The Informational Content Of Changes In Stock Recommendation: Chaebol Vs. Non-Chaebol Affiliated Analysts

Accurate analysts’ reports alleviate information asymmetry between companies and investors by providing accounting information that is useful in investment decision-making for market participants. Investors evaluate the credibility of stock recommendations based on the accuracy of the earnings forecasts of analysts, applying them in the decision-making process. Studies of stock recommendations have focused on their informational content, systematically analyzing the characteristics of recommendations and, to a lesser degree, decision-making factors. For most analysts, when stock recommendations and forecast changes are simultaneously disclosed, a large bias results if analysts fail to consider the magnitude of the market reaction relative to the earnings forecast and stock recommendations. In most previous studies, the informational content of both individual stock recommendations and changes in stock recommendations was investigated. In this study, we examine differences in the informational content depending on the stock recommendations of the report released immediately previous to the current report for the same recommendation. An upgraded (or downgraded) revision within the same recommendation category is associated with a greater (lower) stock price return. Even the same recommendation in the market may cause different reactions depending on both the recommendation itself and on the direction of change of the recommendation. Affiliated analysts have more access to inside information of the companies they analyze. The stock returns after revisions of Chaebol-affiliated analysts are significantly higher than those of non-Chaebol-affiliated analysts

A Literature Review On Chief Executive Officer Hubris And Related Constructs: Is The Theory Of Chief Executive Officer Hubris An Antecedents Or Consequences?

This paper reviews the theory of Chief Executive Officer hubris and related constructs. It is to identify the area of Chief Executive Officer hubris clearly and to clarify the confusion of related constructs which includes: overconfidence, Chief Executive Officer celebrity, and narcissism. We examined the four related constructs comprehensively and evaluated Chief Executive Officer hubris construct as an antecedent or consequence. Throughout the research mainstream, these research related constructs often use the word hubris interchangeably. Many researchers are confused whether Chief Executive Officer hubris is an antecedent or consequence? We will attempt to resolve this issue by examining antecedents and consequences of Chief Executive Officer hubris and related constructs. Furthermore, suggestions and implications for future research based on Chief Executive Officer hubris will be assessed.

A 6.78MHz Adaptive-ZVS Class-D PA with Dynamic Dead-Time for Wireless Power Transfer system

Department of Electrical EngineeringIn this thesis, a class-D power amplifier (PA) with adaptive zero-voltage switching (A-ZVS) technique for Low power 6.78 MHz resonant wireless power transfer (R-WPT) system is proposed. In R-WPT operation, the loading impedance of a PA can be varied by the process tolerance of the LC resonant components and WPT environments, such as the resonant topology, coupling coefficient and loading condition of the receiver. The proposed A-ZVS feedback loop of PA calibrates the equivalent resonant capacitance using PWM-controlled switched capacitor in real-time to achieve ZVS by adjusting the loading impedance to be slightly inductive. Furthermore, the proposed PA adjust the dead-time according to variation of WPT environments. The proposed PA was fully integrated except for one switched capacitor used as the tuning element and fabricated in a TSMC 0.18um BCD process. The measurement results demonstrated robust ZVS operation with a peak system efficiency of 52.7% and an enhanced maximum transmitting power of 107%.ope

The Impact of Cultural Distance on the Performance of Foreign Subsidiaries: Evidence From the Korean Market

This study investigates whether the cultural distance between Korea and the home countries of foreign subsidiaries in Korea affects the subsidiaries’ financial performance. It contributes to the literature on international business in that it sheds light on cultural distance, a well-established but somewhat neglected concept in international business. Unlike most of the previous studies that have used cultural distance as a control or moderating variable, this study uses it as an independent variable in the context of globalization through foreign direct investments in Korea. Focusing on the possible positive side of broad cultural distance, we hypothesize that the performances of foreign subsidiaries are likely to be better when the cultural distance between their home countries and Korea increases. To test our hypothesis, we have conducted an empirical analysis, using data collected from 472 foreign subsidiaries doing business in Korea. The results support our argument that cultural distance has a positive impact on financial performance. This study finds that having cultural similarities with a foreign market does not guarantee success. Instead, it shows that firms can gain opportunities when incorporating in a foreign national market with broad cultural distance.&nbsp

Incorporating Language-Driven Appearance Knowledge Units with Visual Cues in Pedestrian Detection

Large language models (LLMs) have shown their capability in understanding contextual and semantic information regarding appearance knowledge of instances. In this paper, we introduce a novel approach to utilize the strength of an LLM in understanding contextual appearance variations and to leverage its knowledge into a vision model (here, pedestrian detection). While pedestrian detection is considered one of crucial tasks directly related with our safety (e.g., intelligent driving system), it is challenging because of varying appearances and poses in diverse scenes. Therefore, we propose to formulate language-driven appearance knowledge units and incorporate them with visual cues in pedestrian detection. To this end, we establish description corpus which includes numerous narratives describing various appearances of pedestrians and others. By feeding them through an LLM, we extract appearance knowledge sets that contain the representations of appearance variations. After that, we perform a task-prompting process to obtain appearance knowledge units which are representative appearance knowledge guided to be relevant to a downstream pedestrian detection task. Finally, we provide plentiful appearance information by integrating the language-driven knowledge units with visual cues. Through comprehensive experiments with various pedestrian detectors, we verify the effectiveness of our method showing noticeable performance gains and achieving state-of-the-art detection performance.Comment: 11 pages, 4 figures, 9 table

Unsupervised Speech Representation Pooling Using Vector Quantization

With the advent of general-purpose speech representations from large-scale self-supervised models, applying a single model to multiple downstream tasks is becoming a de-facto approach. However, the pooling problem remains; the length of speech representations is inherently variable. The naive average pooling is often used, even though it ignores the characteristics of speech, such as differently lengthed phonemes. Hence, we design a novel pooling method to squash acoustically similar representations via vector quantization, which does not require additional training, unlike attention-based pooling. Further, we evaluate various unsupervised pooling methods on various self-supervised models. We gather diverse methods scattered around speech and text to evaluate on various tasks: keyword spotting, speaker identification, intent classification, and emotion recognition. Finally, we quantitatively and qualitatively analyze our method, comparing it with supervised pooling methods

Learning to Discriminate Information for Online Action Detection

From a streaming video, online action detection aims to identify actions in the present. For this task, previous methods use recurrent networks to model the temporal sequence of current action frames. However, these methods overlook the fact that an input image sequence includes background and irrelevant actions as well as the action of interest. For online action detection, in this paper, we propose a novel recurrent unit to explicitly discriminate the information relevant to an ongoing action from others. Our unit, named Information Discrimination Unit (IDU), decides whether to accumulate input information based on its relevance to the current action. This enables our recurrent network with IDU to learn a more discriminative representation for identifying ongoing actions. In experiments on two benchmark datasets, TVSeries and THUMOS-14, the proposed method outperforms state-of-the-art methods by a significant margin. Moreover, we demonstrate the effectiveness of our recurrent unit by conducting comprehensive ablation studies.Comment: To appear in CVPR 202