اثرات اطلاعاتی بر کشف قیمت و کارایی بازار

The influence of speculation on market performance has long been discussed. Under the framework of bounded rationality in which traders are endowed with different intelligence levels in terms of different learning styles or different representations of intelligence, we examine the effects of traders’ intelligence on price discovery based on “intraday” data, and market efficiency. We find that intelligence does help improve market performance. However, the influence of different intelligence levels on the market crucially depends on the characteristics of learning styles or the representation of intelligence.

What is speculation? As mentioned in Kaldor (1939, p. 1): Speculation, for the purpose of this article, may be defined as the purchase (or sale) of goods with a view to re-sale (re-purchase) at a later date, where the motive behind such action is the expectation of a change in the relevant prices relatively to the ruling price and not a gain accruing through their use, or any kind of transformation effected in them or their transfer between different markets. The impacts of speculation on the market have bothered economists for a long time, and in general, a consensus in terms of opinion has so far not been reached. Some people may argue that speculation has a negative effect on the economy as a whole because it introduces uncertainty into the market. By contrast, many economists have shown that speculation is a source of stability because speculators can correctly predict the underlying price dynamics so as to stabilize the price fluctuations. The main reason behind the controversy is that the essence of speculation as well as its conduits of influence in the economy are not well understood. In addition, different characteristics of traders in the various environments may cause speculation to exhibit quite different features. Basically, in the literature, three different aspects of the effects of speculation are examined. One of them focuses on the impacts of rational speculation in an environment populated by rational speculators and non-speculators. The conditions for supporting the stabilizing or destabilizing feature of rational speculation are provided. Another related area of research emphasizes the importance of irrational speculation. Traders’ irrational behavior has been shown to generate substantial influences that rational traders are unable to eliminate, perhaps further destabilizing the market. The third area is concerned with the role of market structure. Under different market environments, intelligence could be irrelevant to or else give rise to significant effects on the market phenomena. We argue that the effects of speculation crucially depend on the characteristics of learning styles (i.e., the representation of intelligence). Traders with different learning methods may constitute different characteristics of speculation that result in different impacts on the market. In this paper, different levels of traders’ intelligence are modeled by means of zero intelligence (ZI), the simple adaptive learning method, and the genetic programming (GP) learning algorithm. The differences between these modelings consist of the learning styles, or the complexity or representations of the GP functions that traders can use to form their beliefs. Therefore, our framework will serve to enrich our understanding of how the representation of intelligence as well as its consequent characteristics in the microstructure exert their strength on the market properties. In Section 2, the literature regarding speculation and intelligence are briefly discussed. The methodology employed in this paper is described in Section 3. Section 4 presents the framework of the artificial stock market. Sections 5 and 6 summarize the experimental design and the simulation results, respectively, and Section 7 concludes.

To understand the influence of speculation on market performance, we should take into consideration the traders’ rationality. Under the framework of bounded rationality, traders learn from their experiences and adapt to the changing environment according to incomplete or imperfect information. Therefore, the functions and the influence of speculation crucially depend on the degree of traders’ intelligence. In this paper, different levels of traders’ intelligence are presented. We consider the market in which traders’ intelligence is characterized by zero intelligence, the simple adaptive learning method, or the GP learning algorithm. The differences between these modelings consist of the learning styles, or the complexity or representations of GP functions that traders can use to form their beliefs. The effects of traders’ intelligence are analyzed based on the intraday information and market efficiency. Our simulation results indicate that the introduction of intelligence does improve price discovery (in terms of the absolute difference between the opening price and the closing price, the absolute difference between the highest price and the lowest price, the number of rounds for the emergence of price convergence, or the price spreads), and market efficiency. Besides the spreads, the positive side of intelligence also manifests itself in the comparison between the first-half and the second-half of the run. Actually, our results confirm the claim made in Cliff and Bruten: zero intelligence is not enough. The efficiency tests lead to different results from those obtained by Chen and Tai (2003) who found that intelligence harms efficiency. However, the smarter traders in Chen and Tai are presented in such a way that the budget constraint is not imposed, so that traders have more room for strategic planning. In this situation, it is reasonable to derive a lower level of allocative efficiency. Therefore, intelligence is not the reason for the phenomenon observed in Chen and Tai. More importantly, we find that the effects of speculation under different intelligence levels cannot be examined without taking into consideration the representation of intelligence as well as its consequent characteristics. Different representations of intelligence or learning methods may constitute different characteristics of speculation that will give rise to dramatically different impacts on the market. We show that such effects can be investigated and realized via their impacts on traders’ heterogeneity in terms of different reservation prices. Besides, the influence of different representations of intelligence is exhibited not only in the aggregate market properties but also in the different time horizons. Intelligence represented by the simple adaptive learning method would present quite different features from those represented by the more aggressive learning method such as the GP learning algorithm. Therefore, our results suggest that interpreting the effects of speculation should be conducted very prudently and that intelligence plays a crucial rather than a secondary role in the influence of market performance. This is quite different from what Gode and Sunder (1993, p. 24) mentioned: Allocative efficiency of a double auction derives largely from its structure, independent of traders’ motivation, intelligence, or learning. Of course, this study is conducted on the basis of a market where traders are characterized by the same learning style or representation of intelligence. Our next step will be to consider a more general framework in which the traders with different intelligence levels coexist and compete with each other. Such an environment may help us to understand better the aggregate impacts of speculation in which traders with different intelligence levels possess different proportions.