Statistical model selection criteria provide an informed choice of the model with best external (i.e., out-of-sample) validity. Therefore they guard against overfitting (“data snooping”). We implement several model selection criteria in order to verify recent evidence of predictability in excess stock returns and to determine which variables are valuable predictors. We confirm the presence of i...

This paper proposes a scratchpad memory allocation technique for stack data to achieve predictability for hard real-time embedded systems. First, we suggest a profiling-based source code modification technique to make the whole stack area fit in the given scratchpad memory size. Furthermore, even though the whole stack area cannot fit in the scratchpad memory size, we propose another scratchpad...

Two experiments were conducted in order to test the effects of cue predictability on serial dependencies in response times and response durations. Predictability in the timing (Experiment 1) and identity (Experiment 2) of response cues was manipulated. Results of both experiments showed that long-range dependencies in response times were stronger when cues were predictable versus unpredictable....

Data projection is an important tool in exploratory data analysis. Sammon’s non linear projection method lacks predictability and is ineffective for large data sets. To introduce predictability we implement an extension of Sammon’s algorithm using fuzzy logic approach. The fuzzy based rule model is implemented in the .Net framework using Microsoft Visual Studio with Visual C# as the programming...

High dividend yields imply that either future dividend growth must be low, or future discount rates must be high, or both. While previous studies have focused on the predictability of expected returns from dividend yields, dividend yields also strongly predict future dividends, and the predictability of dividend growth is much stronger than discount rates returns at a 1-year horizon. Inference ...

This letter discusses blind separability based on temporal predictability (Stone, 2001 ; Xie, He, & Fu, 2005 ). Our results show that the sources are separable using the temporal predictability method if and only if they have different temporal structures (i.e., autocorrelations). Consequently, the applicability and limitations of the temporal predictability method are clarified. In addition, i...

I wish to address development of theoretical framework by improving and combining currently existing methodologies and techniques. I also wish to adapt new approaches based on dynamical systems theories that have not been used to enhance predictability in the past. My overall approach will involve Eulerian and Lagrangian descriptions of the probablity density funtion evolution, identification a...

This paper examines return predictability when the investor is uncertain about the right state variables. A novel feature of the model averaging approach used in this paper is to account for finite-sample bias of the coefficients in the predictive regressions. Drawing on an extensive international dataset, we find that interest-rate related variables are usually among the most prominent predict...

Real-time embedded systems increasingly need to process and store large volumes of persistent data, requiring fast, timely and predictable storage. Traditional methods of accessing storage using general-purpose operating system-based file systems do not provide the performance and timing predictability needed. This paper firstly examines the speed and consistency of SSD operations in an embedde...

A new metric that quantifies the predictability of a time series is introduced. This new time series predictability metric is developed based on the η -metric method introduced by Kaboudan, but overcomes the resolution and stationarity problems presented in the pure η -metric method. It also provides a new feature, which shows how the predictability changes over different subsequences in a time...