Financial modeling and forecasting is important for current and future sales for firms and the financial actors within the firm. It doesn't only tell you where you need to go but also reminds you of where you have been. It tells you the past, current, and future numbers. With these numbers a...

Accurately forecasting volatility is key in many financial applications. In this study, I suggest that individuals gather information online before implementing their trading decisions. In periods of higher investor concern, online information seeking intensifies. By analysing Google search data...

An examination of the Shiller cyclically adjusted pricing-earnings (CAPE) ratio reveals its forecasting power for 12-month CRSP equally weighted (EW) excess returns and value weighted (VW) excess returns. The 12-month EW excess returns following low CAPE ratios are, on average, 20.7% higher than...

We perform a comprehensive examination of the recursive, comparative predictive performance of a number of linear and non-linear models for UK stock and bond returns. We estimate Markov switching, threshold autoregressive (TAR), and smooth transition autoregressive (STR) regime switching models,...

We propose a new approach to imposing economic constraints on forecasts of the equity premium. Economic constraints are used to modify the posterior distribution of the parameters of the predictive return regression in a way that better allows the model to learn from the data. We consider two...

I consider a consumption based asset pricing model where the consumer does not know if shocks to dividends are stationary (temporary) or non-stationary (permanent). The agent uses a Bayesian learning algorithm with a bias towards recent observations to assign probability to each process. While...

Using the long-term wavelet component of monthly S&P 500 excess returns as supervision information, we employ a machine learning method to extract the common predictive information of 14 prevalent macroeconomic variables, and construct a new macroeconomic index aligned for predicting stock...

It is assumed in the news-based model (NBM) that stock prices are determined with macroeconomic news (modeled with the total market return in the spirit of CAPM), industry news (modeled with the relevant industry ETF returns), and the company-specific news and momentum that are described using...

The volatility specification of the Markov-switching Multifractal (MSM) model is proposed as an alternative mechanism for realized volatility (RV). We estimate the RV-MSM model via Generalized Method of Moments and perform forecasting by means of best linear forecasts derived via the...

We study the well-known multiplicative Lognormal cascade process in which the multiplication of Gaussian and Lognormally distributed random variables yields time series with intermittent bursts of activity. Due to the non-stationarity of this process and the combinatorial nature of such a...