The differential impact of investor sentiment on the value relevance of book value versus earnings

Thesis (Ph.D.)--Boston UniversityThis study investigates the differential role of investor sentiment on the value relevance of book value versus earnings. I predict and find that the value relevance of book value is higher during low sentiment relative to high sentiment periods, and conversely that the value relevance of earnings is higher during high sentiment relative to low sentiment periods. These findings are consistent with investors-when optimistic-placing a higher weight on earnings, which represent an accounting proxy more indicative of future performance, whereas investors-when pessimistic-placing a higher weight on book value, which represents an accounting proxy (given historical cost conventions) that is more indicative of current value. Additional analyses suggest that this sentiment effect is more pronounced for book value components that are closely related to abandonment value, and for earnings components that have strong indication of future earnings (specifically, permanent earnings). Results are also robust to alternative measures of investor sentiment

Sentiment, Loss Firms, and Investor Expectations of Future Earnings

This study investigates the mispricing of market-wide investor sentiment by exploring the relation between sentiment and investor expectations of future earnings. Prior research argues that sentiment-driven mispricing should be most pronounced for hard-to-value firms, such as those reporting losses (Baker and Wurgler 2006). Using investor expectations of future earnings, we provide empirical results consistent with this behavioral finance theory. In particular, we predict and find that investors perceive losses to be more (less) persistent during periods of low (high) sentiment; that investors perceive profit persistence to be lower (higher) during periods of low (high) sentiment; and that the effects appear stronger for loss firms relative to profit firms. In addition, we document predictable cross-sectional variation within losses, with the mispricing mitigated for losses associated with activities expected to generate future benefits: R&D, growth, large negative special items, and severe financial distress. Overall, our results document a new and important channel—investor expectations of future earnings—to explain sentiment-driven mispricing, particularly for loss firms

GOMCL: a t GOMCL: a toolkit t oolkit to cluster o cluster, evaluate, and extr aluate, and extract non-r act non-redundant edundant associations of Gene Ontology-based functions

Background Functional enrichment of genes and pathways based on Gene Ontology (GO) has been widely used to describe the results of various -omics analyses. GO terms statistically overrepresented within a set of a large number of genes are typically used to describe the main functional attributes of the gene set. However, these lists of overrepresented GO terms are often too large and contains redundant overlapping GO terms hindering informative functional interpretations. Results We developed GOMCL to reduce redundancy and summarize lists of GO terms effectively and informatively. This lightweight python toolkit efficiently identifies clusters within a list of GO terms using the Markov Clustering (MCL) algorithm, based on the overlap of gene members between GO terms. GOMCL facilitates biological interpretation of a large number of GO terms by condensing them into GO clusters representing non-overlapping functional themes. It enables visualizing GO clusters as a heatmap, networks based on either overlap of members or hierarchy among GO terms, and tables with depth and cluster information for each GO term. Each GO cluster generated by GOMCL can be evaluated and further divided into non-overlapping sub-clusters using the GOMCL-sub module. The outputs from both GOMCL and GOMCL-sub can be imported to Cytoscape for additional visualization effects. Conclusions GOMCL is a convenient toolkit to cluster, evaluate, and extract non-redundant associations of Gene Ontology-based functions. GOMCL helps researchers to reduce time spent on manual curation of large lists of GO terms, minimize biases introduced by redundant GO terms in data interpretation, and batch processing of multiple GO enrichment datasets. A user guide, a test dataset, and the source code of GOMCL are available at and

MultiLoRA: Democratizing LoRA for Better Multi-Task Learning

LoRA achieves remarkable resource efficiency and comparable performance when adapting LLMs for specific tasks. Since ChatGPT demonstrated superior performance on various tasks, there has been a growing desire to adapt one model for all tasks. However, the explicit low-rank of LoRA limits the adaptation performance in complex multi-task scenarios. LoRA is dominated by a small number of top singular vectors while fine-tuning decomposes into a set of less important unitary transforms. In this paper, we propose MultiLoRA for better multi-task adaptation by reducing the dominance of top singular vectors observed in LoRA. MultiLoRA scales LoRA modules horizontally and change parameter initialization of adaptation matrices to reduce parameter dependency, thus yields more balanced unitary subspaces. We unprecedentedly construct specialized training data by mixing datasets of instruction follow, natural language understanding, world knowledge, to cover semantically and syntactically different samples. With only 2.5% of additional parameters, MultiLoRA outperforms single LoRA counterparts and fine-tuning on multiple benchmarks and model scales. Further investigation into weight update matrices of MultiLoRA exhibits reduced dependency on top singular vectors and more democratic unitary transform contributions

Short-Wave Near-Infrared Spectrometer for Alcohol Determination and Temperature Correction

A multichannel short-wave near-infrared (SW-NIR) spectrometer module based on charge-coupled device (CCD) detection was designed. The design relied on a tungsten lamp enhanced by light emitting diodes, a fixed grating monochromator and a linear CCD array. The main advantages were high optical resolution and an optimized signal-to-noise ratio (0.24 nm and 500, resp.) in the whole wavelength range of 650 to 1100 nm. An application to alcohol determination using partial least squares calibration and the temperature correction was presented. It was found that the direct transfer method had significant systematic prediction errors due to temperature effect. Generalized least squares weighting (GLSW) method was utilized for temperature correction. After recalibration, the RMSEP found for the 25°C model was 0.53% v/v and errors of the same order of magnitude were obtained at other temperatures (15, 35 and 40°C). And an r2 better than 0.99 was achieved for each validation set. The possibility and accuracy of using the miniature SW-NIR spectrometer and GLSW transfer calibration method for alcohol determination at different temperatures were proven. And the analysis procedure was simple and fast, allowing a strict control of alcohol content in the wine industry

Identification of metabolism pathways directly regulated by sigma54 factor in Bacillus thuringiensis

Sigma54 (σ54) normally regulates nitrogen and carbon utilization in bacteria. Promoters that are σ54-dependent are highly conserved and contain short sequences located at the −24 and −12 positions upstream of the transcription initiation site. σ54 requires regulatory proteins known as bacterial enhancer-binding proteins (bEBPs) to activate gene transcription. We show that σ54 regulates the capacity to grow on various nitrogen sources using a Bacillus thuringiensis HD73 mutant lacking the sigL gene encoding σ54 (ΔsigL). A 2-fold-change cutoff and a false discovery rate cutoff of P < 0.05 were used to analyze the DNA microarray data, which revealed 255 genes that were downregulated and 121 that were upregulated in the ΔsigL mutant relative to the wild-type HD73 strain. The σ54 regulon (stationary phase) was characterized by DNA microarray, bioinformatics, and functional assay; 16 operons containing 47 genes were identified whose promoter regions contain the conserved −12/−24 element and whose transcriptional activities were abolished or reduced in the ΔsigL mutant. Eight σ54-dependent transcriptional bEBPs were found in the Bt HD73 genome, and they regulated night σ54-dependent promoters.The metabolic pathways activated by σ54 in this process have yet to be identified in Bacillus thuringiensis; nonetheless, the present analysis of the σ54 regulon provides a better understanding of the physiological roles of σ factors in bacteria