A szerző a 2008-ban kezdődött gazdasági világválság hatását vizsgálja az egy részvényre jutó nyereség előrejelzésének hibájára. Számos publikáció bizonyította, hogy az elemzők a tényértékeknél szisztematikusan kedvezőbb tervértéket adnak meg az egy részvényre...

We study the evolution of the behavioral component of the financial market by estimating a Bayesian mixture model in which two types of investors coexist: one rational, with standard subjective expected utility theory (SEUT) preferences, and one behavioral, endowed with an S-shaped utility...

We provide empirical support for the conventional wisdom that there are times when optimistic investors tend to build their hopes into castles in the air, and pay a large premium over intrinsic value for stocks of firms in the early stages of their life cycles with perceived growth...

We investigate the price dynamics of large market-capitalization U.S. equity exchange-traded funds (ETFs) in order to uncover trader motivations and strategy. We show that prices of highly liquid ETFs can deviate significantly from their daily net asset values. By adjusting for changes in...

Unlike previous studies which have examined the role of financial analysts in developed economies, the aim of this paper is to investigate whether following the Tunisian stock market opening, both the analyst forecast accuracy and the market's reliance on analyst forecasts, increase with time....

We present a novel approach towards the financial Ising model. Most studies utilize the model to find settings which generate returns closely mimicking the financial stylized fact such as fat tails, volatility clustering and persistence, and others. We tackle the model utility from the other...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed...

The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed...

We show how distributions can be reduced to low-dimensional scenario trees. Applied to intertemporal distributions, the scenarios and their probabilities become time-varying factors. From S&P 500 options, two or three time-varying scenarios suffice to forecast returns, implied variance or...