Titanium Trisulfide Monolayer: Theoretical Prediction of a New Direct-Gap Semiconductor with High and Anisotropic Carrier Mobility

A new two-dimensional (2D) layered material, namely, titanium trisulfide (TiS3) monolayer, is predicted to possess novel electronic properties. Ab initio calculations show that the perfect TiS3 monolayer is a direct-gap semiconductor with a bandgap of 1.02 eV, close to that of bulk silicon, and with high carrier mobility. More remarkably, the in-plane electron mobility of the 2D TiS3 is highly anisotropic, amounting to about 10,000 cm2 V−1 s−1 in the b direction, which is higher than that of the MoS2 monolayer, whereas the hole mobility is about two orders of magnitude lower. Furthermore, TiS3 possesses lower cleavage energy than graphite, suggesting easy exfoliation for TiS3. Both dynamical and thermal stability of the TiS3 monolayer is examined by phonon-spectrum calculation and Born–Oppenheimer molecular dynamics simulation. The desired electronic properties render the TiS3 monolayer a promising 2D atomic-layer material for applications in future nanoelectronics. Includes Supplemental Materials (Fig. S1

A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing

The financial crisis of 2008 generated interest in more transparent, rules-based strategies for portfolio construction, with Smart beta strategies emerging as a trend among institutional investors. While they perform well in the long run, these strategies often suffer from severe short-term drawdown (peak-to-trough decline) with fluctuating performance across cycles. To address cyclicality and underperformance, we build a dynamic asset allocation system using Hidden Markov Models (HMMs). We test our system across multiple combinations of smart beta strategies and the resulting portfolios show an improvement in risk-adjusted returns, especially on more return oriented portfolios (up to 50$\%$ in excess of market annually). In addition, we propose a novel smart beta allocation system based on the Feature Saliency HMM (FSHMM) algorithm that performs feature selection simultaneously with the training of the HMM, to improve regime identification. We evaluate our systematic trading system with real life assets using MSCI indices; further, the results (up to 60$\%$ in excess of market annually) show model performance improvement with respect to portfolios built using full feature HMMs