آیا نوسانات مهم است؟ انتظارات بازگشت قیمت و تنوع در یک آزمایش قیمت گذاری دارایی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|1849||2011||23 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 15167 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Economic Behavior & Organization, Volume 77, Issue 2, February 2011, Pages 124–146
We present results of an experiment on expectation formation in an asset market. Participants in our experiment must provide forecasts of the stock future return to computerized utility-maximizing investors, and are rewarded according to how well their forecasts perform in the market. In the Baseline treatment participants must forecast the stock return one period ahead; in the volatility treatment, we also elicit subjective confidence intervals of forecasts, which we take as a measure of perceived volatility. The realized asset price is derived from a Walrasian market equilibrium equation with non-linear feedback from individual forecasts. Our experimental markets exhibit high volatility, fat tails and other properties typical of real financial data. Eliciting confidence intervals for predictions has the effect of reducing price fluctuations and increasing subjects’ coordination on a common prediction strategy.
Understanding investors’ expectations is crucial to model and predict the behavior of financial markets. Stock exchange professionals try to anticipate modifications in investor sentiment that are likely to impact future price trends, and investors’ beliefs are also central to economic theories of asset markets. Various researchers have tried to model investors’ expectations by observing data obtained from ‘real’ markets (e.g., Goetzmann and Massa, 2000 and Grinblatt and Keloharju, 2001). However, the main problem with this method of studying beliefs is that expectations in the field are not directly observable, but can only be inferred (with varying degrees of uncertainty) from measurable variables such as price trends and trading volume. For this reason, other more direct means of collecting belief data have been attempted: among these are the use of surveys as in Turnovsky (1970), Frankel and Froot (1987), and Shiller (1990), and the design of controlled laboratory experiments. The last method is probably the most accurate to observe the dynamics of expectations, given the total control that the experimenter has over the parameters of the financial ‘environment’ in which investors operate. While the main focus of early experiments on asset markets was the impact of trading on deviation of prices from an asset fundamental value (with data on beliefs often collected as a “side-product”), more recent experiments focus on expectation formation in isolation from trading, or where no trading takes place (see, e.g., Hommes et al., 2005, Hommes et al., 2008, Hey, 1994, Marimon and Sunder, 1993 and Sonnemans et al., 2004). In these experiments subjects are usually asked to forecast future prices, either one period ahead, or several periods ahead. In some cases, the time series of prices is exogenously given, while in others it is endogenously generated by the participants’ forecasting activity according to an expectations feedback mechanism. In the latter case, the relation between expectations and prices is usually a linear function, which makes both prediction of future prices and coordination of prediction strategies relatively easy. In our experiment, we create an asset market with positive feedback from individual forecasts. The design is essentially adapted from Hommes et al. (2005): subjects are asked to forecast future returns of an asset, and these forecasts are used by artificial traders to buy or sell (optimal) amounts of the asset in every round. The pricing mechanism is obtained by assuming Constant Absolute Risk Aversion (CARA) behavior on the part of myopic (i.e., acting with one step time horizon) artificial speculators. Unlike previous experiments, we introduce a non-linear, positive feedback mechanism between forecasts and prices. In addition, in one of our treatments, we ask subjects to provide a confidence interval for their prediction, which we take as a measure of the perceived volatility of the corresponding return (and hence of the perceived risk of the investment). We use both forecasted return and forecasted volatility at time t + 1 (we derive the latter from the confidence interval) to compute the price at time t; therefore subjects’ forecasts on returns and confidence intervals have a direct impact on the price level. Forecasted volatility influences the sensitivity of market prices to expectations: a reduction in forecasted volatility increases such sensitivity, while an increase smooths out the relation between expectations and prevailing prices. As a first step, we intend to investigate whether the presence of a non-linear feedback mechanism between forecasts and prices produces aggregate properties similar to those observed in real financial markets (in terms of, e.g., excess volatility and volatility clustering, fat tails, formation of bubbles and crashes) and a level of coordination in the prediction strategies of participants comparable to that observed in previous experiments that have employed linear expectations feedback functions (Hommes et al., 2005 and Hommes et al., 2008) We introduce a non-linear feedback system, derived from the utility maximizing behavior of our artificial traders, that more closely resembles the complexity of real financial markets. We then study the resulting aggregate properties of our experimental markets, focusing on both aggregate dynamics and interactions between these and the dynamics of individual expectations. Secondly, we want to investigate if and how the elicitation of forecasts on volatility, in the form of a confidence interval, has an impact on the dynamics of prices and returns. Despite the obvious importance of perceived volatility (and hence perception of risk) for investment decisions in financial markets, to the best of our knowledge, no experiment so far has tested the role of volatility forecasts, alongside price forecasts, in influencing the dynamics of prices in a financial markets setting. The remainder of the paper is organized as follows: in Section 2 we review the relevant literature; in Section 3 we describe the asset pricing model together with the experimental design and implementation. Section 4 reports results on aggregate market behavior while Section 5 discusses results on individual behavior. Section 5.2 discusses in detail data on predicted volatility. Finally, Section 6 offers come concluding remarks.
نتیجه گیری انگلیسی
In this paper we analyze an experimental market where participants are requested to predict future returns of a security whose price is determined using subjects’ forecasts as inputs. In one treatment, the baseline treatment, the point prediction about the future return is the only variable subjects are asked to provide. In the volatility treatment subjects have to provide a confidence interval associated with their forecast. In both treatments we replicate some of the well-known stylized facts about returns in asset markets, i.e., excess volatility, skewness and fat tails. Introducing the requirement to provide a confidence interval generates dynamics which are significantly different from the previous treatment, being on average less volatile. Moreover we observe cases where price dynamics reach stable equilibria at a level of the market price equal to the fundamental value. With regard to individual behavior, we do not observe the same degree of coordination in prediction strategies that was observed in similar experimental asset markets in which a linear expectations feedback was employed. Furthermore, we again observe a difference between treatments: a higher degree of coordination of predictions appears in the volatility treatment with respect to the baseline treatment, and higher coordination is generally observed in more stable markets. By analyzing the structure of subjects’ confidence intervals in the volatility treatment, we can make further statements concerning the beliefs structure of our subjects. Unstable markets are characterized by less confident participants who on average earn less: our hypothesis is that when agents experience unstable markets, they tend to ‘follow the crowd’ (i.e., past values of prices and returns) in their forecasts, thus generating unstable dynamics, but appear to be on average less confident in the accuracy of their own (extreme) predictions. Moreover, individuals in very unstable markets tend to change their confidence intervals very frequently as compared to subjects in stable markets, mirroring the market variability with changes in their own confidence degree. The similarity between patterns of confidence intervals and patterns of prices and returns suggests that, in general, subjects adapted their confidence degree to the perceived volatility of the market rather than exhibiting a degree of confidence that was independent of market realizations. This result is in line with Macdonald and Marsh (1996) and Jongen et al. (2008) who in survey-based analysis of traders’ expectations discover a positive relationship between different measures of volatility and traders heterogeneity. Jongen et al. (2008) also find Granger causality going from market volatility to the heterogeneity of participants for various heterogeneity measures and different investment horizons. In the light of these findings, we can hypothesize that, in our experiment, the nature of the expectations feedback mechanism rendered participants’ perceived volatility self-fulfilling and increased coordination difficulty in correspondence with higher realized volatility of returns as that found in more unstable markets. Our findings suggest that social welfare may be higher in stable markets: hence, if this is true, it becomes important to identify those measures that generate a reduction in market instability. For example, a mechanism to make risk more salient through the elicitation of confidence intervals has clearly induced a reduction in market volatility in our experiment. As Shefrin and Statman (1993) point out, investors are not indifferent to the frames in which the cash flows related to their portfolio investments are presented. Thus, from a policy point of view, implementing mechanisms – such as framing investment options in specific ways – that make the risk content of financial portfolios more salient could be effective in reducing aggregate market volatility and in increasing social welfare.