Is Firms’ Social Media Engagement Informative about Firm Performance?

Abstract In this paper, I examine whether the volume of a firm’s tweets and its followers’ engagement is informative to capital market participants and financial intermediaries, namely investors and analysts. My data comprises of 178,236 firm-quarters (46,449 Tweet firm-quarters) and approximately 17.50 million firm-initiated tweets collected from the Primary Twitter sites of 2,229 public US firms between 2006 and 2017. I find that the volume of a firm’s tweets and the followers’ engagement during a quarter predicts the firm value during that period. The results also suggest that changes in tweet (engagement) volume are informative to investors and the information gets impounded in the stock prices concurrently. I also find evidence that followers’ engagement is more informative than the firm’s tweet volume for predicting firm-value. My findings further indicate that analysts may be using this additional information in the firm’s tweet (engagement) volume to make more accurate earnings and sales forecast, which reduces the Tweet firm’s unexpected earnings and unexpected sales growth. In additional analysis, I find that the level of tweets (engagement) helps predict a firm’s earnings and sales whereas changes in tweet (engagement) volume incrementally explain the firm’s sales growth and this may be the source of additional information to investors and analysts

In-Vivo Lipidomics using Single-Cell Raman Spectroscopy

We describe a method for direct, quantitative, in vivo lipid profiling of oil producing microalgae using single-cell laser-trapping Raman spectroscopy (LTRS). This approach is demonstrated in the quantitative determination of the degree of unsaturation and transition temperatures of constituent lipids within microalgae. These properties are important markers for determining engine compatibility and performance metrics of algal biodiesel. We show that these factors can be directly measured from a single living microalgal cell held in place with an optical trap while simultaneously collecting Raman data. Cellular response to different growth conditions is monitored in real time. Our approach circumvents the need for lipid extraction and analysis that is both slow and invasive. Furthermore, this technique yields real-time chemical information in a label-free manner, thus eliminating the limitations of impermeability, toxicity and specificity of the fluorescent probes used in other common protocols. Although the single-cell Raman spectroscopy demonstrated here is focused on the study of the microalgal lipids with biofuel applications, the analytical capability and quantitation algorithms demonstrated are applicable to many different organisms, and should prove useful for a diverse range of applications in lipidomics