مقاله انگلیسی رایگان در مورد امواج ورودی بازار و ظهور نوسانات در بازار سهام – الزویر 2018

We develop a simple agent-based financial market model in which speculators’ market entry decisions are subject to herding behavior and market risk. In addition, speculators’ orders depend on price trends, market misalignments and fundamental news. Using a mix of analytical and numerical tools, we show that a herding-induced market entry wave may amplify excess demand, triggering lasting volatility outbursts. Eventually, however, higher stock market risk reduces stock market participation and volatility decreases again. Simulations furthermore reveal that our approach is also able to produce bubbles and crashes, excess volatility, fat-tailed return distributions and serially uncorrelated price changes. Moreover, trading volume is persistent and correlated with volatility.

Introduction

The goal of our paper is to develop a simple agent-based financial market model to explain a number of important stylized facts of stock markets. In particular, we analytically and numerically demonstrate that speculators’ market entry and exit behavior may give rise to volatility clustering. Our model’s key features and its main implications may be summarized as follows. We assume that there is a market maker who adjusts stock prices with respect to speculators’ orders, which, in turn, use technical and fundamental trading rules to determine their trading behavior. Speculators’ market entry decisions depend on two socio-economic principles. First, speculators are subject to herding behavior and increasingly enter the stock market as the number of active speculators increases. Second, speculators react to stock market risk. The higher the past volatility of the stock market, the lower the probability that a speculator will enter the stock market. As it turns out, the stock market is relatively stable if the number of active speculators is low. Since stock market risk is then perceived as negligible, more and more speculators become active. Consequently, excess demand increases, the market maker adjusts stock prices more strongly and volatility picks up. Due to the increase in stock market risk, stock market participation eventually decreases again. Confronted with a lower excess demand, the market maker needs to adjust stock prices less strongly. We also show that the repeated inflow and outflow of speculators along with their heterogeneous trading behavior may produce bubbles and crashes, excess volatility, serially uncorrelated returns and a fat-tailed return distribution. Moreover, trading volume displays significant memory effects and is strongly correlated with volatility. Keeping track of the individual speculators’ wealth dynamics reveals that heterogeneity among speculators may be a persistent phenomenon of financial markets, i.e. neither do a few speculators accumulate all the wealth and dominate the market nor does a substantial fraction of speculators go bankrupt and vanish from the market. Our paper adds to the burgeoning stream of literature on agent-based financial market models (see Chiarella et al., 2009a; Hommes and Wagener, 2009; Lux, 2009 for surveys). Within these models, speculators apply technical and fundamental trading rules to determine their orders. Technical trading rules (Murphy, 1999) are usually based on trend extrapolation and tend to destabilize the dynamics of financial markets. In contrast, fundamental trading rules (Graham and Dodd, 1951) bet on mean reversion, exercising a stabilizing impact on the dynamics of financial markets. Models by Day and Huang (1990), De Grauwe et al. (1993), Brock and Hommes (1998), LeBaron et al. (1999), Farmer and Joshi (2002), Chiarella et al. (2007), Franke and Westerhoff (2012) and Jacob Leal and Napoletano (2017), for example, show that (non-linear) interactions between speculators relying on technical and fundamental trading rules can produce dynamics which resembles the dynamics of actual financial markets quite closely. Without question, this line of research helps us to improve our understanding of the functioning of financial markets. For instance, agent-based financial market models reveal that a bubble may emerge if speculators forcefully rely on technical analysis while a crash can be set in motion if speculators put more weight on fundamental analysis. Such a time-varying impact of technical and fundamental trading rules can also produce volatility clustering. Financial markets tend to be relatively stable when speculators prefer fundamental analysis but turn wilder when speculators opt for technical analysis.