I test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. Using thirty-six industry portfolios and the broad market index as our test assets, I establish several key results. First, a number of industries such as semiconductor, electronics, metal, and petroleum lead the stock market by up to one month. In contrast, the market, which is widely followed, only leads a few industries. Importantly, an industry's ability to lead the market is correlated with its propensity to forecast various indicators of economic activity such as industrial production growth. Consistent with our hypothesis, these findings indicate that the market reacts with a delay to information in industry returns about its fundamentals because information diffuses only gradually across asset markets. Traditional theories of asset pricing assume that investors have unlimited information-processing capacity. However, this assumption does not hold for many traders, even the most sophisticated ones. Many economists recognize that investors are better characterized as being only boundedly rational(see Shiller(2000), Sims(2201)). Even from casual observation, few traders can pay attention to all sources of information much less understand their impact on the prices of assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource(see, eg., Kahneman(1973), Nisbett and Ross(1980), Fiske and Taylor(1991)). A number of papers have explored the implications of limited information- processing capacity for asset prices. I will review this literature in Section II. For instance, Merton(1987) develops a static model of multiple stocks in which investors only have information about a limited number of stocks and only trade those that they have information about. Related models of limited market participation include brennan(1975) and Allen and Gale(1994). As a result, stocks that are less recognized by investors have a smaller investor base(neglected stocks) and trade at a greater discount because of limited risk sharing. More recently, Hong and Stein(1999) develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. Hong and Stein(1999). My hypothesis is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset reaches investors in other markets only with a lag, i.e. news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors may not pay attention or be able to extract the information from the asset prices of markets that they do not participate in. These two assumptions taken together leads to cross-asset return predictability. My hypothesis would appear to be a very plausible one for a few reasons. To begin with, as pointed out by Merton(1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets. Indeed, even among equity money managers, there is specialization along industries such as sector or market timing funds. Some reasons for this limited market participation include tax, regulatory or liquidity constraints. More plausibly, investors have to specialize because they have their hands full trying to understand the markets that they do participate in
International circulation of cultural assets involves numerous countries thereby making an approach based on international law essential to resolving this problem. Since the end of the
Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.
Ancestral Burial Ground on the Inwangsan Mountain is a ten-panel folding screen with images and postscripts. Commissioned by Bak Gyeong-bin (dates unknown), this screen was painted by Jo Jung-muk (1820-after 1894) in 1868. The postscripts were written by Hong Seon-ju (dates unknown). The National Museum of Korea restored this painting, which had been housed in the museum on separate sheets, to its original folding screen format. The museum also opened the screen to the public for the first time at the special exhibition Through the Eyes of Joseon Painters: Real Scenery Landscapes of Korea held from July 23 to September 22, 2019. Ancestral Burial Ground on the Inwangsan Mountain depicts real scenery on the western slopes of Inwangsan Mountain spanning present-day Hongje-dong and Hongeun-dong in Seodaemun-gu, Seoul. In the distance, the Bukhansan Mountain ridges are illustrated. The painting also bears place names, including Inwangsan Mountain, Chumohyeon Hill, Hongjewon Inn, Samgaksan Mountain, Daenammun Gate, and Mireukdang Hall. The names and depictions of these places show similarities to those found on late Joseon maps. Jo Jung-muk is thought to have studied the geographical information marked on maps so as to illustrate a broad landscape in this painting. Field trips to the real scenery depicted in the painting have revealed that Jo exaggerated or omitted natural features and blended and arranged them into a row for the purposes of the horizontal picture plane. Jo Jung-muk was a painter proficient at drawing conventional landscapes in the style of the Southern School of Chinese painting. Details in Ancestral Burial Ground on the Inwangsan Mountain reflect the painting style of the School of Four Wangs. Jo also applied a more decorative style to some areas. The nineteenth-century court painters of the Dohwaseo(Royal Bureau of Painting), including Jo, employed such decorative painting styles by drawing houses based on painting manuals, applying dots formed like sprinkled black pepper to depict mounds of earth and illustrating flowers by dotted thick pigment. Moreover, Ancestral Burial Ground on the Inwangsan Mountain shows the individualistic style of Jeong Seon(1676~1759) in the rocks drawn with sweeping brushstrokes in dark ink, the massiveness of the mountain terrain, and the pine trees simply depicted using horizontal brushstrokes. Jo Jung-muk is presumed to have borrowed the authority and styles of Jeong Seon, who was well-known for his real scenery landscapes of Inwangsan Mountain. Nonetheless, the painting lacks an spontaneous sense of space and fails in conveying an impression of actual sites. Additionally, the excessively grand screen does not allow Jo Jung-muk to fully express his own style. In Ancestral Burial Ground on the Inwangsan Mountain, the texts of the postscripts nicely correspond to the images depicted. Their contents can be divided into six parts: (1) the occupant of the tomb and the reason for its relocation; (2) the location and geomancy of the tomb; (3) memorial services held at the tomb and mysterious responses received during the memorial services; (4) cooperation among villagers to manage the tomb; (5) the filial piety of Bak Gyeong-bin, who commissioned the painting and guarded the tomb; and (6) significance of the postscripts. The second part in particular is faithfully depicted in the painting since it can easily be visualized. According to the fifth part revealing the motive for the production of the painting, the commissioner Bak Gyeongbin was satisfied with the painting, stating that "it appears impeccable and is just as if the tomb were newly built." The composition of the natural features in a row as if explaining each one lacks painterly beauty, but it does succeed in providing information on the geomantic topography of the gravesite. A fair number of the existing depictions of gravesites are woodblock prints of family gravesites produced after the eighteenth century. Most of these are included in genealogical records and anthologies. According to sixteenth- and seventeenth-century historical records, hanging scrolls of family gravesites served as objects of worship. Bowing in front of these paintings was considered a substitute ritual when descendants could not physically be present to maintain their parents' or other ancestors' tombs. Han Hyo-won (1468-1534) and Jo Sil-gul (1591-1658) commissioned the production of family burial ground paintings and asked distinguished figures of the time to write a preface for the paintings, thus showing off their filial piety. Such examples are considered precedents for Ancestral Burial Ground on the Inwangsan Mountain. Hermitage of the Recluse Seokjeong in a private collection and Old Villa in Hwagae County at the National Museum of Korea are not paintings of family gravesites. However, they serve as references for seventeenth-century paintings depicting family gravesites in that they are hanging scrolls in the style of the paintings of literary gatherings and they illustrate geomancy. As an object of worship, Ancestral Burial Ground on the Inwangsan Mountain recalls a portrait. As indicated in the postscripts, the painting made Bak Gyeong-bin "feel like hearing his father's cough and seeing his attitudes and behaviors with my eyes." The fable of Xu Xiaosu, who gazed at the portrait of his father day and night, is reflected in this gravesite painting evoking a deceased parent. It is still unclear why Bak Gyeong-bin commissioned Ancestral Burial Ground on the Inwangsan Mountain to be produced as a real scenery landscape in the folding screen format rather than a hanging scroll or woodblock print, the conventional formats for a family gravesite paintings. In the nineteenth century, commoners came to produce numerous folding screens for use during the four rites of coming of age, marriage, burial, and ancestral rituals. However, they did not always use the screens in accordance with the nature of these rites. In the Ancestral Burial Ground on the Inwangsan Mountain, the real scenery landscape appears to have been emphasized more than the image of the gravesite in order to allow the screen to be applied during different rituals or for use to decorate space. The burial mound, which should be the essence of Ancestral Burial Ground on the Inwangsan Mountain, might have been obscured in order to hide its violation of the prohibition on the construction of tombs on the four mountains around the capital. At the western foot of Inwangsan Mountain, which was illustrated in this painting, the construction of tombs was forbidden. In 1832, a tomb discovered illegally built on the forbidden area was immediately dug up and the related people were severely punished. This indicates that the prohibition was effective until the mid-nineteenth century. The postscripts on the Ancestral Burial Ground on the Inwangsan Mountain document in detail Bak Gyeong-bin's efforts to obtain the land as a burial site. The help and connivance of villagers were necessary to use the burial site, probably because constructing tombs within the prohibited area was a burden on the family and villagers. Seokpajeong Pavilion by Yi Han-cheol (1808~1880), currently housed at the Los Angeles County Museum of Art, is another real scenery landscape in the format of a folding screen that is contemporaneous and comparable with Ancestral Burial Ground on the Inwangsan Mountain. In 1861 when Seokpajeong Pavilion was created, both Yi Han-cheol and Jo Jung-muk participated in the production of a portrait of King Cheoljong. Thus, it is highly probable that Jo Jung-muk may have observed the painting process of Yi's Seokpajeong Pavilion. A few years later, when Jo Jungmuk was commissioned to produce Ancestral Burial Ground on the Inwangsan Mountain, his experience with the impressive real scenery landscape of the Seokpajeong Pavilion screen could have been reflected in his work. The difference in the painting style between these two paintings is presumed to be a result of the tastes and purposes of the commissioners. Since Ancestral Burial Ground on the Inwangsan Mountain contains the multilayered structure of a real scenery landscape and family gravesite, it seems to have been perceived in myriad different ways depending on the viewer's level of knowledge, closeness to the commissioner, or viewing time. In the postscripts to the painting, the name and nickname of the tomb occupant as well as the place of his surname are not recorded. He is simply referred to as "Mister Bak." Biographical information about the commissioner Bak Gyeong-bin is also unavailable. However, given that his family did not enter government service, he is thought to have been a person of low standing who could not become a member of the ruling elite despite financial wherewithal. Moreover, it is hard to perceive Hong Seon-ju, who wrote the postscripts, as a member of the nobility. He might have been a low-level administrative official who belonged to the Gyeongajeon, as documented in the Seungjeongwon ilgi (Daily Records of Royal Secretariat of the Joseon Dynasty). Bak Gyeong-bin is presumed to have moved the tomb of his father to a propitious site and commissioned Ancestral Burial Ground on the Inwangsan Mountain to stress his filial piety, a conservative value, out of his desire to enter the upper class. However, Ancestral Burial Ground on the Inwangsan Mountain failed to live up to its original purpose and ended up as a contradictory image due to its multiple applications and the concern over the exposure of the violation of the prohibition on the construction of tombs on the prohibited area. Forty-seven years after its production, this screen became a part of the collection at the Royal Yi Household Museum with each panel being separated. This suggests that Bak Gyeong-bin's dream of bringing fortune and raising his family's social status by selecting a propitious gravesite did not come true.