Corporate investment decisions require managers to forecast expected future cash flows from potential investments. Although these forecasts are a critical component of successful investing, they are not directly observable by external stakeholders. In this study, we investigate whether the...

The use of fundamentalist traders in the stock market models is problematic since fundamental values in the real world are unknown. Yet, in the literature to date, fundamentalists are often required to replicate key stylized facts. The authors present an agent-based model of the stock market in...

A novel dynamic asset-allocation approach is proposed where portfolios as well as portfolio strategies are updated at every decision period based on their past performance. For modeling, a general class of models is specified that combines a dynamic factor and a vector autoregressive model and...

We survey the textual sentiment literature, comparing and contrasting the various information sources, content analysis methods, and empirical models that have been used to date. We summarize the important and influential findings about how textual sentiment impacts on individual, firm-level and...

This paper reveals that in addition to fundamental factors, the 52-week high price and recent investor sentiment play an important role in analysts' target price formation. Analysts' forecasts of short-term earnings and long-term earnings growth are shown to be important explanatory variables...

There is a logical bound on the time-series variability of analyst forecasts; when variability exceeds this bound it must be caused by something besides statistically rational forecasting. We document occurrences of excessively volatile analyst forecasts and show that they influence investment...

In this article, the problem of constructing models of multi-section conveyor-type transport systems using a neural network is considered. The analysis of models of long-distance multi-section transport systems, which are used to design control systems of the flow parameters from the point of...

The multi-section transport conveyor model based on the neural network for predicting the output flow parameters is considered. The expediency of using sequential and batch modes of training of a neural network in a model of a multi-section transport conveyor has been investigated. The quality...

In this article, the Universal Approximation Theorem of Artificial Neural Networks (ANNs) is applied to the SABR stochastic volatility model in order to construct highly efficient representations. Initially, the SABR approximation of Hagan et al. [2002] is considered, then a more accurate...