Learning from experience in the stock market

New evidence suggests that individuals “learn from experience,” meaning they learn from events occurring during their lives as opposed to the entire history of events. Moreover, they weigh more heavily recent events compared to events occurring in the distant past. This paper analyzes the implications of such learning for stock pricing in a model with finitely lived agents. Individuals learn about the rate of change of the stock price and of dividends using a weighted decreasing-gain algorithm. As a result of waves of optimism and pessimism, the stock price exhibits stochastic fluctuations around the rational expectations equilibrium. Conditional on the historical path of dividends, the model produces a price–dividend ratio which is in line with the evidence for the last century, except for the “dot-com” bubble in the 1990s.     

Asset price dynamics with heterogeneous beliefs and local network interactions

In this paper we investigate the effects of network topologies on asset price dynamics. We introduce network communications into a simple asset pricing model with heterogeneous beliefs. The agents may switch between several belief types according to their performance. The performance information is available to the agents only locally through their own experience and the experience of other agents directly connected to them. We model the communications with four commonly considered network topologies: a fully connected network, a regular lattice, a small world, and a random graph. The results show that the network topologies influence asset price dynamics in terms of the regions of stability, amplitudes of fluctuations and statistical properties.     

A conditionally heteroskedastic independent factor model with an application to financial stock returns

We propose a new conditionally heteroskedastic factor model, the GICA-GARCH model, which combines independent component analysis (ICA) and multivariate GARCH (MGARCH) models. This model assumes that the data are generated by a set of underlying independent components (ICs) that capture the co-movements among the observations, which are assumed to be conditionally heteroskedastic. The GICA-GARCH model separates the estimation of the ICs from their fitting with a univariate ARMA-GARCH model. Here, we will use two ICA approaches to find the ICs: the first estimates the components, maximizing their non-Gaussianity, while the second exploits the temporal structure of the data. After estimating and identifying the common ICs, we fit a univariate GARCH model to each of them in order to estimate their univariate conditional variances. The GICA-GARCH model then provides a new framework for modelling the multivariate conditional heteroskedasticity in which we can explain and forecast the conditional covariances of the observations by modelling the univariate conditional variances of a few common ICs. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. Finally, we present an empirical application to the Madrid stock market, where we evaluate the forecasting performances of the GICA-GARCH and two additional factor GARCH models: the orthogonal GARCH and the conditionally uncorrelated components GARCH.     

A scenario-based integrated approach for modeling carbon price risk

Carbon prices are highly dependent on government emission policies and local industrial compositions. When historical data does not exist or limited price data can only be sourced from another country, scenario analysis becomes the only tool for the modelling of future carbon prices. However, various plausible but equally possible scenarios can produce large variations in forecast carbon prices. In a traditional approach of scenario analysis, investment decisions or risk management strategies are proposed and analysed for each given scenario, optimal solutions are determined. However, when the number of scenarios becomes large, it often becomes too complex and intractable to have a clear view on the selection of investment decisions or risk-management strategies because these decisions and strategies are closely linked with each of the many scenarios. In this paper, it is proposed to use a stochastic mean-reversion model to represent future carbon price movements, but this model is calibrated to the forecast carbon prices of all the scenarios. In this approach, a single model is used to capture the underlying uncertainty and expectation of the stochastic carbon prices as projected by all the scenarios, carbon price risk can thus be modeled and analysed without the need for direct references to any specific scenarios. The modelling and management of long-term carbon-price risk are therefore purely dependent on future carbon price levels and volatilities of these scenarios, instead of on the scenarios themselves. Through such an approach, the optimization of investment decisions and risk management solutions can be much simpler because the forecasted carbon prices are the only input data.      

Compensation for illiquidity in China: Evidence from an alternative measure

This paper shows that liquidity is an important source of priced risk in China. Using A-share stocks in Shanghai and Shenzhen Exchange over the period 2007–2017, we examine the influence of liquidity on stock returns. A new liquidity measure that captures multiple dimensions of liquidity is proposed. Fama-Macbeth cross-sectional regression shows that the expected return is negatively correlated with liquidity. Based on Fama and French (1993), we propose a five-factor pricing model by incorporating reversal factor and liquidity factor. Time-series regressions show that the liquidity factor makes significantly marginal contributions to explaining excess stock returns. The liquidity factor based on the proposed measure works better than alternative liquidity measures such as turnover, Amihud illiquidity measure and the measure in Liu (2006).     

A calibration method with dynamic updates for within-match forecasting of wins in tennis

In-match predictions of player win probabilities for professional tennis matches have a wide range of potential applications, including betting, fan engagement, and performance evaluation. The ideal properties of an in-play prediction method include the ability to incorporate both useful pre-match information and relevant in-match information as the match progresses, in order to update the pre-match expectations. This paper presents an in-play forecasting method that achieves both of these goals by combining a pre-match calibration method with a dynamic empirical Bayes updating rule. We present an optimisation rule for guiding the specifications of the dynamic updates using a large sample of professional tennis matches. We apply the results to data from the 2017 season and show that the dynamic model provides a 28% reduction in the error of in-match serve predictions and improves the win prediction accuracy by four percentage points relative to a constant ability model. The method is applied to two Australian Open men’s matches, and we derive several corollary statistics to highlight key dynamics in the win probabilities during a match.     

A new business model for sustainable development: an exploratory study using the theory of constraints in Nordic organizations

The business model in use by many large companies has changed significantly from that of a decade ago and has incorporated environmental and social aspects of performance. However, given these achievements, are there unavoidable inhibitions in the contemporary business model that mean that even exemplar corporations cannot become sustainable? A key issue is consumption without limits, but can businesses do anything about this? The UK Government's Sustainable Development Commission identifies this as an issue. There is a need for an open‐minded consideration of business fundamentals to consider this issue as part of an identification of criteria for a sustainable business model. This is an account of an exploratory study undertaken to identify a new business model for sustainable development. The theory of constraints was adapted to provide the project's methodology that made use of semi‐structured interviews and secondary material. The cloverleaf account of sustainable development was used to structure and analyse sustainable development information. The organizations studied are all located in Nordic countries, since these countries are globally recognized for sustainable development achievements. Conclusions of the study acknowledge that, whilst specific new management tools and approaches of Nordic organizations do help sustainable development, it is the social context in which these organizations function that is a critical factor. Copyright © 2007 John Wiley & Sons, Ltd and ERP Environment.