Volatile and rising agricultural prices put significant strain on the global fight against poverty. An accurate reading of future food price movements can be an invaluable budgetary planning tool for various government agencies and food aid programs. Using the asset-pricing approach developed in...

During the period leading up to the global financial crisis many asset classes registered rapid price increases. This coincided with a significant rise in global liquidity. This paper attempts to determine the extent to which the rise in asset prices was influenced by developments in global...

We propose a novel reinforcement learning approach to extract high-frequency aggregate growth expectations from asset prices. While much expectations-based research in macroeconomics and finance relies on low-frequency surveys, the multitude of events that pass between survey dates renders...

I generalize the long-run risks (LRR) model of Bansal and Yaron (2004) by incorporating recursive smooth ambiguity aversion preferences from Klibanoff et al. (2005, 2009) and time-varying ambiguity. Relative to the Bansal-Yaron model, the generalized LRR model is as tractable but more flexible...

Optimal investment of firms implies that expected stock returns are tied with the expected marginal benefit of investment divided by the marginal cost of investment. Winners have higher expected growth and expected marginal productivity (two major components of the marginal benefit of...

This paper investigates the validity and usefulness of “hybrid” valuation models. We recast the model in Ohlson and Johannesson (2016) as a hybrid of the Dividend Discount Model and an earnings-based price multiple model, and develop a new hybrid model that generalizes the Residual Income...

Using management earnings forecasts over the period 1996-2010, I find that the sensitivity of forecast revisions to contemporaneous stock returns is increasing in the amount of investors' private information in prices. This effect remains after controlling for various confounds and is robust to...

We show empirically that survey-based measures of expected inflation are significant and strong predictors of future aggregate stock returns in several industrialized countries both in-sample and out-of-sample. By empirically discriminating between competing sources of this return predictability...

The study reports empirical evidence that artificial neural network based models are applicable to forecasting of stock market returns. The Nigerian stock market logarithmic returns time series was tested for the presence of memory using the Hurst coefficient before the models were trained. The...