When can social media lead financial markets?

Social media analytics is showing promise for the prediction of financial markets. The research presented here employs linear regression analysis and information theory analysis techniques to measure the extent to which social media data is a predictor of the future returns of stock-exchange traded financial assets. Two hypotheses are proposed which investigate if the measurement of social media data in real-time can be used to pre-empt – or lead – changes in the prices of financial markets. Using Twitter as the social media data source, this study firstly investigates if geographically-filtered Tweets can lead the returns of UK and US stock indices. Next, the study considers if string-filtered Tweets can lead the returns of currency pairs and the securities of individual publically-traded companies. The study evaluates Tweet message sentiments – mathematical quantifications of text strings’ moods – and Tweet message volumes. A sentiment classification system specifically designed and validated in literature to accurately rank social media’s colloquial vernacular is employed. This research builds on previous studies which either use sentiment analysis techniques not geared for such text, or which instead only consider social media message volumes. Stringent tests for statistical-significance are employed. Tweets on twenty-eight financial instruments were collected over three months – a period chosen to minimise the effect of the economic cycle in the time-series whilst encapsulating a range of market conditions, and during which no major product changes were made to Twitter. The study shows that Tweet message sentiments contain lead-time information about the future returns of twelve of these securities, in excess of what is achievable via the analysis of Twitter message volumes. The study’s results are found to be robust against modification in analysis parameters, and that additional insight about market returns can be gained from social media data sentiment analytics under particular parameter variations

Promotion of protocell self-assembly from mixed amphiphiles at the origin of life

Vesicles formed from single-chain amphiphiles (SCAs) such as fatty acids probably played an important role in the origin of life. A major criticism of the hypothesis that life arose in an early ocean hydrothermal environment is that hot temperatures, large pH gradients, high salinity and abundant divalent cations should preclude vesicle formation. However, these arguments are based on model vesicles using 1–3 SCAs, even though Fischer–Tropsch-type synthesis under hydrothermal conditions produces a wide array of fatty acids and 1-alkanols, including abundant C10–C15 compounds. Here, we show that mixtures of these C10–C15 SCAs form vesicles in aqueous solutions between pH ~6.5 and >12 at modern seawater concentrations of NaCl, Mg2+ and Ca2+. Adding C10 isoprenoids improves vesicle stability even further. Vesicles form most readily at temperatures of ~70 °C and require salinity and strongly alkaline conditions to self-assemble. Thus, alkaline hydrothermal conditions not only permit protocell formation at the origin of life but actively favour it

Active Photoswitching of Sharp Fano Resonances in THz Metadevices

Fano resonances offer exciting features in enhancing the non-linearity and sensing capabilities in metamaterial systems. An active photoswitching of Fano resonances in a terahertz metadevice at low optical pump powers is demonstrated, which signifies the extreme sensitivity of the high-quality-factor resonant electric field to the external light illumination.MOE (Min. of Education, S’pore)Accepted versio