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Proteins with hetero-bimetallic metal centers can catalyze important reactions and are challenging to design. Azurin is a mononuclear copper center that has been extensively studied for electron transfer. Here we inserted the lanthanide binding tag (LBT), which binds lanthanide with sub μM affinity, into the copper binding loop of azurin, while keeping the type 1 copper center unperturbed. The resulting protein, Az-LBT, which has two metal bonding centers, shows strong luminescence upon coordination with Tb3+ and luminescence quenching upon Cu2+ binding. The in vitro luminescence quenching has high metal specificity and a limit-of-detection of 0.65 μM for Cu2+. With the low background from lanthanide's long luminescence lifetime, bacterial cells expressing Az-LBT in the periplasm also shows sensitivity for metal sensing.

Background Bone–tendon junction (BTJ) is a unique structure connecting tendon and bone through a fibrocartilage zone. Owing to its unique structure, the regeneration of BTJ remains a challenge. Here, we study the fibrochondrogenic differentiation of human tendon-derived stem/progenitor cells (TSPCs) both in vitro and in vivo. Methods TSPCs were isolated from human patellar tendon tissues and investigated for their multidifferentiation potential. TSPCs were cultured in chondrogenic medium with transforming growth factor beta 3 (TGF-β3) and BMP-2 in vitro ​and examined for the expression of fibrochondrogenic marker genes by quantitative real-time reverse transcription polymerase chain reaction, enzyme-linked immunosorbent assay, and immunofluorescence. TSPCs pretreated were also seeded in collage II sponge and then transplanted in immunocompromised nude mice to examine if the fibrochondrogenic characteristics were conserved in vivo. Results We found that TSPCs were differentiated towards fibrochondrogenic lineage, accompanied by the expression of collagen I, collagen II, SRY-box transcription factor 9 (Sox 9), and tenascin C. Furthermore, after TSPCs were seeded in collagen II sponge and transplanted in immunocompromised nude mice, they expressed fibrochondrogenic genes, including proteoglycan, collagen I, and collagen II. Conclusion Taken together, this study showed that TSPCs are capable of differentiating towards fibrocartilage-like cells, and the fibrochondrogenic characteristics were conserved even in vivo, and thus might have the potential application for fibrocartilage regeneration in BTJ repair. The translational potential of this article TSPCs are able to differentiate into fibrocartilage-like cells and thus might well be one potential cell source for fibrocartilage regeneration in a damaged BTJ repair.

Electro thermal papers have been widely applied in people's daily life. Compared to traditional metal alloy resistance heaters, three dimensional graphene/carbonized-PAN composite freestanding paper with lightweight, good flexibility and excellent electrothermal performance is presented. It retains intrinsic properties of graphene sheets in the bulk state, and exhibits fast electrothermal response under low operation voltage with robust mechanical property. The fastest specific heating rate could be up to 213 °C s−1 V−1, and the highest Ts is 235 °C at low driving voltage just of 1.75 V, which far surpasses those of previous carbon-based film heaters, commercial nichrome wire, and Kanthal wire. These outstanding properties together with the advantage of facile and large-scale fabrication, make the composite paper with great potential applications in flexible electronics.

We show that hedge fund managers who more actively and astutely adjust the political sensitivity of their portfolios, in line with the dynamic U.S. political landscape, improve their investment performance. Funds that tilt their portfolios toward market segments expected to perform better during the new political regime, specifically around U.S. Presidential elections, generate significantly higher alphas. Further, hedge fund families with greater responsiveness to political changes exhibit persistently superior performance and are more likely to survive. Hedge fund investors reward more responsive fund managers with greater inflows.

The application of pose assessment on rehabilitation training has gradually received attention in recent years. However, current evaluation indicators of these methods are mostly based on the score or scoring function that defined by users, which is too subjective and hard to be used by patients directly. In this paper, we conceptualized a new idea for pose matching, namely pose-guided matching that aims at providing objective and accurate score, feedback and guidance (i.e. guided) to the patients when the pose is compared to the standard pose. More specifically, we proposed a pair-based Siamese Convolutional Neural Network (SCNN) abbreviated ST-AMCNN to realize the idea of pose-guided matching on the eight-section brocade dataset which is one of the most representative traditional rehabilitation exercises in China. We simplified the multi-stages pose matching by merging two standalone modules (i.e. alignment and matching module) into a one-stage task. Such that, only one loss function is required to tune, which reduces the computational complexity. On top of the Spatial Transformer Networks (STN) employed as an alignment module, we proposed a new Attention-based Multi-Scale Convolution (AMC) to match different posture parts (i.e. multi-scale). Furthermore, the proposed AMC can assign more weight to useful pose features as opposed to other irrelevant features e.g. background features for performance gain. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) is adopted to visualize the matching result for the learner. Experimental results indicate that ST-AMCNN achieves a competitive performance than the state-of-the-art models and can provide accurate feedback for learners on rehabilitation training. Simultaneously, the proposed method is also deployed in client software for testing.

Unlike past studies which have focused on either executives or boards of directors, this study takes an interactionist view to investigate the determinants of corporate financial fraud. We propose that CEOs evaluate the opportunities for financial fraud according to both situational stimuli and their own personal characteristics. As older directors are often more experienced and have more to lose if they fail in their monitoring duties, we expect them to be more capable and to have stronger motivation for monitoring CEOs closely. As such, we propose that a CEO is less likely to engage in corporate financial fraud when the average age of the board of directors increases (i.e., board age). However, when the CEO is older than the board, the CEO may attach less importance to board age when deciding whether to commit fraud. Therefore, we further propose that the CEO–board directional age difference can weaken the effect of board age. Our empirical analyses provide strong support for these hypotheses. Our study contributes to the literature on corporate governance by highlighting the often neglected roles of board age and CEO–board directional age difference in deterring corporate financial fraud.

We investigate the relationship between IPO long-run underperformance ( Ritter, 1991 ; Loughran and Ritter, 1995 ) and the idiosyncratic risk puzzle ( Ang, Hodrick, Xing, and Zhang, 2006 ) or the phenomenon of abnormally low returns for stocks with high idiosyncratic risk. We find that IPO long-run underperformance is a manifestation of surprisingly low returns for high idiosyncratic risk stocks. IPO underperformance disappears after we control for idiosyncratic risk. On the other hand, we find that the idiosyncratic risk puzzle is magnified by IPO underperformance. Our results are robust to various specifications or sample requirements. We evaluate a couple of potential common causes for the two puzzles and conclude that investors’ preferences for stocks with lottery features is the primary mechanism linking the two puzzles.

Fact verification aims to retrieve related evidence from raw text to verify the correctness of a given claim. Existing works mainly leverage the single-granularity features for the representation learning of evidences, i.e., sentence features, ignoring other features like entity-level and context-level features. In addition, they usually focus on improving the prediction accuracy while lacking the interpretability of the inference process, which leads to unreliable results. Thus, in this paper, to investigate how to utilize multi-granularity semantic units for evidence representation as well as to improve the explainability of evidence reasoning, we propose a Hierarchical Reasoning-based Heterogeneous Graph Neural Network for fact verification (HHGN). HHGN combines multiple features of entity, sentence as well as context for evidence representation, and employs a heterogeneous graph to capture their semantic relations. Inspired by the human inference process, we design a hierarchical reasoning-based node updating strategy to propagate the evidence features. Then, we extract the potential reasoning paths from the graph to predict the label, which aggregates the results of different paths weighted by their relevance to the claim. We evaluate our proposal on FEVER, a large-scale benchmark dataset for fact verification. Our experimental results demonstrate the superiority of HHGN over the competitive baselines in both single evidence and multiple evidences settings. In addition, HHGN presents reasonable interpretability in the form of aggregating the features of relevant entity units and selecting the evidence sentences with high confidence.

It is a great challenge to fabricate lightweight microwave absorption materials (MAMs) with strong electromagnetic wave attenuation over wide frequency range. In this work, ultralight multiwalled carbon nanotube (MWCNT)/graphene foams (CGFs) are prepared through a facile solvothermal method and their microwave absorption (MA) properties are fully investigated. The CGFs exhibit tunable complex permittivity and conductivity through regulating MWCNT loading and thermal reduction temperature. The addition of MWCNT remarkably enhances the MA intensity of CGFs in low frequency. A minimum reflection loss value of −39.5 dB and average absorption intensity exceeding 22.5 dB in both C (4–8 GHz) and X (8–12 GHz) bands are obtained. For the optimized CGF, the qualified bandwidth with reflection loss less than - 10 dB reaches up to 16 GHz, which covers the whole measured range of 2–18 GHz and shares the widest qualified bandwidth among open literature reports. Furthermore, a specific MA performance of 12243 dB cm2 g−1 is realized, which is one of the best results among various MAMs. The synergistic effect of MWCNT and graphene and thus obtained three dimensional high loss multilevel network architecture are thought to be the primary causes for the excellent MA performance of CGFs.

This study applied a type of fly ash microsphere (FAM) collected directly from a high-temperature furnace using ceramic dust tubes to high-strength concrete, and with silica fume as a control admixture, investigated the effects of FAM on the hydration and hardening processes of the cementitious materials and the macroscopic properties of the high-strength concrete. Two cement replacement levels (8% and 15%) and two water-to-binder (W/B) ratios (0.35 and 0.25) were utilized. The results show that FAM has a relatively high level of early activity; SEM images indicate that a significant portion of the FAM reacted at early ages in the cement-FAM hardened paste. Though the early activity of FAM is lower than that of silica fume, the cement-FAM complex binder has similar hydration properties with the cement-silica fume complex binder. At 90 d, FAM consumed less Ca(OH) 2 than silica fume and a significant amount of unreacted FAM remained in the hardened pastes. The contributions of FAM to the pore structure of the hardened pastes are lower than those of silica fume at early ages; however, FAM can significantly improve pore structure at late ages, similar to silica fume. In addition, FAM can improve the flowability, late-age strength, and permeability to chloride ions of concrete, while decreasing early-age autogenous shrinkage.

The influence of the initial moist curing time on the resistance to the sulfate attack of concretes with different binders and at different water/binder ratios was studied by determining the compressive strength and the chloride ion permeability of the concrete before sulfate attack, the compressive strength after sulfate attack, and the microstructure of the corroded concrete matrix. The results illustrate that both the crystallized sodium sulfate and ettringite formed due to invaded sulfate are the principal expansion sources of the concrete subjected to the sulfate attack environment. Both the additions of fly ash and ground granulated blast furnace slag (GGBS) tend to significantly reduce the permeability and enhance the sulfate attack resistance of concrete on the premise of sufficient moist curing. The enhancing effect of GGBS is greater than that of fly ash on the sulfate attack resistance of concrete with each initial moist curing regime. In the case of insufficient moist curing for 3 or 7 days, the compressive strength loss rate of concrete containing fly ash is close to that of the plain cement concrete, indicating that the addition of fly ash makes little contribution to the enhancement of the sulfate attack resistance of concrete. Though the enhancing effect of GGBS on the sulfate attack resistance of concrete decreases with the decrease of moist curing time, the compressive strength loss rate of the concrete containing GGBS is about half of that of the plain cement concrete. The sulfate attack resistance of concrete is less sensitive to the moist curing time at lower water/binder ratio.

Using detailed data about company visits by Chinese mutual funds, we provide direct evidence of mutual fund information acquisition activities and the consequent informational advantages mutual funds establish in local firms. Mutual funds are more likely to visit local and nearby firms, though ease of travel between fund and firm locations alleviates geographic distance constraints. Company visits by mutual funds are related to both fund trading activities and fund trading performance. The relation between company visits and fund trading performance is particularly strong in newly initiated stock positions. Consistent with this result, we find evidence of intensive information acquisition by mutual funds in stocks not in their current portfolios. Our results suggest that mutual fund local preference in portfolio choice is at least partially driven by geographic constraints of information acquisition and the ensuing information asymmetry.

Fact verification aims to retrieve relevant evidence from a knowledge base, e.g., Wikipedia, to verify the given claims. Existing methods only consider the sentence-level semantics for evidence representations, which typically neglect the importance of fine-grained features in the evidence-related sentences. In addition, the interpretability of the reasoning process has not been well studied in the field of fact verification. To address such issues, we propose an entity-graph based reasoning method for fact verification abbreviated as RoEG, which generates the fine-grained features of evidence at the entity-level and models the human reasoning paths based on an entity graph. In detail, to capture the semantic relations of retrieved evidence, RoEG introduces the entities as nodes and constructs the edges in the graph based on three linking strategies. Then, RoEG utilizes a selection gate to constrain the information propagation in the sub-graph of relevant entities and applies a graph neural network to propagate the entity-features for reasoning. Finally, RoEG employs an attention aggregator to gather the information of entities for label prediction. Experimental results on a large-scale benchmark dataset FEVER demonstrate the effectiveness of our proposal by beating the competitive baselines in terms of label accuracy and FEVER Score. In particular, for a task of multiple-evidence fact verification, RoEG produces 5.48% and 4.35% improvements in terms of label accuracy and FEVER Score against the state-of-the-art baseline. In addition, RoEG shows a better performance when more entities are involved for fact verification.

Incorporating topic information can help response generation models to produce informative responses for chat-bots. Previous work only considers the individual semantic of each topic, ignoring its specific dialog context, which may result in inaccurate topic representation and hurt response coherence. Besides, as an important feature of multi-turn conversation, dynamic topic transitions have not been well-studied. We propose a Context-Controlled Topic-Aware neural response generation model, i.e., CCTA, which makes dialog context interact with the process of topic representing and transiting to achieve balanced improvements on response informativeness and contextual coherence. CCTA focuses on capturing the semantical relations within topics as well as their corresponding contextual information in conversation, to produce context-dependent topic representations at the word-level and turn-level. Besides, CCTA introduces a context-controlled topic transition strategy, utilizing contextual topics to yield relevant transition words. Extensive experimental results on two benchmark multi-turn conversation datasets validate the superiority of our proposal on generating coherent and informative responses against the state-of-the-art baselines. We also find that topic transition modeling can work as an auxiliary learning task to boost the response generation.

A comparative study of the mechanical properties, volume stability and durability of high-volume limestone powder concrete (HVLPC) and high-volume fly ash concrete (HVFAC) was conducted by testing pore structures of the paste and determining the adiabatic temperature rise, porosity, compressive strength, chloride ion permeability, 1 year's natural carbonation depth and dry shrinkage strains of the concrete. The test results illustrate that the reductive influence of high-volume limestone powder on the temperature rise of the massive concrete is identical to that of the high-volume fly ash. Compared with high-volume fly ash concrete, high-volume limestone powder concrete with the same water/binder ratio has relatively high interconnected porosity, low compressive strength, and low resistance to carbonation and chloride ion penetration, especially at the steam-curing conditions. The volume stability of concrete containing a high volume of limestone powder is almost identical to that of concrete containing fly ash with the same water/binder ratio. Reducing the water/binder ratio and replacing part of the limestone powder by ground granulated blast furnace slag can reduce the negative effects of limestone powder on the macro-properties of concrete.

Theoretical analyses and empirical studies are lacking on the antecedents, consequences, and contingencies of employee participation in company-sponsored volunteer programs. In response, we build on the motivation-based theory of volunteerism to explore the questions of why and when employees engage in company-sponsored volunteer programs and when corporate volunteering experience positively influences job performance at work. Using a three-wave time-lagged study with a sample from a large real estate company, we found that coworker corporate volunteering (but not leader role modeling of corporate volunteering) weakened and social support for corporate volunteering from family and friends strengthened the relationship between prosocial motivation and participation in volunteer programs. Furthermore, we discovered that when employees had positive learning experiences from corporate volunteering, their participation in volunteer programs did not distract from job performance, whereas when employees did not learn much from corporate volunteering, their corporate volunteering harmed job performance at work. The findings contribute to the literature on corporate volunteering and explain the joint influences of personal, social, and learning motives underlying corporate volunteering.

Recent evidence of excessive comovement among stocks following index additions (Barberis, Shleifer, and Wurgler, 2005) and stock splits (Green and Hwang, 2009) challenges traditional finance theory. Based on a simple model, we show that the bivariate regressions relied upon in the literature often provide little or no information about the economic magnitude of the phenomenon of interest, and the coefficients in these regressions are very sensitive to time-variation in the characteristics of the return processes that are unrelated to excess comovement. Instead, univariate regressions of the stock return on the returns of the group it is leaving (e.g., non-S&P stocks) and the group it is joining (e.g., S&P stocks) reveal the relevant information. When we reexamine the empirical evidence using control samples matched on past returns and compute Dimson betas, almost all evidence of excess comovement disappears. The results in the literature are consistent with changes in the fundamental factor loadings of the stocks. One key element to understanding these striking results is that, in both the examples we study, the stocks exhibit strong returns prior to the event in question. We document the heretofore unknown empirical regularity that winner stocks exhibit increases in betas. Thus, much of the apparent excess comovement is just a manifestation of momentum.

Macroscopic lossy foam has been expected to be the most promising candidate for lightweight high-performance microwave absorption (MA). However, inferior MA behaviors of conventional foams reported previously are disappointing. The emerging graphene foam (GF) has broken this paradoxical state of affairs. Here, series of GFs with various chemical compositions and physical structures have been prepared via a facile and controllable method and their MA performance is investigated in 2–18 GHz. The in-depth analyses of the GF’s composition, structure and MA property demonstrate that the MA performance of the GF is strongly correlated with the C/O ratio, conjugated carbon domain size and graphene framework’s microstructure. A maximum absorption value of −34.0 dB as well as 14.3 GHz qualified bandwidth with reflection loss below −10 dB is achieved for the GF with an ultralow bulk density of 1.6 mg/cm3, of which the average absorption intensity and the specific MA efficiency are much higher than those of the best available MA materials in previous literature. The composition & structure–performance relationship of MA foams is revealed. The balance between small interfacial impedance gap and high loss characteristic has wide implications in improving the MA performance of the GF and other porous materials.

While few-layered graphene (FG) has been synthesized by several different arc-discharge methods, its growing mechanism has hardly been studied by experiment. Here, we have investigated systematically its growth mechanism using the arc-discharge method under different environments including helium, oxygen-helium and hydrogen-helium. The results indicate that FG can only be produced in the presence of reactive gases, implying that the growing mechanism of few-layered graphene involves graphite evaporation and reactive-gas-confining crystallization of the evaporated carbon clusters. The key factor inducing the discrepancies in FG synthesis under different buffer gases can be assigned to the reactivity of corresponding gases.

There is a growing need for enabling reprogramming a working sensor network in unattended area. We prefer to meet the requirements remotely by disseminating parameters instead of collecting all deployed sensors. Identifying the version differences of parameters on different sensor nodes can significantly reduce the communication overhead, because only those out-of-date ones need to be updated. In this paper, we propose BDP, a Bloom filter based data dissemination protocol for wireless sensor networks. Using Bloom filters as compact storage of the version information of data items, BDP efficiently identifies the version differences among data items with the same key and guarantees network-wide consistency with high reliability. Testbed experiment and simulation results demonstrate that BDP outperforms the existing schemes with low energy cost, short propagation delay of updating new items, and high reliability.

We develop and test a hypothesis that hedge-fund managers who respond to and capitalize on the changing political environment around Presidential elections are better skilled. We find that hedge funds, on average, trade in anticipation of the presidential election outcome, and adjust the political sensitivity of their portfolio accordingly. Managers who adjust their portfolio political sensitivity most successfully generate significantly higher alpha than those that are least successful in their adjustments. The significant superior performance by these funds persists for about a year. We also find that these funds are more likely to survive over the following two years. Our evidence suggests that hedge funds’ anticipation and response to Presidential elections indicate managerial skill and can successfully predict future fund performance.

Li 1.95 FeSiO 4 /C/CNTs, Li 2 FeSiO 4 /C/CNTs and Li 1.95 FeSiO 4 /C composites were successfully prepared by the means of solid-state reaction in the present work. X-ray diffraction (XRD), scanning electron microscopy (SEM), charge–discharge test and electrochemical impedance spectra measurement were used to characterize the above samples and their differences were comparatively discussed. It was found that the Li 1.95 FeSiO 4 /C/CNTs composite delivered the best electrochemical performance in terms of the discharge capacity, cycling stability and rate capability among the above three samples. It exhibited a discharge capacity of 148 mAh g − 1 at 0.2 C and 104 mAh g − 1 at 5 C, respectively, and after 100 cycles at 1 C, 99.2% of its initial capacity was retained. EIS result showed that electrochemical resistance of the Li 1.95 FeSiO 4 /C/CNTs electrode was obviously decreased, revealing much faster kinetics in comparison with the Li 2 FeSiO 4 /C/CNTs and Li 1.95 FeSiO 4 /C electrodes.

Li1.95FeSiO4/C and Li2FeSiO4/C composites were synthesized by a traditional solid-state reaction method and then discussed comparatively through the results of X-ray diffraction (XRD), scanning electron microscopy (SEM), the Brunauer–Emmet–Teller (BET) method, the charge–discharge test and electrochemical impedance spectra measurement, respectively. The results demonstrated that the Li1.95FeSiO4/C composite could exhibit much better battery performance in terms of the discharge capacity, cycling stability and rate capability in comparison with the Li2FeSiO4/C composite. At 0.2C and 5C, it delivered a discharge capacity of 142 mAh g−1 and 93 mAh g−1, respectively, and after 100 cycles at 1C, 95.1% of its initial capacity was retained.

BACKGROUND: Individuals with early-onset depression may be a clinically distinct group with particular symptom patterns, illness course, comorbidity and family history. This question has not been previously investigated in a Han Chinese population. METHODS: We examined the clinical features of 1970 Han Chinese women with DSM-IV major depressive disorder (MDD) between 30 and 60 years of age across China. Analysis of linear, logistic and multiple logistic regression models was used to determine the association between age at onset (AAO) with continuous, binary and discrete characteristic clinical features of MDD. RESULTS: Earlier AAO was associated with more suicidal ideation and attempts and higher neuroticism, but fewer sleep, appetite and weight changes. Patients with an earlier AAO were more likely to suffer a chronic course (longer illness duration, more MDD episodes and longer index episode), increased rates of MDD in their parents and a lower likelihood of marriage. They tend to have higher comorbidity with anxiety disorders (general anxiety disorder, social phobia and agoraphobia) and dysthymia. CONCLUSIONS: Early AAO in MDD may be an index of a more severe, highly comorbid and familial disorder. Our findings indicate that the features of MDD in China are similar to those reported elsewhere in the world.

We develop a new methodology that controls for both the timing of annual earnings news ( Asquith et al., 1989 ) and the performance prior to split announcements ( Barber and Lyon, 1996 ) to evaluate the information content of stock splits. In contrast to existing evidence, we find that stock splits in aggregate are followed by positive abnormal future earnings growth, suggesting that stock splits contain information about future, rather than past, operating performance. When we use changes in breadth of institutional ownership as a new metric of information content to corroborate our findings, we find that splits with the greatest increase in breadth experience positive post-split abnormal returns and positive abnormal earnings growth. Together, our results suggest that some splits contain positive information about future performance, and that sophisticated market participants such as institutional investors are able to select these splits.

This paper proposes a novel architecture called KCube. KCube is a compound graph of Kautz digraph and hypercube. It employs the hypercube topology as a unit cluster and connects many such clusters by means of a Kautz digraph. It then utilizes the topological properties of hypercube to realize convenient embedding of parallel algorithms, and the short diameter of Kautz graph to support efficient inter-cluster communication. KCube possesses many attractive characteristics, such as modularity, expansibility, and regularity, while these benefits are achieved at the cost of only increasing the degree of any node by one, regardless of the network size. The methodology to construct KCube can also be applied to other compound networks after minimal modifications.

The dentate gyrus of adult mammalian brain contains neural progenitor cells with self-renewal and multi-lineage potential. The lineage and maturation of the neural progenitors are determined by the composition and levels of the trophic factors in their microenvironment. In Alzheimer disease (AD) brain, especially the hippocampus, the level of basic fibroblast growth factor (FGF-2) is markedly elevated. Here we show that elevated FGF-2 enhances the division and nestin levels of cultured adult rat hippocampal progenitors but impairs neuronal lineage determination and maturation of these cells in culture. The trophic factors ciliary neurotrophic factor (CNTF), glial-derived neurotrophic factor (GDNF), and insulin-like growth factors-1 and -2 (IGF-1, IGF-2) as well as an Alzheimer peptidergic drug, Cerebrolysin((R)) (CL), in which we found these neurotrophic activities, counteract the effect of FGF-2 in inducing neuronal lineage (early neurogenesis). Whereas CNTF is the most active of the neurotrophic factors studied in promoting neurogenesis, CL, probably because of a combined effect of these factors, induces similar changes but without inhibiting cell proliferation. These findings suggest that CNTF, GDNF, IGF-1, and IGF-2 are promising therapeutic targets for AD and other diseases in which neurogenesis is probably inhibited.

We apply an ex ante measure of divergence of investor opinions – industry-level dispersion in analyst earnings forecast – to test the Miller (1977) prediction about IPO overvaluation. Our measure of divergence of opinion is the dispersion in analyst long-term earnings growth forecasts divided by the absolute value of the mean long-term growth forecast averaged at the industry level in the month prior to the IPO. We focus on the effect of divergence of opinion on long-run IPO performance, and test two hypotheses. First, greater divergence of opinion should predict lower long-run performance. Second, since the Miller effect depends on the demand of the offer relative to the supply, we further predict that the effect of belief dispersion should be stronger for offers with high investor demand, as measured by issue overallotment at the time of the offer. Using a sample of about 7,000 IPOs from 1982 to 2005, we find that IPOs in industries with high divergence of opinion have lower long-run returns in the following 3-5 years than IPOs in industries with low divergence of opinion. The effect of belief dispersion on long-run performance primarily exists among IPOs with high demand, as measured by the overallotment at the time of the offer, and this effect remains strong when returns are value-weighted. These findings support Miller’s hypothesis that in markets with restricted short-selling, valuations reflect the most optimistic investor’s appraisal in the short-run, and the initial overvaluation is corrected in the long-run. There is evidence that IPOs with high divergence of opinion also have lower first-day returns, but this association is weakened by the amount of overallotment, suggesting that the correction of initial overvaluation starts shortly after the offer, but the initial correction is dampened by investor demand at the time of the offer.

We find that, due to arbitrage around index changes, investors in S&P 500-linked funds lose between 0.03% and 0.12% annually, while investors in Russell 2000-linked funds lose between 1.30% and 1.84%. In dollar terms, the losses range from $3.75 billion to $6 billion a year for the two indexes together. These losses are an unexpected consequence of index fund investors evaluating index fund managers based on tracking error in an effort to control agency costs. Minimization of tracking error coupled with the predictability and/or pre-announcement of index changes creates the opportunity for a wealth transfer from index fund investors to arbitrageurs, particularly for Russell 2000-linked funds where the index changes are predictable. We propose solutions aimed at resolving the problem that can be implemented by indexing companies, index fund managers, or fund investors.

We present evidence on the December effect. When investors do not sell winner stocks in December but postpone their sale to January so that capital gains will not be realized in the current fiscal year, the "winners" appreciate in December. The December effect is relatively easy to arbitrage. We also present evidence regarding the persistence of the January effect and note that the January effect continues because it is difficult to exploit profitably.

The multitude of explanations for the January effect leaves the reader confused about its primary cause(s): is it tax-loss selling, window-dressing, information, bid-ask bounce, or a combination of these causes? The confusion arises, in part, because evidence has been presented in support of a particular hypothesis though the same evidence may be consistent with more than one hypothesis. Furthermore, prior work has not adequately controlled for the bid-ask bounce. In this paper, we try to disentangle different explanations of the January effect and identify its primary cause. We find that tax-related selling is the most important cause, overshadowing other possible explanations.

Several studies have found that stock price changes resulting from firms added to the S&P 500 index can be best exp lained by a downward sloping demand curve. In this paper, we study price effects around both additions and deletions and find that the price effect of index changes is consistent with Merton's (1987) investor-awareness and market segmentation hypothesis. We find that the reduction in shadow cost of incomplete diversification that follows additions is correlated with abnormal returns accruing to the added stocks. We also find that the asymmetric price effects of additions and deletions that have not been explained by empirical studies thus far are consistent with market segmentation.

In this paper, we argue that short sellers affect prices in a significant and systematic manner. In particular, we contend that speculative short sales contribute to the weekend effect: the inability to trade over the weekend is likely to cause these short sellers to close many of their speculative positions on Fridays and reestablish new short positions on Mondays causing stock prices to rise on Fridays and fall on Mondays. We find evidence in support of this hypothesis: the weekend effect is significantly larger for high short-interest stocks than for low short-interest stocks. Further, we find that the likely substitution of speculative short sales by put options results in the weekend effect to diminish for stocks with actively traded options, but to continue for other stocks. An analysis of several special types of stocks, viz. IPOs, zero short-interest stocks, and highly volatile stocks, reveals support for the hypothesis.

The January anomaly has attracted much academic interest and has been explained in different ways. However, the multitude of explanations has created confusion about the validity and relative importance of those explanations. In some cases, the hypotheses are examined individually though the evidence may be consistent with more than one hypothesis. Furthermore, prior work has not adequately controlled for the bid-ask bounce. Therefore, the results leave the reader somewhat confused regarding the January effect: is it caused by tax-loss selling, window-dressing, information, bid-ask bounce, or a combination of these causes? In this paper, we try to disentangle different explanations of the January effect and identify its primary cause. We find that past losers are more likely to be sold in December than in January to realize the tax advantage of capital losses. Past winners are more likely to be sold in January than in December to postpone payment of taxes. The selling is accompanied by changes in volume around turn of the year consistent with the tax-related selling hypotheses. The results are not materially affected when we use the midpoint of quotes instead of actual prices: the bid-ask bounce accounts for about 20-25% of the observed returns. To verify the window-dressing hypothesis, we examine stock returns around June-July, the period of semi-annual reporting by institutional managers that is not contaminated by tax-related trading. We do not find an economically meaningful difference between the 5-day return at the end of June and the 5-day return at the beginning of July, which is not consistent with window dressing. If the January effect occurs due to release of new information in January that affects the information-poor firms more than the information-rich firms then the returns in January should be related to availability of information (for example, with the number of analysts as a proxy). We do not find a correlation consistent with the information hypothesis. There is no information-related effect in June-July. The evidence here supports the tax-related selling hypotheses as the drivers of January effect.