Dynamic Forecasting Behavior by Analysts: Theory and Evidence

We examine the dynamic forecasting behavior of security analysts in response to their prior performance relative to their peers within a continuous time/multi-period framework. Our model predicts a U-shaped relationship between the boldness of an analyst's forecast, that is, the deviation of her forecast from the consensus and her prior relative performance. In other words, analysts who significantly out perform or under perform their peers issue bolder forecasts than intermediate performers. We then test these predictions of our model on observed analyst forecast data. Consistent with our theoretical predictions, we document an approximately U-shaped relationship between analysts' prior relative performance and the deviation of their forecasts from the consensus. Our theory examines the impact of both explicit incentives in the form of compensation structures and implicit incentives in the form of career concerns, on the dynamic forecasting behavior of analysts. Consistent with existing empirical evidence, our results imply that analysts who face greater employment risk (that is, the risk of being fired for poor performance) have greater incentives to herd, that is, issue forecasts that deviate less from the consensus. Our multi-period model allows us to examine the dynamic forecasting behavior of analysts in contrast with the extant two-period models that are static in nature. Moreover, the model also differs significantly from existing theoretical models in that it does not rely on any specific assumptions regarding the existence of asymmetric information and/or differential analyst abilities.Security analysts, herding, career concerns

Risk, Uncertainty and Optimism in Venture Capital Relationships

We develop a dynamic, structural model to quantitatively assess the effects of risk, uncertainty and asymmetric beliefs about project quality on the characteristics of venture capital relationships. We estimate the model parameters with data about the distributions of total investments, payoffs, risks and returns of venture capital projects. Entrepreneurial optimism mitigates the agency costs of risk-sharing between venture capitalists (VCs) and entrepreneurs (ENs) by over 20 % and significantly enhances the VC’s expected payoffs. The EN optimism premium implied by the data explains the huge discrepancy between the discount rates used by VCs ( ∼ 40%), which adjust for optimistic payoff projections by ENs, and the average expected return of VC projects ( ∼ 15%). Consistent with observed contractual structures, the equilibrium dynamic contracts feature both equity-like and debt-like components for the VC and progressive vesting of the EN’s stake. The duration, project value and the VC’s expected payoff all increase with the project’s transient risk but decrease with its intrinsic risk

Unified translation repression mechanism for microRNAs and upstream AUGs

Abstract Background MicroRNAs (miRNAs) are endogenous small RNAs that modulate gene expression at the post-transcriptional level by binding complementary sites in the 3'-UTR. In a recent genome-wide study reporting a new miRNA target class (miBridge), we identified and validated interactions between 5'-UTRs and miRNAs. Separately, upstream AUGs (uAUGs) in 5'-UTRs are known to regulate genes translationally without affecting mRNA levels, one of the mechanisms for miRNA-mediated repression. Results Using sequence data from whole-genome cDNA alignments we identified 1418 uAUG sequences on the 5'-UTR that specifically interact with 3'-ends of conserved miRNAs. We computationally identified miRNAs that can target six genes through their uAUGs that were previously reported to suppress translation. We extended this meta-analysis by confirming expression of these miRNAs in cell-lines used in the uAUG studies. Similarly, seven members of the KLF family of genes containing uAUGs were computationally identified as interacting with several miRNAs. Using KLF9 as an example (whose protein expression is limited to brain tissue despite the mRNA being expressed ubiquitously), we show computationally that miRNAs expressed only in HeLa cells and not in neuroblastoma (N2A) cells can bind the uAUGs responsible for translation inhibition. Our computed results demonstrate that tissue- or cell-line specific repression of protein translation by uAUGs can be explained by the presence or absence of miRNAs that target these uAUG sequences. We propose that these uAUGs represent a subset of miRNA interaction sites on 5'-UTRs in miBridge, whereby a miRNA binding a uAUG hinders the progression of ribosome scanning the mRNA before it reaches the open reading frame (ORF). Conclusions While both miRNAs and uAUGs are separately known to down-regulate protein expression, we show that they may be functionally related by identifying potential interactions through a sequence-specific binding mechanism. Using prior experimental evidence that shows uAUG effects on translation repression together with miRNA expression data specific to cell lines, we demonstrate through computational analysis that cell-specific down-regulation of protein expression (while maintaining mRNA levels) correlates well with the simultaneous presence of miRNA and target uAUG sequences in one cell type and not others, suggesting tissue-specific translation repression by miRNAs through uAUGs.http://deepblue.lib.umich.edu/bitstream/2027.42/112383/1/12864_2009_Article_2749.pd

Novel Bioinformatics Approaches for MicroRNA Detection and Target Prediction.

MicroRNAs (miRNAs) are regulators of gene expression at the post-transcriptional level. Scientists have not been able to fully unlock their therapeutic potential because their functions and mechanisms of action have not been fully characterized. In this thesis we address shortcomings and provide solutions for detecting miRNAs in a high-throughput manner and for predicting miRNA targets - areas key to understanding miRNA function. Profiling expression of miRNAs using microarrays has its limitations owing to diverse melting temperatures and high sequence similarities, which affects sensitivity and specificity. A simple yet effective strategy that we employ involves base changes to probes complementary to mature miRNAs. Using nearest-neighbour thermodynamic principles we determine the best probes for all mature miRNAs that serve to eliminate cross-hybridization and create a uniform melting temperature profile. We present a set of probes that are designed for the human let-7 family and demonstrate their power to resolve these similar sequences in a microarray experiment using both spiked-in and true biological samples. The second problem that is tackled in this thesis involves improving miRNA target prediction, a key to understanding miRNA function in various biological processes. We use a combination of thermodynamic and sequence-based searches to identify endogenous sites on 5′-UTRs. There are two aspects that make our approach unique compared to other target prediction methodologies. First, we not only consider seed-matches on the 3′-UTR but also 5′-UTR matches with 3′-ends of miRNAs. Second, we show that non-conserved sites on the 5′-UTR can possibly contribute to species-specific targeting. We verify our claims through in vitro experiments using two predicted miRNA-target pairs: hsa-miR-34a and its target AXIN2, and cel-lin-4 and its target lin28. Extending results from the target prediction study, we show that upstream AUGs (uAUGs), which are known to post-transcriptionally regulate gene expression, are probable binding sites for miRNAs. We show that the cell- or tissue-specific repression of genes that harbour uAUGs can be explained by the expression of targeting miRNAs in those cells. The approaches suggested here will help further our understanding of how these tiny RNAs regulate gene expression.Ph.D.BioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/62207/1/sasubram_1.pd

Spatial-frequency channels, shape bias, and adversarial robustness

What spatial frequency information do humans and neural networks use to recognize objects? In neuroscience, critical band masking is an established tool that can reveal the frequency-selective filters used for object recognition. Critical band masking measures the sensitivity of recognition performance to noise added at each spatial frequency. Existing critical band masking studies show that humans recognize periodic patterns (gratings) and letters by means of a spatial-frequency filter (or "channel'') that has a frequency bandwidth of one octave (doubling of frequency). Here, we introduce critical band masking as a task for network-human comparison and test 14 humans and 76 neural networks on 16-way ImageNet categorization in the presence of narrowband noise. We find that humans recognize objects in natural images using the same one-octave-wide channel that they use for letters and gratings, making it a canonical feature of human object recognition. On the other hand, the neural network channel, across various architectures and training strategies, is 2-4 times as wide as the human channel. In other words, networks are vulnerable to high and low frequency noise that does not affect human performance. Adversarial and augmented-image training are commonly used to increase network robustness and shape bias. Does this training align network and human object recognition channels? Three network channel properties (bandwidth, center frequency, peak noise sensitivity) correlate strongly with shape bias (53% variance explained) and with robustness of adversarially-trained networks (74% variance explained). Adversarial training increases robustness but expands the channel bandwidth even further away from the human bandwidth. Thus, critical band masking reveals that the network channel is more than twice as wide as the human channel, and that adversarial training only increases this difference.Comment: Accepted to Neural Information Processing Systems (NeurIPS) 2023 (Oral Presentation

Structural, electrical transport and optical studies of Li ion doped ZnO nanostructures

In the present work, we studied the morphological aspects, electrical transport and optical properties of pure and lithium ion doped semiconducting ZnO nanostructures successfully prepared by a co-precipitation method. The effect of lithium doping and various morphologies on the structural, electrical and optical properties of these nanostructures were investigated. The X-ray diffraction (XRD) pattern demonstrated that the Li doped ZnO nanostructures exhibits the hexagonal wurtzite structure. A slight change in the 101 peak position was detected among the samples with various morphologies. The UV-Vis diffused reflectance spectroscopic (DRS) studies showed that the band gap increases with Li doping, due to the Burstein-Moss band filling effect. Photoluminescence (PL) studies confirm that the Li incorporation into ZnO material can induce oxygen enrichment of ZnO surface that leads to increase the cyan emission. This material could be used in light emitting diodes in nanoscale optoelectronic devices