Understanding the behavior of asset prices is essential in evaluating investment decisions so that practitioners and academics have been investigating what assets are worth and whether asset prices are predictable. The degree of stock return predictability is a statistical concept and totally an empirical issue. In this study, we conduct an empirical application on the equity premium forecastability concept. We consider both macroeconomic and technical indicators in order to predict the equity risk premiums of selected thirteen stock markets including Belgium, Greece, Malaysia, Mexico, Portugal, Spain, Taiwan, Turkey, Brazil, Hong-Kong, India, Russia, and South Africa. Incorporating information from all indicators including domestic and foreign macroeconomic indicators, oil price changes, and technical indicators provides in-sample fitting gains. Out-of-sample forecasting results show that the strategy of combining forecasts provides out-of-sample gains. Portfolio performance analyses suggests following the forecasts generated by the proposed models to obtain positive returns over the buy-and-hold strategy.

Intense competition, fast changing technology, increased and diverse customer demand, and fast and reliable services and goods delivery expectations have reduced the profit margins for products. To be able to obtain a sustainable economic success, companies search for ways to offer the existing products via innovative ways. Revenue management is a tool that helps firms in this purpose. On the other hand, to match and even exceed customer anticipations, firms need to understand customer preferences and offer products accordingly. Customers can now reach wide range of competitor products due to online services. Hence it became much more important to apprehend and integrate customer choices into decision support tools. At this point, choice models are used to mathematically understand the customer choice preferences among a set of presented alternatives. This book combines revenue management and choice models to present a methodology to understand customer choices and integrate these to revenue management methods to be able to offer the best set of products to the customers. Also real life applications for hotels have been presented for different methods that are discussed and also proposed in this book with respective algorithms. When combined with decision support tools, concepts presented in this book offers a significant revenue increase for companies.