Nicholas S.P. Tay

Fuzzy Inductive Reasoning, Expectation Formation and the Behavior of Security Prices

This paper extends the Santa Fe Artificial Stock Market Model (SFASM) studied by LeBaron, Arthur and Palmer (1999, Journal of Economic Dynamics and Control 23, 1487-1516) in two important directions. First, some might question whether it is reasonable to assume that traders are capable of handling a large number of rules, each with numerous conditions, as is assumed in the SFASM. We demonstrate that similar results can be obtained even after severely limiting the reasoning process. We show this by allowing agents the ability to compress information into a few fuzzy notions which they can in turn process and analyze with fuzzy logic. Second, LeBaron et al. have reported that the kurtosis of their simulated stock returns is too small as compared to real data. We demonstrate that with a minor modification to how traders go about deciding which of their prediction rules to rely on when making demand decisions, the model can in fact produce return kurtosis that is comparable to that of actual returns data.

Correlations in Returns and Volatilities in Pacific-Rim Stock Markets

Most studies on the correlations in stock returns and volatilities focus on the contemporaneous relationships and spillover effects in major stock markets such as the US and Japan. This paper adds to the literature by focusing on the dynamic relationship in the volatilities of the returns in the Pacific-Rim stock markets. The causality in variances test method of Cheung and Ng (1996), a multivariate GARCH model and VAR analyses are employed to model conditional volatilities and study the dynamic responses of volatilities to innovations in conditional variances. The results suggest that while the stock markets are correlated in returns and volatilities contemporaneously and with lags, idiosyncratic factors play important roles in national stock markets. In addition, the dynamic adjustment of the market return volatilities can take a much longer time than previously reported in some of the countries studied.

Agent-Based Modeling of Ambidextrous Organizations: Virtualizing Competitive Strategy

Turbulence, uncertainty, dynamic processes, and networks increasingly characterize competitive markets and business strategies. Consequently, theres a need to model such markets and strategies as dynamic, evolutionary processes that is, as complex adaptive systems. Agent-based modeling, a rich platform for studying complex evolving systems, is used to model a market where ambidextrous and nonambidextrous organizations compete for buyers. Viewing competitive market and business processes as interactions among agents who mutually influence each other reduces economics to its most microscopic level. Social networks such as the Internet have attracted much research attention because of the rise in stock fraud on the Internet.

Some Challenges to Economic Growth and Stability in China

China has experienced an incredible rate of growth in recent decades. According to the April 2015 issue of the IMF World Economic Outlook database, China’s GDP based on current prices has grown fro...

Complexity and the Character of Stock Returns: Empirical Evidence and a Model of Asset Prices Based on Complex Investor Learning

Empirical evidence on the distributional characteristics of common stock returns indicates: (1) A power-law tail index close to three describes the behavior of the positive tail of the survivor function of returns (pr(r > x) ∼ x -α), a reflection of fat tails; (2) general linear and nonlinear dependencies exist in the time series of returns; (3) the time-series return process is characterized by short-run dependence (short memory) in both returns as well as their volatility, the latter usually characterized in the form of autoregressive conditional heteroskedasticity; and (4) the time-series return process probably does not exhibit long memory, but the squared returns process does exhibit long memory. We propose a model of complex, self-referential learning and reasoning amongst economic agents that jointly produces security returns consistent with these general observed facts and which are supported here by empirical results presented for a benchmark sample of 50 stocks traded on the New York Stock Exchange. The market we postulate is populated by traders who reason inductively while compressing information into a few fuzzy notions that they can in turn process and analyze with fuzzy logic. We analyze the implications of such behavior for the returns on risky securities within the context of an artificial stock market model. Dynamic simulation experiments of the market are conducted, from which market-clearing prices emerge, allowing us to then compute realized returns. We test the effects of varying values of the parameters of the model on the character of the simulated returns. The results indicate that the model proposed in this paper can jointly account for the presence of a power-law characterization of the positive tail of the survivor function of returns with exponent on the order of three, for autoregressive conditional heteroskedasticity, for long memory in volatility, and for general nonlinear dependencies in returns.

Social Network Characteristics and the Evolution of Investor Sentiment

This paper creates a bare bone model to understand how network characteristics such as the richness of the information environment, tendency for investors to extrapolate past data and social influence affect the transmission and evolution of investor sentiment within the network. Our results replicate qualitatively the empirical characteristics of actual investor sentiment documented by [1].

The Impact on Stock Returns of Crowding by MutualFunds

Evidence from recent financial debacles suggests that crowding can adversely impact the subsequent performance of crowded investments and destabilize financial markets. However, the term “crowding” has been used loosely in the public media. To be precise, the authors define and develop a measure of crowding that captures the interaction of correlated trades and illiquidity and use this metric to study how crowding on stocks by mutual funds affects the subsequent returns on the stocks for the period from 1981 to 2012. They find a strong negative association between the crowding measure and the quarterly returns two quarters ahead. More in-depth analysis reveals that a long–short portfolio with a long position in the least crowded stocks and a short position in the most crowded stocks can earn an annualized abnormal return as high as 14.53% after adjusting for size, book to market, and momentum characteristics. The authors further confirm that the substantial abnormal returns are not driven by time-varying expected returns. Surprisingly, the abnormal returns can mostly be attributed to the least crowded stocks, which have characteristics resembling stocks neglected by mutual funds. They demonstrate that their crowding measure is an improvement over the liquidity measure and conveys important signals beyond what is embedded in turnover.

Economic and Financial Drivers of Growth: Guest Editors' Introduction to Parts I and II

3 Nicholas S.P. Tay is a professor of finance at the University of San Francisco, San Francisco, CA; e-mail: tay@usfca.edu. Dilip K. Das is a professor of international economics and international finance and director of the Institute of International Business at the SolBridge International School of Business, Woosong University, Daejeon, Republic of Korea; e-mail: Dilip.Das@solBridge.ac.kr. Lee Yao is J.A. Butt Distinguished Professor of Accounting, Loyola University, New Orleans, LA; e-mail: yao@loyno.edu. Pisun (Tracy) Xu is an assistant professor of finance at the University of Denver, Denver, CO; e-mail: Pisun.Xu@du.edu. the Chinese Economy, vol. 45, no. 4, July–August 2012, pp. 3–6.

A Conversation with John “Jack” Wadsworth, Jr., Honorary Chairman of Morgan Stanley Asia

A Conversation with John “Jack” Wadsworth, Jr., Honorary Chairman of Morgan Stanley Asia. Wadsworth talks about Morgan Stanley’s experiences in doing business in Asia, both historically and currently, and talks about the different approaches in different regions of Asia.

Fuzzy Inductive Reasoning and Nonlinear Dependence in Security Returns: Results from an Artificial Stock Market Environment

We present an examination of security returns generated in an artificial stock market. The market is populated by traders who reason inductively while compressing information into a few fuzzy notions which they can in turn process and analyze with fuzzy logic. We demonstrate that nonlinear return dependence is present even after we have controlled for ARCH effects in the simulated data and that these results are similar to those found in an examination of the actual returns of two actively traded stocks used as case studies. We conclude that the model we present provides an explanation for nonlinearities observed in US stock returns. The appeal of the model is its close ties to evidence on how individuals actually reason.