Query: Sources of Momentum Profits : Evidence on the Irrelevance of Characteristics ∗

Relevant passages:
Momentum and Credit Rating <=SEP=> This paper establishes a strong link between momentum and firm credit rating. Momentum profitability is statistically significant and economically large among low-grade firms, but it is nonexistent among high-grade firms. The momentum payoffs documented in the literature are generated by low rated firms that account for less than four percent of the overall market capitalization of rated firms. For loser (winner) stocks in the low rating category, profit margins, sales growth, operating cash flows, and interest coverage decrease (increase) over the formation and holdings periods, while illiquidity and volatility increase (decrease). This operating performance of the winner and loser stocks is not anticipated by the market as evidenced by the earnings surprises and analyst forecast revisions. As the market observes the deteriorating (improving) conditions there is a pressure to sell (buy) losers (winners). Indeed, we show that institutional investors buy winners and sell losers during one year into the holding period. Such buying and selling activity accompanied with high illiquidity precipitates the gains among high credit-risk winners and losses among high credit-risk losers.

A Unified Theory of Underreaction , Momentum Trading , and Overreaction in Asset Markets <=SEP=> We model a market populated by two groups of boundedly rational agents: “newswatchers” and “momentum traders.” Each newswatcher observes some private information, but fails to extract other newswatchers’ information from prices. If information diffuses gradually across the population, prices underreact in the short run. The underreaction means that the momentum traders can profit by trendchasing. However, if they can only implement simple ~i.e., univariate! strategies, their attempts at arbitrage must inevitably lead to overreaction at long horizons. In addition to providing a unified account of underand overreactions, the model generates several other distinctive implications. OVER THE LAST SEVERAL YEARS, a large volume of empirical work has documented a variety of ways in which asset returns can be predicted based on publicly available information. Although different studies have used a host of different predictive variables, many of the results can be thought of as belonging to one of two broad categories of phenomena. On the one hand, returns appear to exhibit continuation, or momentum, in the short to medium run. On the other hand, there is also a tendency toward reversals, or fundamental reversion, in the long run.1 It is becoming increasingly clear that traditional asset-pricing models— such as the capital asset pricing model ~CAPM! of Sharpe ~1964! and Lintner ~1965!, Ross’s ~1976! arbitrage pricing theory ~APT!, or Merton’s ~1973! intertemporal capital asset pricing model ~ICAPM!—have a hard time explaining the growing set of stylized facts. In the context of these models, all of the predictable patterns in asset returns, at both short and long horizons, must ultimately be traced to differences in loadings on economically meaningful risk factors. And there is little affirmative evidence at this point to suggest that this can be done. * Stanford Business School and MIT Sloan School of Management and NBER. This research is supported by the National Science Foundation and the Finance Research Center at MIT. We are grateful to Denis Gromb, René Stulz, an anonymous referee, and seminar participants at MIT, Michigan, Wharton, Duke, UCLA, Berkeley, Stanford, and Illinois for helpful comments and suggestions. Thanks also to Melissa Cunniffe for help in preparing the manuscript. 1 We discuss this empirical work in detail and provide references in Section I below. THE JOURNAL OF FINANCE • VOL. LIV, NO. 6 • DECEMBER 1999

ON THE PRICING OF CORPORATE DEBT : THE RISK STRUCTURE OF INTEREST RATES <=SEP=> On the Pricing of Corporate Debt: The Risk Structure of Interest Rates Author(s): Robert C. Merton Source: The Journal of Finance, Vol. 29, No. 2, Papers and Proceedings of the Thirty-Second Annual Meeting of the American Finance Association, New York, New York, December 28-30, 1973 (May, 1974), pp. 449-470 Published by: Blackwell Publishing for the American Finance Association Stable URL: http://www.jstor.org/stable/2978814 Accessed: 16/10/2008 10:09

Momentum , Business Cycle and Time-Varying Expected Returns By <=SEP=> Momentum, Business Cycle and Time -Varying Expected Returns In recent years there has been a dramatic growth in academic interest in the predictability of asset returns based on past history. A growing number of researchers argue that time-series patterns in returns are due to investor irrationality, and thus can be translated into abnormal profits. Continuation of shortterm returns or momentum is one such pattern that has defied any rational explanation, and is at odds with market efficiency. This paper shows that profits to momentum strategies can be explained by a set of lagged macroeconomic variables and payoffs to momentum strategies disappear once stock returns are adjusted for their predictability based on these macroeconomic variables. Our results provide a possible role for time-varying expected returns as an explanation for momentum payoffs.

Disclosure , Liquidity , and the Cost of Capital <=SEP=> This paper shows that revealing public information to reduce information asymmetry can reduce a firm's cost of capital by attracting increased demand from large investors due to increased liquidity of its securities. Large firms will disclose more information since they benefit most. Disclosure also reduces the risk bearing capacity available through market makers. If initial information asymmetry is large, reducing it will increase the current price of the security. However, the maximum current price occurs with some asymmetry of information: further reduction of information asymmetry accentuates the undesirable effects of exit from market making. THIS PAPER STUDIES THE causes and consequences of a security's liquidity, especially the effect of future liquidity on the security's current price-equivalently the effect on its required expected rate of return, its cost of capital. Under conditions that we identify, reducing information asymmetry reduces the cost of capital. Under other (less typical) conditions, this reduced information asymmetry can have the opposite effect. We use public disclosure of information as the means of changing information asymmetry, but the points are more general. Our model is related to those of Kyle (1985), Glosten and Milgrom (1985), and Admati and Pfleiderer (1988). They assume that there is unlimited risk bearing capacity devoted to market making, which implies that changes in future liquidity never influence a security's cost of capital.1 In contrast, we develop a model of trade in an illiquid market with limited risk bearing capacity of risk-averse market makers and examine the effects of private information on the incentives of market makers to provide risk bearing *The University of Chicago and the University of Pennsylvania, respectively. Diamond gratefully acknowledges support from NSF grant SES-8896223 and from a gift to the University of Chicago from Dimensional Fund Advisors. Verrecchia gratefully acknowledges financial assistance from Ernst & Young and the Wharton School of the University of Pennsylvania. We are grateful to Franklin Allen, Ren6 Stulz, Robert Vishny, an anonymous referee, and workshop participants at the University of Chicago, the Federal Reserve Bank of Richmond, Georgetown University, and UCLA for helpful comments on a previous draft. 'Given the assumption, one could even close the secondary market for an arbitrarily long time in the future without influencing the current price. As a security becomes less liquid in these models, more of the security is permanently held by the market makers who do not care about liquidity.

Robust sensor-based grasp primitive for a three-finger robot hand <=SEP=> This paper addresses the problem of robot grasping in conditions of uncertainty. We propose a grasp controller that deals robustly with this uncertainty using feedback from different contact-based sensors. This controller assumes a description of grasp consisting of a primitive that only determines the initial configuration of the hand and the control law to be used. We exhaustively validate the controller by carrying out a large number of tests with different degrees of inaccuracy in the pose of the target objects and by comparing it with results of a naive grasp controller.

Integrating Out Multinomial Parameters in Latent Dirichlet Allocation and Naive Bayes for Collapsed Gibbs Sampling <=SEP=> This note shows how to integrate out the multinomial parameters for latent Dirichlet allocation (LDA) and naive Bayes (NB) models. This allows us to perform Gibbs sampling without taking multinomial parameter samples. Although the conjugacy of the Dirichlet priors makes sampling the multinomial parameters relatively straightforward, sampling on a topic-by-topic basis provides two advantages. First, it means that all samples are drawn from simple discrete distributions with easily calculated parameters. Second, and more importantly, collapsing supports fully stochastic Gibbs sampling where the model is updated after each word (in LDA) or document (in NB) is assigned a topic. Typically, more stochastic sampling leads to quicker convergence to the stationary state of the Markov chain made up of the Gibbs samples.

Feature selection for high-dimensional data <=SEP=> This paper offers a comprehensive approach to feature selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection in the context of high-dimensional data. First, we focus on the basis of feature selection, providing a review of its history and basic concepts. Then, we address different topics in which feature selection plays a crucial role, such as microarray data, intrusion detection, or medical applications. Finally, we delve into the open challenges that researchers in the field have to deal with if they are interested to confront the advent of “Big Data” and, more specifically, the “Big Dimensionality”.

A 6.8GHz low-profile H-plane ridged SIW horn antenna <=SEP=> The substrate integrated waveguide (SIW) allows the construct of some types of planar antennas which cannot be conventionally integrated on the substrate. However, owing to some technology restrictions, designed SIW horn antennas normally work in the frequency above 10 GHz. This paper proposes a 6.8GHz low-profile H-plane horn antenna based on the ridged SIW, which allows the substrates thinner than λ0/10. Far field radiation patterns are reported to reveal the good performance of proposed antenna. Comparisons among the ridged SIW horn antennas with different number of ridges are also made to shown the matching improvements.

Neural Networks applied to wireless communications <=SEP=> This paper presents a time-delayed neural network (TDNN) model that has the capability of learning and predicting the dynamic behavior of nonlinear elements that compose a wireless communication system. This model could help speeding up system deployment by reducing modeling time. This paper presents results of effective application of the TDNN model to an amplifier, part of a wireless transmitter.

Long Text Generation via Adversarial Training with Leaked Information <=SEP=> Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc. Recently, by combining with policy gradient, Generative Adversarial Nets (GAN) that use a discriminative model to guide the training of the generative model as a reinforcement learning policy has shown promising results in text generation. However, the scalar guiding signal is only available after the entire text has been generated and lacks intermediate information about text structure during the generative process. As such, it limits its success when the length of the generated text samples is long (more than 20 words). In this paper, we propose a new framework, called LeakGAN, to address the problem for long text generation. We allow the discriminative net to leak its own high-level extracted features to the generative net to further help the guidance. The generator incorporates such informative signals into all generation steps through an additional MANAGER module, which takes the extracted features of current generated words and outputs a latent vector to guide the WORKER module for next-word generation. Our extensive experiments on synthetic data and various realworld tasks with Turing test demonstrate that LeakGAN is highly effective in long text generation and also improves the performance in short text generation scenarios. More importantly, without any supervision, LeakGAN would be able to implicitly learn sentence structures only through the interaction between MANAGER and WORKER.

Pvclust: an R package for assessing the uncertainty in hierarchical clustering <=SEP=> SUMMARY
Pvclust is an add-on package for a statistical software R to assess the uncertainty in hierarchical cluster analysis. Pvclust can be used easily for general statistical problems, such as DNA microarray analysis, to perform the bootstrap analysis of clustering, which has been popular in phylogenetic analysis. Pvclust calculates probability values (p-values) for each cluster using bootstrap resampling techniques. Two types of p-values are available: approximately unbiased (AU) p-value and bootstrap probability (BP) value. Multiscale bootstrap resampling is used for the calculation of AU p-value, which has superiority in bias over BP value calculated by the ordinary bootstrap resampling. In addition the computation time can be enormously decreased with parallel computing option.

Behavioral Game Theory on Online Social Networks : Colonel Blotto is on Facebook <=SEP=> We show how online social networks such as Facebook can be used in Behavioral Game Theory research. We report the deployment of a Facebook application ‘Project Waterloo’ that allows users to play the Colonel Blotto game against their friends and strangers. Unlike conventional studies performed in the laboratory environment, which rely on monetary incentives to attract human subjects to play games, our framework does not use money and instead relies on reputation and entertainment incentives. We describe the Facebook application we created for conducting this experiment, and perform a preliminary analysis of the data collected in the game. We conclude by discussing the advantages of our approach and list some ideas for future work.

Chinese character CAPTCHA recognition based on convolution neural network <=SEP=> CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are increasingly used in many applications for machine and human identification. Compared with traditional English and digital characters based CAPTCHAs, Chinese characters contain more complicated characters which greatly enhance difficulty of automatic recognition. To solve that problem, we proposed a Convolution Neural Network (CNN) based approach. This approach greatly improves the recognition accuracy of Chinese Character CAPTCHAs with distortion, rotation and background noise. Our experiment results show that this approach achieves more than 95% accuracy for single character and 84% accuracy for three types of Chinese Character CAPTCHAs with four characters. This encouraging result indicates that deep neural network is useful in complicated structure perception of Chinese Character CAPTCHAs.

Robust single particle tracking in live cell time-lapse sequences <=SEP=> Single-particle tracking (SPT) is often the rate-limiting step in live-cell imaging studies of subcellular dynamics. Here we present a tracking algorithm that addresses the principal challenges of SPT, namely high particle density, particle motion heterogeneity, temporary particle disappearance, and particle merging and splitting. The algorithm first links particles between consecutive frames and then links the resulting track segments into complete trajectories. Both steps are formulated as global combinatorial optimization problems whose solution identifies the overall most likely set of particle trajectories throughout a movie. Using this approach, we show that the GTPase dynamin differentially affects the kinetics of long- and short-lived endocytic structures and that the motion of CD36 receptors along cytoskeleton-mediated linear tracks increases their aggregation probability. Both applications indicate the requirement for robust and complete tracking of dense particle fields to dissect the mechanisms of receptor organization at the level of the plasma membrane.

Improving the resolution performance of eigenstructure-based direction-finding algorithms <=SEP=> 

A Semi-Automatic Approach to Handle Data Warehouse Schema Evolution using Ontology <=SEP=> In recent years, the number of digital information storage and retrieval systems has increased immensely. Data warehousing has been found to be an extremely useful technology for integrating such heterogeneous and autonomous information sources. Data within the data warehouse is modelled in the form of a star or snowflake schema which facilitates business analysis in a multidimensional perspective. The data warehouse schema is derived from these sources and business requirements. Due to changing business needs the information sources not only change their data but as well change their schema structure. In addition to the source changes the business requirements for data warehouse may also change. Both these changes results in data warehouse schema evolution. Existing approaches either deal with source changes or requirements changes in a manual way and the changes to the data warehouse schema is carried out at the physical level. This may induce high maintenance costs and complex OLAP server administration. As ontology seems to be promising technique in data warehousing research, in this paper we propose an ontological approach to automate the evolution of a data warehouse schema. Our method assists the designer of the data warehouse to handle evolution at the conceptual level and after validation the physical schema is constructed automatically.

A Survey on Semantic Web Search Engine <=SEP=> The tremendous growth in the volume of data and with the terrific growth of number of web pages, traditional search engines now a days are not appropriate and not suitable anymore. Search engine is the most important tool to discover any information in World Wide Web. Semantic Search Engine is born of traditional search engine to overcome the above problem. The Semantic Web is an extension of the current web in which information is given well-defined meaning. Semantic web technologies are playing a crucial role in enhancing traditional web search, as it is working to create machine readable data. but it will not replace traditional search engine. In this paper we made a brief survey on various promising features of some of the best semantic search engines developed so far and we have discussed the various approaches to semantic search. We have summarized the techniques, advantages of some important semantic web search engines that are developed so far.The most prominent part is that how the semantic search engines differ from the traditional searches and their results are shown by giving a sample query as input.

Distributed Mutual Exclusion Algorithms for Intersection Traffic Control <=SEP=> Traffic control at intersections is a key issue and hot research topic in intelligent transportation systems. Existing approaches, including traffic light scheduling and trajectory maneuver, are either inaccurate and inflexible or complicated and costly. More importantly, due to the dynamics of traffic, it is really difficult to obtain the optimal solution in a real-time way. Inspired by the emergence of vehicular ad hoc network, we propose a novel approach to traffic control at intersections. Via vehicle to vehicle or vehicle to infrastructure communications, vehicles can compete for the privilege of passing the intersection, i.e., traffic is controlled via coordination among vehicles. Such an approach is flexible and efficient. To realize the coordination among vehicles, we first model the problem as a new variant of the classic mutual exclusion problem, and then design algorithms to solve new problem. Both centralized and distributed algorithms are. We conduct extensive simulations to evaluate the performance of our proposed algorithms. The results show that, our approach is efficient and outperforms a reference algorithm based on optimal traffic light scheduling. Moreover, our approach does not rely on traffic light or intersection controller facilities, which makes it flexible and applicable to various kinds of intersections.

Combining Machine Learning Techniques and Natural Language Processing to Infer Emotions Using Spanish Twitter Corpus <=SEP=> In the recent years, microblogging services, as Twitter, have become a popular tool for expressing feelings, opinions, broadcasting news, and communicating with friends. Twitter users produced more than 340 million tweets per day which may be consider a rich source of user information. We take a supervised approach to the problem, but leverage existing hashtags in Twitter for building our training data. Finally, we tested the Spanish emotional corpus applying two different machine learning algorithms for emotion identification reaching about 65% accuracy.

Learning to Create Jazz Melodies Using a Product of Experts <=SEP=> We describe a neural network architecture designed to learn the musical structure of jazz melodies over chord progressions, then to create new melodies over arbitrary chord progressions from the resulting connectome (representation of neural network structure). Our architecture consists of two sub-networks, the interval expert and the chord expert, each being LSTM (long short-term memory) recurrent networks. These two sub-networks jointly learn to predict a probability distribution over future notes conditioned on past notes in the melody. We describe a training procedure for the network and an implementation as part of the opensource Impro-Visor (Improvisation Advisor) application, and demonstrate our method by providing improvised melodies based on a variety of training sets.

Ship Classification in TerraSAR-X Images With Feature Space Based Sparse Representation <=SEP=> Ship classification is the key step in maritime surveillance using synthetic aperture radar (SAR) imagery. In this letter, we develop a new ship classification method in TerraSAR-X images based on sparse representation in feature space, in which the sparse representation classification (SRC) method is exploited. In particular, to describe the ship more accurately and to reduce the dimension of the dictionary in SRC, we propose to employ a representative feature vector to construct the dictionary instead of utilizing the image pixels directly. By testing on a ship data set collected from TerraSAR-X images, we show that the proposed method is superior to traditional methods such as the template matching (TM), K-nearest neighbor (K-NN), Bayes and Support Vector Machines (SVM).

Robust Real-Time Periodic Motion Detection, Analysis, and Applications <=SEP=> We describe new techniques to detect and analyze periodic mo t on as seen from both a static and moving camera. By tracking objects of interest, we compute a n object’s self-similarity as it evolves in time. For periodic motion, the self-similarity measure is a l o periodic, and we apply Time-Frequency analysis to detect and characterize the periodic motion. Th e periodicity is also analyzed robustly using the 2-D lattice structures inherent in similarity matrices . A real-time system has been implemented to track and classify objects using periodicity. Examples of o bject classification (people, running dogs, vehicles), person counting, and non-stationary periodici ty are provided.

Finding scientific gems with Google's PageRank algorithm <=SEP=> We apply the Google PageRank algorithm to assess the relative importance of all publications in the Physical Review family of journals from 1893 to 2003. While the Google number and the number of citations for each publication are positively correlated, outliers from this linear relation identify some exceptional papers or “gems” that are universally familiar to physicists. © 2006 Published by Elsevier Ltd. PACS: 02.50.Ey; 05.40.-a; 05.50.+q; 89.65.-s

Automatic Driving on Ill-defined Roads: An Adaptive, Shape-constrained, Color-based Method <=SEP=> Autonomous following of ill-defined roads is an important part of visual navigation systems. This paper presents an adaptive method that uses a statistical model of the colour of the road surface within a trapezoidal shape that approximately corresponds to the projection of the road on the image plane. The method does not perform an explicit segmentation of the images but instead expands the shape sideways until the match between shape and road worsens, simultaneously computing the colour statistics. Results show that the method is capable of reactively following roads, at driving speeds typical of the robots used, in a variety of situations while coping with variable conditions of the road such as surface type, puddles and shadows. We extensively evaluate the proposed method using a large number of datasets with ground truth (that will be made public on our server once the paper is published). We moreover evaluate many colour spaces in the context of road following and find that the colour spaces that separate luminance from colour information perform best, especially if the luminance information is discarded.

Mapping the knowledge domain and the theme evolution of appropriability research between 1986 and 2016: a scientometric review <=SEP=> The scholars in the research domains of innovation and strategic management concerned about the appropriability for about 30 years or more. They focused on appropriability research and constantly evolving. In this paper, we analyze 30 years (1986–2016) literature on appropriability studies from Web of Science Core Collection database. A cited reference clustering map of different periods and terms co-occurrence map have been generated using bibliometric analysis and content analysis. Based on this, we study the evolutionary trajectory, mechanisms and theoretical architecture of appropriability research and explore further research directions. The results indicate that the essence of the appropriability research evolution is the perception changes in opening and sharing, value creation and value growth, and future research is focusing on role of appropriability in the platform governance, generative appropriability and the evolution of the problem-solving mechanisms.

Robust real-time underwater digital video streaming using optical communication <=SEP=> We present a real-time video delivery solution based on free-space optical communication for underwater applications. This solution comprises of AquaOptical II, a high-bandwidth wireless optical communication device, and a two-layer digital encoding scheme designed for error-resistant communication of high resolution images. Our system can transmit digital video reliably through a unidirectional underwater channel, with minimal infrastructural overhead. We present empirical evaluation of this system's performance for various system configurations, and demonstrate that it can deliver high quality video at up to 15 Hz, with near-negligible communication latencies of 100 ms. We further characterize the corresponding end-to-end latencies, i.e. from time of image acquisition until time of display, and reveal optimized results of under 200 ms, which facilitates a wide range of applications such as underwater robot tele-operation and interactive remote seabed monitoring.

Reidentification by Relative Distance Comparison <=SEP=> Matching people across nonoverlapping camera views at different locations and different times, known as person reidentification, is both a hard and important problem for associating behavior of people observed in a large distributed space over a prolonged period of time. Person reidentification is fundamentally challenging because of the large visual appearance changes caused by variations in view angle, lighting, background clutter, and occlusion. To address these challenges, most previous approaches aim to model and extract distinctive and reliable visual features. However, seeking an optimal and robust similarity measure that quantifies a wide range of features against realistic viewing conditions from a distance is still an open and unsolved problem for person reidentification. In this paper, we formulate person reidentification as a relative distance comparison (RDC) learning problem in order to learn the optimal similarity measure between a pair of person images. This approach avoids treating all features indiscriminately and does not assume the existence of some universally distinctive and reliable features. To that end, a novel relative distance comparison model is introduced. The model is formulated to maximize the likelihood of a pair of true matches having a relatively smaller distance than that of a wrong match pair in a soft discriminant manner. Moreover, in order to maintain the tractability of the model in large scale learning, we further develop an ensemble RDC model. Extensive experiments on three publicly available benchmarking datasets are carried out to demonstrate the clear superiority of the proposed RDC models over related popular person reidentification techniques. The results also show that the new RDC models are more robust against visual appearance changes and less susceptible to model overfitting compared to other related existing models.

The implications of embodiment for behavior and cognition: animal and robotic case studies <=SEP=> In this paper, we will argue that if we want to understand the function of the brain (or the control in the case of robots), we must understand how the brain is embedded into the physical system, and how the organism interacts with the real world. While embodiment has often been used in its trivial meaning, i.e. ‘intelligence requires a body’, the concept has deeper and more important implications, concerned with the relation between physical and information (neural, control) processes. A number of case studies are presented to illustrate the concept. These involve animals and robots and are concentrated around locomotion, grasping, and visual perception. A theoretical scheme that can be used to embed the diverse case studies will be presented. Finally, we will establish a link between the low-level sensory-motor processes and cognition. We will present an embodied view on categorization, and propose the concepts of ‘body schema’ and ‘forward models’ as a natural extension of the embodied approach toward first representations.

Double-Hypergraph Based Sentence Ranking for Query-Focused Multi-document Summarizaton <=SEP=> Traditional graph based sentence ranking approaches modeled the documents as a text graph where vertices represent sentences and edges represent pairwise similarity relationships between two sentences. Such modeling cannot capture complex group relationships shared among multiple sentences which can be useful for sentence ranking. In this paper, we propose two different group relationships (sentence-topic relationship and document-topic relationship) shared among sentences, and construct a double-hypergraph integrating these relationships into a unified framework. Then, a double-hypergraph based sentence ranking algorithm is developed for query-focused multi-document summarization, in which Markov random walk is defined on each hypergraph and the mixture Markov chains are formed so as to perform transductive learning in the double-hypergraph. When evaluated on DUC datasets, performance of the proposed approach is remarkable.