asset Return Distribu- tions: Implications for Risk Management, Portfolio Selec- tion, and Option Pricing. John Wiley and Sons. ISBN 978-0471718864.
[12] B. Mandelbrot, The variation of certain Speculative Prices, The Journal of Business 1963
8.1.6 References
• Harold Markowitz, Portfolio Selection, Journal of Finance, 7, 1952, pp. 77–91
• William F. Sharpe, Investments, Prentice-Hall, 1985 • Attilio Meucci, P versus Q: Differences and Com- monalities between the Two Areas of Quantitative Fi- nance, GARP Risk Professional, February 2011, pp. 41–44
• Nicole El Karoui,The Future of Financial Mathe- matics, ParisTech Review, September 2013
Chapter 9
Experimental finance
9.1 Experimental finance
The goals of experimental finance are to understand hu- man and market behavior in settings relevant to finance. Experiments are synthetic economic environments cre- ated by researchers specifically to answer research ques- tions. This might involve, for example, establishing dif- ferent market settings and environments to observe ex- perimentally and analyze agents’ behavior and the result- ing characteristics of trading flows, information diffu- sion and aggregation, price setting mechanism and returns processes.
Fields to which experimental methods have been ap- plied include corporate finance, asset pricing, finan- cial econometrics, international finance, personal finan- cial decision-making, macro-finance, banking and finan- cial intermediation, capital markets, risk management and insurance, derivatives, quantitative finance, corpo- rate governance and compensation, investments, market mechanisms, SME and microfinance and entrepreneurial finance.[1]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 eco- nomics and its most common use lies in the field of behavioral finance.
9.1.1
History
In 1948, Chamberlin reported results of the first mar- ket experiment.[2] Since then the acceptability, recog- nition, role, and methods of experimental economics have evolved. From the early 1980s on a similar pat- tern emerged in experimental finance.[3]The foundational work in experimental finance was the work ofForsythe, PalfreyandPlott(1980),[4] Plott and Sunder (1982),[5] andSmith, Suchanek and Williams (1988).[6]
9.1.2 Scientific value
Financial economics has one of the most detailed and up- dated observational data available of all branches of eco- nomics. Consequently, finance is characterized by strong empirical traditions. Lots of analysis is done on data from stock exchangesincluding 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 expecta- tions, on whichtheory of financial marketsis built. In experimental markets the researcher is able to know ex- pectations, and control fundamental values, trading insti- tutions, 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.[7]
9.1.3 Advantages
Financial data analysis is based on data drawn from set- tings created for a purpose other than answering a spe- cific 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 includeomitted-variables biases,self- selection biases,unobservable independent variables, and unobservable dependent variables.[8]
Properly designed experiments are able to avoid these problems:[8]
Avoid omitted-variables biases
Multiple experiments can be created with settings that differ from one another in exactly one independent vari- able. This way all other variables of the setting are con- trolled, which eliminates alternative explanations for ob- served differences in the dependent variable.
Avoid self-selection problems
By randomly assigning subjects to different treatment groups, the experimenters avoid issues caused by self- selectionand are able to directly observe the changes in the dependent variable by changing by altering certain in- dependent variables.
Avoid problems of unobservable independent vari- ables
Experimentalists can create experimental settings them- selves. 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 designcan create measures that to a large extent reflects the unobservable independent variable and the problem is therefore avoided.
Avoid problems of unobservable dependent variables
In traditional data studies, extracting the cause for the de- pendent variable to change may prove to be difficult. Ex- perimentalists have the ability to create certain tasks that elicit the dependent variable.
9.1.4
Types of experiments
Laboratory experimentsLaboratory experimentsare the most common form of experimentation. Here the idea is to construct a highly controlled setting in a laboratory.[8]The use of lab exper- iments increased due to growing interest in issues such as economic cooperation, trust, andneuroeconomics.[9] In this type of experiments, treatment is assigned randomly to a group of individuals in order to compare their eco- nomic 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 ofcausality.[8]
Controlled field studies or randomized field experi- ments
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.[9]
Natural experiments
Anatural experimenthappens 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 un- derlying identifying assumptions and enable a broader au- dience to check their consistency, this compared to purely statistical identification.[9]
9.1.5 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. Experi- mental studies complement empirical work, particularly in the area of theory testing and development. Exploiting this experimental methodology has revealed some impor- tant 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 contribu- tions to the field of finance:[7][10]
• Security markets can aggregate and disseminate in- formation (there areefficient markets), but this pro- cess is less effective as the information becomes less widely held and the number of information compo- nents that must be aggregated increases.
• But this is not always the case (some of them are inefficient).
• Wheninformation disseminationoccurs, it is rarely perfect or instantaneous. Learning takes time. • More information is not always better from the point
of view of the individual trader. Only those insid- ers who are much better informed than others can outperform other traders.
• Markets for longer-lived assets have a strong ten- dency to generate price bubblesand crashes, pro- longed deviations from fundamental values. • Emotions of traders play a role in generating bubbles
in experimental asset markets.
• Asset mispricing has been largely associated with trader overconfidence.
• Prices as well as bids, offers, timing, etc., convey in- formation. There are many channels for information flow.
• Well-functioningderivative marketscan help to im- proveprimary markets’ efficiency.
9.1. EXPERIMENTAL FINANCE 85
• Statistical efficiencyor inability to make money us- ing past data does not mean informational efficiency. Not being able to earn abnormal returns from the market does not mean that the price is right.
9.1.6
See also
• Experimental economics
• Behavioral economics
• Game theory
9.1.7
References
[1] Lucey, Brian M. (August 26, 2013). A New Jour- nal – Journal of Behavioral and Experimental Fi- nance. http://brianmlucey.wordpress.com/2013/08/26/ a-new-journal-journal-of-behavioral-and-experimental-finance/
[2] Chamberlin, Edward H. (1948). “An Experimental Im- perfect Market”. Journal of Political Economy, 56(2), 95- 108.
[3] Sunder, Shyam. (June, 2013). Experimen- tal 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
[4] Forsythe, R., Palfrey, T. and Plott, C. R. (1982).“Asset Valuation in an Experimental Market”. Econometrica, 50(3), 537-568.
[5] Plott, C. R. and Sunder, S. (1982).“Efficiency of Exper- imental Security Markets with Insider Information: An Application of Rational Expectations Models”, Journal of Political Economy, 90(4), 663-698.
[6] Smith, V. L., Suchanek, G. and Williams, A. (1988).
“Bubbles, Crashes, and Endogenous Expectations in Ex- perimental Spot Asset Markets”, Econometrica, 56(5), 1119-1151.
[7] Sunder, Shyam. (2007). “What have we learned from experimental finance?". Developments on Ex- perimental Economics. Springer Berlin Heidelberg. 91-100. http://link.springer.com/chapter/10.1007/ 978-3-540-68660-6_6#
[8] Bloomfield, Robert and Anderson, Alyssa.“Experimental finance”. 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
[9] Sauter, Wolf N. (2010).“Essays on Natural Experiments in Behavioral Finance and Trade”. Doctoral dissertation, Ludwig-Maximilians University, München.
[10] Noussair, Charles N. and Tucker, Steven. (2013).
“Experimental research on asset pricing”. Journal of Eco- nomic Surveys, 27(3), 554-569.
9.1.8 External links
Behavioral finance
10.1 Behavioral finance
Behavioral economics and the related field, behav- ioral finance, study the effects of psychological, so- cial, cognitive, and emotional factors on theeconomic decisions of individuals and institutions and the con- sequences for market prices, returns, and the resource allocation.[1] Behavioral economics is primarily con- cerned with the bounds of rationality of economic agents. Behavioral models typically integrate insights frompsychology,neuroscienceandmicroeconomic the- ory; in so doing, these behavioral models cover a range of concepts, methods, and fields.[2][3]Behavioral economics is sometimes discussed as an alternative to neoclassical economics.
The study of behavioral economics includes howmarket decisions are made and the mechanisms that drivepublic choice. The use of “Behavioral economics” in U.S. schol- arly papers has increased in the past few years as a recent study shows.[4]
There are three prevalent themes in behavioral finances:[5] • Heuristics: People often make decisions based on
approximaterules of thumband not strict logic. • Framing: The collection of anecdotes and
stereotypes that make up the mental emotional filters individuals rely on to understand and respond to events.
• Market inefficiencies: These include mis-pricings
andnon-rational decision making.
10.1.1
Issues
Behavioral financeThe central issue in behavioral finance is explaining why market participants make irrationalsystematic errors contrary to assumption of rational market participants.[1] Such errors affect prices and returns, creating market in- efficiencies. It also investigates how other participants take advantage (arbitrage) of such errors and market in- efficiencies.
Behavioral finance highlights inefficiencies such as un- der or over-reactions to information as causes ofmarket trendsand in extreme cases ofbubblesandcrashes. Such reactions have been attributed to limited investor atten- tion, overconfidence, overoptimism, mimicry (herding instinct) and noise trading. Technical analysts consider behavioral finance, to be behavioral economics’ “aca- demic cousin” and to be the theoretical basis fortechnical analysis.[6]
Other key observations include the asymmetry between decisions to acquire, or keep resources, known as the “bird in the bush” paradox, andloss aversion, the unwill- ingness to let go of a valued possession. Loss aversion appears to manifest itself in investor behavior as a re- luctance to sell shares or other equity, if doing so would result in a nominal loss.[7]It may also help explain why housing prices rarely/slowly decline to market clearing levels during periods of low demand.
Benartzi and Thaler (1995), applying a version of prospect theory, claim to have solved theequity premium puzzle, something conventional finance models have been unable to do so far.[8] Experimental financeapplies the experimental method, e.g., creating an artificial market by some kind of simulation software to study people’s decision-making process and behavior in financial mar- kets.
Quantitative behavioral finance Quantitative behav- ioral finance uses mathematical and statistical method- ology to understand behavioral biases. In marketing re- search, a study shows little evidence that escalating bi- ases impact marketing decisions.[9]Leading contributors includeGunduz Caginalp (Editor of theJournal of Be- havioral Financefrom 2001–2004) and collaborators in- cluding 2002 NobelistVernon Smith, David Porter, Don Balenovich,[10] Vladimira Ilieva and Ahmet Duran,[11] and Ray Sturm.[12]
Financial models
Some financial models used in money management and asset valuation incorporate behavioral finance parame- ters, for example:
10.1. BEHAVIORAL FINANCE 87
• Thaler’s model of price reactions to informa- tion, with three phases, underreaction-adjustment- overreaction, creating a pricetrend
One characteristic of overreaction is that aver- age returns following announcements of good news is lower than following bad news. In other words, overreaction occurs if the market reacts too strongly or for too long to news, thus requir- ing adjustment in the opposite direction. As a result, outperforming assets in one period are likely to underperform in the following period. This also applies to customers’ irrational pur- chasing habits.[13]
• Thestock imagecoefficient
Criticisms Critics such asEugene Famatypically sup- port theefficient-market hypothesis. They contend that behavioral finance is more a collection of anomalies than a true branch offinanceand that these anomalies are ei- ther quickly priced out of the market or explained by appealing to market microstructure arguments. How- ever, individualcognitive biasesare distinct from social biases; the former can be averaged out by the market, while the other can create positive feedback loopsthat drive the market further and further from a "fair price" equilibrium. Similarly, for an anomaly to violate mar- ket efficiency, an investor must be able to trade against it and earn abnormal profits; this is not the case for many anomalies.[14]
A specific example of this criticism appears in some ex- planations of theequity premium puzzle. It is argued that the cause isentry barriers(both practical and psycholog- ical) and that returns between stocks and bonds should equalize as electronic resources open up the stock mar- ket to more traders.[15]In reply, others contend that most personal investment funds are managed through superan- nuation funds, minimizing the effect of these putative en- try barriers. In addition, professional investors and fund managers seem to hold more bonds than one would ex- pect given return differentials.
Behavioral game theory
Main article:Behavioral game theory
Behavioral game theory analyzes interactive strategic decisions and behavior using the methods of game theory,[16] experimental economics, and experimental psychology. Experiments include testing deviations from typical simplifications of economic theory such as the independence axiom[17] and neglect ofaltruism,[18] fairness,[19] andframing effects.[20]On thepositiveside, the method has been applied to interactive learning[21]
andsocial preferences.[22][23]As a research program, the subject is a development of the last three decades.[24]
Economic reasoning in non-human animals
A handful ofcomparative psychologistshave attempted to demonstrate economic reasoning in non-human ani- mals. Early attempts along these lines focus on the be- havior ofrats and pigeons. These studies draw on the tenets ofcomparative psychology, where the main goal is to discover analogs to human behavior inexperimentally- tractable non-human animals. They are also method- ologically similar to the work ofFersterandSkinner.[25] Methodological similarities aside, early researchers in non-human economics deviate frombehaviorismin their terminology. Although such studies are set up primar- ily in anoperant conditioning chamber, using food re- wards for pecking/bar-pressing behavior, the researchers describe pecking and bar pressing not in terms of reinforcement and stimulus–response relationships, but instead in terms of work,demand,budget, andlabor. Re- cent studies have adopted a slightly different approach, taking a moreevolutionaryperspective, comparing eco- nomic behavior of humans to a species of non-human primate, thecapuchin monkey.[26]
Non-human animal studies Many early studies of non-human economic reasoning were performed on rats and pigeons in an operant conditioning chamber. These studies looked at things like peck rate (in the case of the pigeon) and bar-pressing rate (in the case of the rat) given certain conditions of reward. Early researchers claim, for example, that response pattern (pecking/bar pressing rate) is an appropriate analogy to humanlabor supply.[27] Researchers in this field advocate for the appropriateness of using animal economic behavior to understand the ele- mentary components of human economic behavior.[28]In a paper by Battalio, Green, and Kagel (1981, p 621),[27] they write
Labor supply The typical laboratory environment to study labor supply in pigeons is set up as follows. Pi- geons are first deprived of food. Since the animals are hungry, food becomes highly desired. The pigeons are placed in an operant conditioning chamber and through orienting and exploringthe environment of the chamber they discover that by pecking a small disk located on one side of the chamber, food is delivered to them. In effect, pecking behavior becomesreinforced, as it is associated with food. Before long, the pigeon pecks at the disk (or stimulus) regularly.
In this circumstance, the pigeon is said to “work” for the food by pecking. The food, then, is thought of as the currency. The value of the currency can be adjusted in several ways, including the amount of food delivered, the
rate of food delivery and the type of food delivered (some foods are more desirable than others).
Economic behavior similar to that observed in humans is discovered when the hungry pigeons stop working/work less when the reward is reduced. Researchers argue that this is similar tolabor supplybehavior in humans. That is like humans (who, even in need, will only work so much for a given wage) the pigeons demonstrate decreases in pecking (work) when the reward (value) is reduced.[27]
Demand In human economics, a typicaldemand curve hasnegative slope. This means that as the price of a cer-