Экспериментальные финансы: методы и применение в изучении финансовых рынков.

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. 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. 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.

In 1948, Chamberlin reported results of the first market experiment. 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. The foundational work in experimental finance was the work of Forsythe, Palfrey and Plott (1980), Plott and Sunder (1982), and Smith, Suchanek and Williams (1988).

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.

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. Properly designed experiments are able to avoid several problems: 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. Security markets can aggregate and disseminate information (there are efficient markets), but this process is less effective as the information becomes less widely held and the number of information components that must be aggregated increases. But this is not always the case (some of them are inefficient). When information dissemination occurs, 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 insiders who are much better informed than others can outperform other traders. Markets for longer lived assets have a strong tendency to generate price bubbles and crashes, prolonged 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 information. There are many channels for information flow. Well functioning derivative markets can help to improve primary markets’ efficiency. Statistical efficiency or inability to make money using 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.