Quantitative behavioral finance
Encyclopedia
Quantitative behavioral finance is a new discipline that uses mathematical and statistical methodology to understand behavioral biases in conjunction with valuation
. Some of this endeavor has been led by Gunduz Caginalp
(Professor of Mathematics and Editor of Journal of Behavioral Finance
during 2001–2004) and collaborators including Vernon L. Smith
(2002 Nobel Laureate in Economics), David Porter, Don Balenovich, Vladimira Ilieva, Ahmet Duran). Studies by Jeff Madura, Ray Sturm and others have demonstrated significant behavioral effects in stocks and exchange traded funds.
The research can be grouped into the following areas:
s during the second half of the 20th century has been the efficient market hypothesis
(EMH) which states that all public information is incorporated into asset prices. Any deviation from this true price is quickly exploited by informed traders who attempt to optimize their returns and it restores the true equilibrium price. For all practical purposes, then, market prices behave as though all traders were pursuing their self-interest with complete information and rationality.
Toward the end of the 20th century, this theory was challenged in several ways. First, there were a number of large market events that cast doubt on the basic assumptions. On October 19, 1987
the Dow Jones average plunged over 20% in a single day, as many smaller stocks suffered deeper losses. The large oscillations on the ensuing days provided a graph that resembled the famous crash of 1929. The crash of 1987 provided a puzzle and challenge to most economists who had believed that such volatility
should not exist in an age when information and capital flows are much more efficient than they were in the 1920s.
As the decade continued, the Japanese market soared to heights that were far from any realistic assessment of the valuations. Price-earnings ratios soared to triple digits, as Nippon Telephone and Telegraph achieved a market valuation (stock market price times the number of shares) that exceeded the entire market capitalization of West Germany. In early 1990 the Nikkei index
stood at 40,000, having nearly doubled in two years. In less than a year the Nikkei dropped to nearly half its peak.
Meanwhile, in the US the growth of new technology, particularly the internet, spawned a new generation of high tech companies, some of which became publicly traded long before any profits. As in the Japanese stock market bubble
a decade earlier these stocks soared to market valuations of billions of dollars sometimes before they even had revenue. The bubble continued into 2000 and the consequent bust reduced many of these stocks to a few percent of their prior market value. Even some large and profitable tech companies lost 80% of their value during the period 2000-2003.
These large bubbles and crashes in the absence of significant changes in valuation cast doubt on the assumption of efficient markets that incorporate all public information accurately. In his book, “Irrational Exuberance”
, 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 (e.g., Jeffrey Pontiff ), of closed-end fund
s which trade like stocks, but have a precise valuation that is reported frequently. (See Seth Anderson and Jeffrey Born “Closed-end Fund Pricing” for review of papers relating to these
issues.)
In addition to these world developments, other challenges to classical economics and EMH came from the new field of experimental economics
pioneered by Vernon L. Smith
who won the 2002 Nobel Prize in Economics. These experiments (in collaboration with Gerry Suchanek, Arlington Williams and David Porter and others) featuring participants trading an asset defined by the experimenters on a network of computers. A series of experiments involved a single asset which pays a fixed dividend during each of 15 periods and then becomes worthless. Contrary to the expectations of classical economics, trading prices often soar to levels much higher than the expected payout. Similarly, other experiments showed that many of the expected results of classical economics and game theory are not borne out in experiments. A key part of these experiments is that participants earn real money as a consequence of their trading decisions, so that the experiment is an actual market rather than a survey of opinion.
Behavioral finance
(BF) is a field that has grown during the past two decades in part as a reaction to the phenomena described above. Using a variety of methods researchers have documented systematic biases (e.g., underreaction, overreaction, etc.) that occur among professional investors as well as novices. Behavioral finance researchers generally do not subscribe to EMH as a consequence of these biases. However, EMH theorists counter that while EMH makes a precise prediction about a market based upon the data, BF usually does not go beyond saying that EMH is wrong.
Research in Quantitative Behavioral Finance
The attempt to quantify basic biases and to use them in mathematical models is the subject of Quantitative Behavioral Finance. Caginalp
and collaborators have used both statistical and mathematical methods on both the world market data and experimental economics
data in order 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 price trend and valuation within a system that has finite cash and asset. This feature is distinct from classical finance in which there is the assumption of infinite arbitrage.
One of the predictions of this theory by Caginalp and Balenovich (1999) was that a larger supply of cash per share would result in a larger bubble. Experiments by Caginalp, Porter and Smith (1998) confirmed that doubling the level of cash, for example, while maintaining constant number of shares essentially doubles the magnitude of the bubble.
Using the differential equations to predict experimental markets as they evolved also proved successful, as the equations were approximately as accurate as human forecasters who had been selected as the best traders of previous experiments (Caginalp, Porter and Smith).
The challenge of using these ideas to forecast price dynamics in financial markets has been the focus of some of the recent work that has merged two different mathematical methods. The differential equations can be used in conjunction with statistical methods to provide short term forecasts.
One of the difficulties in understanding the dynamics of financial markets has been the presence of “noise” (Fischer Black
). Random world events are always making changes in valuations that are difficult to extract from any deterministic forces that may be present. Consequently, many statistical studies have only shown a negligible non-random component. For example, Poterba
and Summers
demonstrate a tiny trend effect in stock prices. White showed that using neural networks with 500 days of IBM stock was unsuccessful in terms of short term forecasts.
In both of these examples, the level of “noise” or changes in valuation apparently exceeds any possible behavioral effects. A methodology that avoids this pitfall has been developed during the past decade. If one can subtract out the valuation as it varies in time, one can study the remaining behavioral effects, if any. An early study along these lines (Caginalp and Greg Consantine) studied the ratio of two clone closed-end funds. Since these funds had the same portfolio but traded independently, the ratio is independent of valuation. A statistical time series study showed that this ratio was highly non-random, and that the best predictor of tomorrow’s price is not today’s price (as suggested by EMH) but halfway between the price and the price trend.
The subject of overreactions has also been important in behavioral finance. In his 2006 PhD thesis, Duran examined 130,000 data points of daily prices for closed-end funds in terms of their deviation from the net asset value
(NAV). Funds exhibiting a large deviation from NAV were likely to behave in the opposite direction of the subsequent day. Even more interesting was the statistical observation that a large deviation in the opposite direction preceded such large deviations. These precursors may suggest that an underlying cause of these large moves—in the absence of significant change in valuation—may be due to the positioning of traders in advance of anticipated news. For example, suppose many traders are anticipating positive news and buy the stock. If the positive news does not materialize they are inclined to sell in large numbers, thereby suppressing the price significantly below the previous levels. This interpretation is inconsistent with EMH but is consistent
with asset flow differential equations that incorporate behavioral concepts with the finiteness of assets. Research continues on efforts to optimize the parameters of the asset flow equations in order to forecast near term prices.
Valuation (finance)
In finance, valuation is the process of estimating what something is worth. Items that are usually valued are a financial asset or liability. Valuations can be done on assets or on liabilities...
. Some of this endeavor has been led by Gunduz Caginalp
Gunduz Caginalp
Gunduz Caginalp is a Turkish mathematician, currently a professor at the University of Pittsburgh.He received his PhD from Cornell University in 1978...
(Professor of Mathematics and Editor of Journal of Behavioral Finance
Journal of Behavioral Finance
The Journal of Behavioral Finance is a peer-reviewed journal that publishes research related to the field of behavioral finance. It formerly published as The Journal of Psychology and Financial Markets....
during 2001–2004) and collaborators including Vernon L. Smith
Vernon L. Smith
Vernon Lomax Smith is professor of economics at Chapman University's Argyros School of Business and Economics and School of Law in Orange, California, a research scholar at George Mason University Interdisciplinary Center for Economic Science, and a Fellow of the Mercatus Center, all in Arlington,...
(2002 Nobel Laureate in Economics), David Porter, Don Balenovich, Vladimira Ilieva, Ahmet Duran). Studies by Jeff Madura, Ray Sturm and others have demonstrated significant behavioral effects in stocks and exchange traded funds.
The 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.
- Forecasting based on these methods.
- Studies of experimental asset marketsExperimental economicsExperimental 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...
and use of models to forecast experiments.
History
The prevalent theory of financial marketFinancial market
In economics, a financial market is a mechanism that allows people and entities to buy and sell financial securities , commodities , and other fungible items of value at low transaction costs and at prices that reflect supply and demand.Both general markets and...
s during the second half of the 20th century has been the efficient market hypothesis
Efficient market hypothesis
In finance, the efficient-market hypothesis asserts that financial markets are "informationally efficient". That is, one cannot consistently achieve returns in excess of average market returns on a risk-adjusted basis, given the information available at the time the investment is made.There are...
(EMH) which states that all public information is incorporated into asset prices. Any deviation from this true price is quickly exploited by informed traders who attempt to optimize their returns and it restores the true equilibrium price. For all practical purposes, then, market prices behave as though all traders were pursuing their self-interest with complete information and rationality.
Toward the end of the 20th century, this theory was challenged in several ways. First, there were a number of large market events that cast doubt on the basic assumptions. On October 19, 1987
Black Monday (1987)
In finance, Black Monday refers to Monday October 19, 1987, when stock markets around the world crashed, shedding a huge value in a very short time. The crash began in Hong Kong and spread west to Europe, hitting the United States after other markets had already declined by a significant margin...
the Dow Jones average plunged over 20% in a single day, as many smaller stocks suffered deeper losses. The large oscillations on the ensuing days provided a graph that resembled the famous crash of 1929. The crash of 1987 provided a puzzle and challenge to most economists who had believed that such volatility
Volatility (finance)
In finance, volatility is a measure for variation of price of a financial instrument over time. Historic volatility is derived from time series of past market prices...
should not exist in an age when information and capital flows are much more efficient than they were in the 1920s.
As the decade continued, the Japanese market soared to heights that were far from any realistic assessment of the valuations. Price-earnings ratios soared to triple digits, as Nippon Telephone and Telegraph achieved a market valuation (stock market price times the number of shares) that exceeded the entire market capitalization of West Germany. In early 1990 the Nikkei index
Nikkei 225
The , more commonly called the Nikkei, the Nikkei index, or the Nikkei Stock Average , is a stock market index for the Tokyo Stock Exchange . It has been calculated daily by the Nihon Keizai Shimbun newspaper since 1950. It is a price-weighted average , and the components are reviewed once a year...
stood at 40,000, having nearly doubled in two years. In less than a year the Nikkei dropped to nearly half its peak.
Meanwhile, in the US the growth of new technology, particularly the internet, spawned a new generation of high tech companies, some of which became publicly traded long before any profits. As in the Japanese stock market bubble
Stock market bubble
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....
a decade earlier these stocks soared to market valuations of billions of dollars sometimes before they even had revenue. The bubble continued into 2000 and the consequent bust reduced many of these stocks to a few percent of their prior market value. Even some large and profitable tech companies lost 80% of their value during the period 2000-2003.
These large bubbles and crashes in the absence of significant changes in valuation cast doubt on the assumption of efficient markets that incorporate all public information accurately. In his book, “Irrational Exuberance”
Irrational Exuberance (book)
Irrational Exuberance is a March 2000 book written by Yale University professor Robert Shiller, named after Alan Greenspan's "irrational exuberance" quote. Published at the height of the dot-com boom, it put forth several arguments demonstrating how the stock markets were overvalued at the time...
, Robert Shiller
Robert Shiller
Robert James "Bob" Shiller is an American economist, academic, and best-selling author. He currently serves as the Arthur M. Okun Professor of Economics at Yale University and is a Fellow at the Yale International Center for Finance, Yale School of Management...
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 (e.g., Jeffrey Pontiff ), of closed-end fund
Closed-end fund
A closed-end fund is a collective investment scheme with a limited number of shares. It is called a closed-end fund because new shares are rarely issued once the fund has launched, and because shares are not normally redeemable for cash or securities until the fund liquidates.Typically an...
s which trade like stocks, but have a precise valuation that is reported frequently. (See Seth Anderson and Jeffrey Born “Closed-end Fund Pricing” for review of papers relating to these
issues.)
In addition to these world developments, other challenges to classical economics and EMH came from the new field of experimental economics
Experimental economics
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...
pioneered by Vernon L. Smith
Vernon L. Smith
Vernon Lomax Smith is professor of economics at Chapman University's Argyros School of Business and Economics and School of Law in Orange, California, a research scholar at George Mason University Interdisciplinary Center for Economic Science, and a Fellow of the Mercatus Center, all in Arlington,...
who won the 2002 Nobel Prize in Economics. These experiments (in collaboration with Gerry Suchanek, Arlington Williams and David Porter and others) featuring participants trading an asset defined by the experimenters on a network of computers. A series of experiments involved a single asset which pays a fixed dividend during each of 15 periods and then becomes worthless. Contrary to the expectations of classical economics, trading prices often soar to levels much higher than the expected payout. Similarly, other experiments showed that many of the expected results of classical economics and game theory are not borne out in experiments. A key part of these experiments is that participants earn real money as a consequence of their trading decisions, so that the experiment is an actual market rather than a survey of opinion.
Behavioral finance
Behavioral finance
Behavioral economics and its related area of study, behavioral finance, use social, cognitive and emotional factors in understanding the economic decisions of individuals and institutions performing economic functions, including consumers, borrowers and investors, and their effects on market...
(BF) is a field that has grown during the past two decades in part as a reaction to the phenomena described above. Using a variety of methods researchers have documented systematic biases (e.g., underreaction, overreaction, etc.) that occur among professional investors as well as novices. Behavioral finance researchers generally do not subscribe to EMH as a consequence of these biases. However, EMH theorists counter that while EMH makes a precise prediction about a market based upon the data, BF usually does not go beyond saying that EMH is wrong.
Research in Quantitative Behavioral Finance
The attempt to quantify basic biases and to use them in mathematical models is the subject of Quantitative Behavioral Finance. Caginalp
Gunduz Caginalp
Gunduz Caginalp is a Turkish mathematician, currently a professor at the University of Pittsburgh.He received his PhD from Cornell University in 1978...
and collaborators have used both statistical and mathematical methods on both the world market data and experimental economics
Experimental economics
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...
data in order 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 price trend and valuation within a system that has finite cash and asset. This feature is distinct from classical finance in which there is the assumption of infinite arbitrage.
One of the predictions of this theory by Caginalp and Balenovich (1999) was that a larger supply of cash per share would result in a larger bubble. Experiments by Caginalp, Porter and Smith (1998) confirmed that doubling the level of cash, for example, while maintaining constant number of shares essentially doubles the magnitude of the bubble.
Using the differential equations to predict experimental markets as they evolved also proved successful, as the equations were approximately as accurate as human forecasters who had been selected as the best traders of previous experiments (Caginalp, Porter and Smith).
The challenge of using these ideas to forecast price dynamics in financial markets has been the focus of some of the recent work that has merged two different mathematical methods. The differential equations can be used in conjunction with statistical methods to provide short term forecasts.
One of the difficulties in understanding the dynamics of financial markets has been the presence of “noise” (Fischer Black
Fischer Black
Fischer Sheffey Black was an American economist, best known as one of the authors of the famous Black–Scholes equation.-Background:...
). Random world events are always making changes in valuations that are difficult to extract from any deterministic forces that may be present. Consequently, many statistical studies have only shown a negligible non-random component. For example, Poterba
James M. Poterba
James Michael "Jim" Poterba is an American economist, Mitsui Professor of Economics at the Massachusetts Institute of Technology, and current NBER president and chief executive officer.- Early years :...
and Summers
Lawrence Summers
Lawrence Henry Summers is an American economist. He served as the 71st United States Secretary of the Treasury from 1999 to 2001 under President Bill Clinton. He was Director of the White House United States National Economic Council for President Barack Obama until November 2010.Summers is the...
demonstrate a tiny trend effect in stock prices. White showed that using neural networks with 500 days of IBM stock was unsuccessful in terms of short term forecasts.
In both of these examples, the level of “noise” or changes in valuation apparently exceeds any possible behavioral effects. A methodology that avoids this pitfall has been developed during the past decade. If one can subtract out the valuation as it varies in time, one can study the remaining behavioral effects, if any. An early study along these lines (Caginalp and Greg Consantine) studied the ratio of two clone closed-end funds. Since these funds had the same portfolio but traded independently, the ratio is independent of valuation. A statistical time series study showed that this ratio was highly non-random, and that the best predictor of tomorrow’s price is not today’s price (as suggested by EMH) but halfway between the price and the price trend.
The subject of overreactions has also been important in behavioral finance. In his 2006 PhD thesis, Duran examined 130,000 data points of daily prices for closed-end funds in terms of their deviation from the net asset value
Net asset value
Net asset value is a term used to describe the value of an entity's assets less the value of its liabilities. The term is most commonly used in relation to open-ended or mutual funds because shares of such funds registered with the U.S. Securities and Exchange Commission are redeemed at their net...
(NAV). Funds exhibiting a large deviation from NAV were likely to behave in the opposite direction of the subsequent day. Even more interesting was the statistical observation that a large deviation in the opposite direction preceded such large deviations. These precursors may suggest that an underlying cause of these large moves—in the absence of significant change in valuation—may be due to the positioning of traders in advance of anticipated news. For example, suppose many traders are anticipating positive news and buy the stock. If the positive news does not materialize they are inclined to sell in large numbers, thereby suppressing the price significantly below the previous levels. This interpretation is inconsistent with EMH but is consistent
with asset flow differential equations that incorporate behavioral concepts with the finiteness of assets. Research continues on efforts to optimize the parameters of the asset flow equations in order to forecast near term prices.