Financial prices have been found to exhibit some universal characteristics 1±6 that resemble the scaling laws characterizing physical systems in which large numbers of units interact. This raises the question of whether scaling in ®nance emerges in a similar wayÐfrom the interactions of a large ensemble of market participants. However, such an explanation is in contradiction to the prevalent`ef®cient market hypothesis' 7 in economics, which assumes that the movements of ®nancial prices are an immediate and unbiased re¯ection of incoming news about future earning prospects. Within this hypothesis, scaling in price changes would simply re¯ect similar scaling in the`input' signals that in¯uence them. Here we describe a multi-agent model of ®nancial markets which supports the idea that scaling arises from mutual interactions of participants. Although the`news arrival process' in our model lacks both power-law scaling and any temporal dependence in volatility, we ®nd that it generates such behaviour as a result of interactions between agents.In our model, the pool of traders is divided into two groups: the ®rst group (`fundamentalists') follows the premise of the ef®cient market hypothesis in that they expect the price (p) to follow the socalled fundamental value of the asset ( p f ), which is the discounted sum of expected future earnings (for example, dividend payments). A fundamentalist trading strategy consists of buying (selling) when the actual market price is believed to be below (above) the fundamental value. The second group (called`noise traders' following established terminology in economics 8 ), however, does not believe in an immediate tendency of the price to revert to its underlying fundamental value. Instead of focusing on fundamentals, these agents attempt to identify price trends and patterns (charts), and also consider the behaviour of other traders as a source of information, which results in a tendency towards herding behaviour. Furthermore (because it is important for the resulting market operations whether a noise trader believes in a rising or declining market), we further distinguish between optimistic and pessimistic individuals in this group: optimists will buy additional units of the asset, whereas the pessimists will sell part of their actual holdings of the asset.The main building blocks of the model are movements of individuals from one group to another together with the (exogenous) changes of the fundamental value and the (endogenous) price changes resulting from the agents' market operations. A distinguishing feature of our approach as compared with other recent simulation models 9±14 is that we adopt a mass-statistical formalization inspired by statistical physics 15,16 : individuals react to certain economic forces by changing their behaviour with a certain (endogenous) probability. As a simple formalization of movements into and out of the three groups we use exponential functions, so that a switch from one group to another occurs with a certain endogenous and time-varying probabili...

A new global optimization algorithm for functions of continuous variables is presented, derived from the “Simulated Annealing” algorithm recently introduced in combinatorial optimization. The algorithm is essentially an iterative random search procedure with adaptive moves along the coordinate directions. It permits uphill moves under the control of a probabilistic criterion, thus tending to avoid the first local minima encountered. The algorithm has been tested against the Nelder and Mead simplex method and against a version of Adaptive Random Search. The test functions were Rosenbrock valleys and multiminima functions in 2,4, and 10 dimensions. The new method proved to be more reliable than the others, being always able to find the optimum, or at least a point very close to it. It is quite costly in term of function evaluations, but its cost can be predicted in advance, depending only slightly on the starting point.

The finding of clustered volatility and ARCH effects is ubiquitous in financial data. This paper presents a possible explanation for this phenomenon within a multi-agent framework of speculative activity. In the model, both chartist and fundamentalist strategies are considered with agents switching between both behavioural variants according to observed differences in pay-offs. Price changes are brought about by a market maker reacting to imbalances between demand and supply. Most of the time, a stable and efficient market results. However, its usual tranquil performance is interspersed by sudden transient phases of destabilisation. An outbreak of volatility occurs if the fraction of agents using chartist techniques surpasses a certain threshold value, but such phases are quickly brought to an end by stabilising tendencies. Formally, this pattern can be understood as an example of a new type of dynamic behaviour known as "on-off intermittency" in physics literature. Statistical analysis of simulated time series shows that the main stylised facts (unit roots in levels together with heteroscedasticity and leptokurtosis of returns) can be found in this "artificial" market.

Abstract-Human Affectiveness, i.e., the emotional state of a person, plays a crucial role in many domains where it can make or break a team's ability to produce successful products. Software development is a collaborative activity as well, yet there is little information on how affectiveness impacts software productivity. As a first measure of this impact, this paper analyzes the relation between sentiment, emotions and politeness of developers in more than 560K Jira comments with the time to fix a Jira issue. We found that the happier developers are (expressing emotions such as JOY and LOVE in their comments), the shorter the issue fixing time is likely to be. In contrast, negative emotions such as SADNESS, are linked with longer issue fixing time. Politeness plays a more complex role and we empirically analyze its impact on developers' productivity.

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