How About behavioral finance ??
Quantitative behavioral finance is a new discipline that uses mathematical and statistical methodology to understand behavioral biases jointly with the valuation.
Some of this effort has been lead by Gunduz Caginalp (mathematics professor and editor of the Journal of Behavioral Finance during 2001-2004) and collaborators including Vernon Smith (2002 Nobel laureate in economics), David Porter, put Balenovich , Vladimira Ilieva, Ahmet Duran, Huseyin Merdan).
Studies by Jeff Madura, Ray Sturm and others have demonstrated significant behavioral effects in stock and exchange traded funds.
Research can be grouped into the following areas:
* Empirical studies that demonstrate significant deviations from classical theories.
* Modeling using the concepts of behavioral effects together with the non-classical assumption of the finiteness of assets.
* Forecast based on these methods.
* Studies of experimental asset markets and use of models to forecast experiments.
History of Quantitative behavioral finance
The common theory of financial markets during the second half of the twentieth century has been the efficient market hypothesis (EMH) which states that all public information is incorporated into asset prices. Any deviation from the true price is quickly exploited by informed traders who seek to optimize their returns and restores the equilibrium real price. For all practical purposes, then, market prices behave as if all traders were pursuing their own interest with the information.
Towards the end of the twentieth century, this theory was challenged in several ways. First, there was a large number of market events that cast doubt on the basic assumptions. October 19, 1987, the Dow Jones average plunged over 20% of a single day, much smaller stocks suffered more losses profundas.Las large swings in the ensuing days provided a figure that resembled the famous crash of 1929 . The crash of 1987 provided a puzzle and a challenge to most economists had believed that such volatility should not exist in an age where information and capital flows are much more efficient than they were in the 20′s.
As the decade continued, the Japanese market rose to the heights that were far from any realistic assessment of valuations. ratios of the price-earnings rose to triple digits, such as telephone and telegraph Nippon reached a market valuation (the stock price times the number of parties) that exceeded the entire market capitalization of the Federal Republic Germany.
These large bubbles and crashes in the absence of significant changes in the valuation cast doubt on the assumption of efficient markets incorporate all public information accurately. In his book, “irrational exhuberance,” Robert Shiller discusses the excesses that have plagued markets and concludes that stock prices move in excess of changes in valuation. This line of reasoning has also been confirmed in several studies (eg, Jeffrey Pontiff), the closed end funds that trade like stocks, but has a precise valuation is reported frequently. (See Seth Anderson and “closed end fund that led Jeffrey priced” for the review of papers relating to these issues.)
In addition to these developments in the world, other challenges to the EMH classical economics came from the new field of experimental economics initiated by Vernon Smith who won the 2002 Nobel Prize in economics. These experiments (in collaboration with Gerry Suchanek, and Arlington Williams and others goalkeeper David) offering an active trading participants defined by the experimenters on a computer network. A series of experiments involved a single asset that pays a fixed dividend in each of 15 periods and then becomes void. Contrary to the expectations of classical economics, the trading price often rises to levels much higher than the disbursement schedule. Similarly, other experiments showed that many of the expected results of economics and classical game theory are not borne out in experiments.
Research on quantitative behavioral finance. Attempt to quantify the basic diagonal and use mathematical models is the subject of quantitative behavioral finance. Caginalp and colleagues have used mathematical and statistical methods in the world market data and experimental data of the economy to make quantitative predictions. In a series of papers dating back to 1989, Caginalp and collaborators have studied asset market dynamics using differential equations that incorporate strategies and biases of investors such as the trend and valuation of price in a cash system with and finite asset. This feature is different from conventional finance in which the assumption of arbitration is infinite.
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