Fundamental Value Investors: Characteristics and Performance

We examine novel data on the detailed investment decisions of professional value investors. We find evidence that value investors are not easily defined: they exploit traditional tangible asset valuation discrepancies such as buying high book-to-market stocks, but spend more time analyzing intrinsic value, growth measures, and special situation investments. We also test whether fundamental value investors outperform the market in our sample (January 2000 to June 2008). Analyzing buy-and-hold abnormal returns and calendar-time portfolio regressions, we conclude that value investors have stock picking skills.Value investing, abnormal returns, hedge funds, market efficiency, Valueinvestorsclub.com performance

Reliable Inference from Unreliable Agents

Distributed inference using multiple sensors has been an active area of research since the emergence of wireless sensor networks (WSNs). Several researchers have addressed the design issues to ensure optimal inference performance in such networks. The central goal of this thesis is to analyze distributed inference systems with potentially unreliable components and design strategies to ensure reliable inference in such systems. The inference process can be that of detection or estimation or classification, and the components/agents in the system can be sensors and/or humans. The system components can be unreliable due to a variety of reasons: faulty sensors, security attacks causing sensors to send falsified information, or unskilled human workers sending imperfect information. This thesis first quantifies the effect of such unreliable agents on the inference performance of the network and then designs schemes that ensure a reliable overall inference. In the first part of this thesis, we study the case when only sensors are present in the system, referred to as sensor networks. For sensor networks, the presence of malicious sensors, referred to as Byzantines, are considered. Byzantines are sensors that inject false information into the system. In such systems, the effect of Byzantines on the overall inference performance is characterized in terms of the optimal attack strategies. Game-theoretic formulations are explored to analyze two-player interactions. Next, Byzantine mitigation schemes are designed that address the problem from the system\u27s perspective. These mitigation schemes are of two kinds: Byzantine identification schemes and Byzantine tolerant schemes. Using learning based techniques, Byzantine identification schemes are designed that learn the identity of Byzantines in the network and use this information to improve system performance. When such schemes are not possible, Byzantine tolerant schemes using error-correcting codes are developed that tolerate the effect of Byzantines and maintain good performance in the network. Error-correcting codes help in correcting the erroneous information from these Byzantines and thereby counter their attack. The second line of research in this thesis considers humans-only networks, referred to as human networks. A similar research strategy is adopted for human networks where, the effect of unskilled humans sharing beliefs with a central observer called \emph{CEO} is analyzed, and the loss in performance due to the presence of such unskilled humans is characterized. This problem falls under the family of problems in information theory literature referred to as the \emph{CEO Problem}, but for belief sharing. The asymptotic behavior of the minimum achievable mean squared error distortion at the CEO is studied in the limit when the number of agents $L$ and the sum rate $R$ tend to infinity. An intermediate regime of performance between the exponential behavior in discrete CEO problems and the $1/R$ behavior in Gaussian CEO problems is established. This result can be summarized as the fact that sharing beliefs (uniform) is fundamentally easier in terms of convergence rate than sharing measurements (Gaussian), but sharing decisions is even easier (discrete). Besides theoretical analysis, experimental results are reported for experiments designed in collaboration with cognitive psychologists to understand the behavior of humans in the network. The act of fusing decisions from multiple agents is observed for humans and the behavior is statistically modeled using hierarchical Bayesian models. The implications of such modeling on the design of large human-machine systems is discussed. Furthermore, an error-correcting codes based scheme is proposed to improve system performance in the presence of unreliable humans in the inference process. For a crowdsourcing system consisting of unskilled human workers providing unreliable responses, the scheme helps in designing easy-to-perform tasks and also mitigates the effect of erroneous data. The benefits of using the proposed approach in comparison to the majority voting based approach are highlighted using simulated and real datasets. In the final part of the thesis, a human-machine inference framework is developed where humans and machines interact to perform complex tasks in a faster and more efficient manner. A mathematical framework is built to understand the benefits of human-machine collaboration. Such a study is extremely important for current scenarios where humans and machines are constantly interacting with each other to perform even the simplest of tasks. While machines perform best in some tasks, humans still give better results in tasks such as identifying new patterns. By using humans and machines together, one can extract complete information about a phenomenon of interest. Such an architecture, referred to as Human-Machine Inference Networks (HuMaINs), provides promising results for the two cases of human-machine collaboration: \emph{machine as a coach} and \emph{machine as a colleague}. For simple systems, we demonstrate tangible performance gains by such a collaboration which provides design modules for larger, and more complex human-machine systems. However, the details of such larger systems needs to be further explored

Transformation in three American orchestras: an analysis of labor, agency, and change

Orchestras across the United States have always struggled to maintain balanced budgets as nonprofits dependent on philanthropists and public funds. Consequently, it is normal for orchestra musicians to struggle with job insecurity and financial uncertainty to some degree. While the industry is no stranger to labor disputes, the last decade marked a notable shift in the character of labor negotiations that caused an unprecedented trend of lockouts – the refusal of employees to the workplace until a contract is reached. The orchestras that successfully reached a contract did not come out the other side unchanged; there was significant upheaval in the organizations both ideologically and structurally. My research explored the musicians\u27 experience with lockouts and restructuring in the Minnesota Orchestra, Louisville Orchestra, and the Atlanta Symphony Orchestra. The findings from this research detail the common experience of locked out orchestra musicians, how orchestra musicians have effectively influenced changes to the organizational structure, and the nature of these structural and ideological changes. Methods included qualitative interviews and survey. This research contributes to the gap in the literature around the experience of the workforce in this industry, and to broader conversations of art performance as labor and the future of American orchestras. The findings will be made available to select members of orchestra boards and administrations with musicians’ consent in the spirit of improving understanding, communication, and operations in the orchestra

Analysis of leisure tourism in Peru during the COVID-19 Pandemic

This study analyzes the economic impact caused by the COVID-19 pandemic on leisure tourism in Peru, in terms of tourist services such as means of transportation, accommodation, city tours and restaurants. The analysis is based on the application of a survey. The cross-sectional analytical study evaluated 2,443 potential tourists of legal age from the 25 regions of Peru who intended to engage in leisure tourism during the quarantine period. Likewise, potential tourists who intended to make a trip culminating the quarantine (in the remaining time of the year 2020) were evaluated. Based on the study of people who were going to carry out local leisure tourism until the end of 2020, the estimated losses in tour- ist services amounted to approximately $203 dollars millions (based on a budget less than or equal to$83.00, the amount that most respondents were willing to pay for tourist services). An increase in the demand for domestic leisure tourism is expected associated with the reduction in prices of each tourist service in order to reactivate this sector economically

Spartan Daily, March 26, 1987

Volume 88, Issue 41https://scholarworks.sjsu.edu/spartandaily/7567/thumbnail.jp