Off-line Thai handwriting recognition in legal amount

Thai handwriting in legal amounts is a challenging problem and a new field in the area of handwriting recognition research. The focus of this thesis is to implement Thai handwriting recognition system. A preliminary data set of Thai handwriting in legal amounts is designed. The samples in the data set are characters and words of the Thai legal amounts and a set of legal amounts phrases collected from a number of native Thai volunteers. At the preprocessing and recognition process, techniques are introduced to improve the characters recognition rates. The characters are divided into two smaller subgroups by their writing levels named body and high groups. The recognition rates of both groups are increased based on their distinguished features. The writing level separation algorithms are implemented using the size and position of characters. Empirical experiments are set to test the best combination of the feature to increase the recognition rates. Traditional recognition systems are modified to give the accumulative top-3 ranked answers to cover the possible character classes. At the postprocessing process level, the lexicon matching algorithms are implemented to match the ranked characters with the legal amount words. These matched words are joined together to form possible choices of amounts. These amounts will have their syntax checked in the last stage. Several syntax violations are caused by consequence faulty character segmentation and recognition resulting from connecting or broken characters. The anomaly in handwriting caused by these characters are mainly detected by their size and shape. During the recovery process, the possible word boundary patterns can be pre-defined and used to segment the hypothesis words. These words are identified by the word recognition and the results are joined with previously matched words to form the full amounts and checked by the syntax rules again. From 154 amounts written by 10 writers, the rejection rate is 14.9 percent with the recovery processes. The recognition rate for the accepted amount is 100 percent

IFRSとのコンバージェンスが価値関連性に与える影響 : 中国資本市場からの証拠

早大学位記番号:新8486早稲田大

Combination of Local and Global Vision Modelling for Arabic Handwritten Words Recognition

Colloque avec actes et comité de lecture. internationale.International audienceWe propose in this paper a recognition system of Arabic hand-written words issued from literal amounts of Arabic checks. This system is based on the concept of PERCEPTRO developed by M. Côté for Latin word recognition. It is a specific NN, named Transparent Neural Network (TNN), combining a global and a local vision modelling (GVM - LVM) of the word. In the forward propagation movement, the former (GVM) proposes a list of structural features characterising the presence of some letters in the word. GVM proposes a list of possible letters and words containing these characteristics. Then, in the back-propagation movement, these letters are confirmed or not according to their proximity with corresponding printed letters. The correspondence between the letter shapes and the corresponding printed letters is performed by LVM using the correspondence of their Fourier descriptors, playing the role of a letter shape normalizer

Cross-listing and valuation differences between the Hong Kong and the Chinese stock markets

PURPOSE OF THE STUDY The purpose of this study is to investigate cross-listing and valuation differences between the Chinese and the Hong Kong stock markets. Majority of the cross-listing literature is focused on US and UK stock markets due to the large amount of cross-listings. However, there has been considerable cross-listing activity from China to Hong Kong since the beginning of the 21st century. In addition, the Chinese stock market is relatively young and has many restrictions originating from the socialist history of the country. Therefore the cross-listings from China to Hong Kong offer an interesting framework to study the phenomenon. The main focus of this study is to find out whether cross-listings add and whether their share classes in Hong Kong reach the valuation of Hong Kong peers. In the academic literature related to the cross-listing these issues are called ‘cross-listing premium’ and ‘cross-listing discount’, respectively. DATA AND METHODOLOGY The data set consists of all the companies listed in the Shanghai Stock Exchange, the Shenzhen Stock Exchange and the Hong Kong Stock Exchange between 2001 and 2010. When the data was retrieved the amounts of companies listed in the exchanges were in total 970, 1419 and 1479. The main focus is on the 164 H-shares. H-shares are stocks of the Chinese companies listed on the Hong Kong Stock Exchange. 70 of those companies have cross-listing; listing in Shanghai/Shenzhen as well as in Hong Kong. Following previous literature, Tobin’s Q is utilized to act as a proxy for company valuation. There are four hypotheses constructed based on the previous literature and then hypotheses are tested with OLS regression. The model is controlled with a set of independent variables influencing company valuation. All the data was retrieved from Thomson One Banker and Datastream. RESULTS The set of hypotheses are mainly supported by the results. The first hypothesis and the test cover the relative valuation between the Chinese and the Hong Kong stock markets. The Chinese stock markets are found to be more highly valued when compared to the pure Hong Kong companies in the Hong Kong Stock Exchange. The cross-listed H-shares offer a possibility to study the valuation of different share classes of the same companies. Second hypothesis states that the Chinese share class is more expensive and the tests give support for the hypothesis. Interestingly evidence indicates that the same company, with same cash-flow and voting rights, is valued differently within the Chinese and the Hong Kong stock markets. Third hypothesis tests whether cross-listing is value adding in China. The results support the hypothesis. However, when the test covers only large companies the cross-listing premium seems not to exist. Last hypothesis assumes that the H-shares do not reach the valuation of other Hong Kong shares. The tests give support for the hypothesis and there is evidence for the cross-listing discount in China and Hong Kong framework

Three Essays on Cross-Listing

This dissertation examines the role of cross-listing in shaping corporate earnings quality, stock price informativeness, and firm valuation, as well as its impact on a listing firm\u27s home country information asymmetry and stock misvaluation. The first essay addresses the information asymmetry between Chinese local A-share and foreign B-share markets and its impact on the B-share discount puzzle. In contrast with the widespread belief that domestic investors are better informed than foreign investors, this study indicates that foreign investors actually possess more value-relevant, firm-specific information in an emerging market such as China, where information transparency and investor protection are relatively weak. As such, the observed B-share discount is not compensation for the informational disadvantage of foreign investors but, rather, the result of a downward price correction effect. The second essay examines the impact of cross-listing on corporate earnings management, price informativeness, and firm value, contingent upon increased market integration. Consistent with the bonding hypothesis, cross-listed firms are found to have better earnings quality, more informative stock prices, and higher valuation than non-cross-listed firms, even though the divergence between the two groups of firms has been less evident since the regulatory reforms of the Chinese stock market liberalization. The third essay investigates the role of U.S. listing in mitigating a listing firm\u27s home country information asymmetry and stock misvaluation. In contrast with conventional theories that predict enormous cross-listing benefits, this study finds no significant cross-listing premiums. Further investigation indicates that the absence of cross-listing premiums for Chinese firms is mainly a result of a downward price correction (toward the fundamental values of the stocks) once U.S. listing allows for an enhanced capitalization of firm-specific information. In particular, I find that firms with U.S. listings have more informative and less overvalued stock prices than comparable home country firms and that exchange-based U.S. listings result in more informative and more accurately valued stocks than non-exchange-based listings. The empirical findings of these studies suggest a consistent story: cross-listing on a more regulated market plays an important role in inducing better corporate governance and more transparent information environments, even in today\u27s increasingly integrated world

Recognition of off-line arabic handwritten dates and numeral strings

In this thesis, we present an automatic recognition system for CENPARMI off-line Arabic handwritten dates collected from Arabic Nationalities. This system consists of modules that segment and recognize an Arabic handwritten date image. First, in the segmentation module, the system explicitly segments a date image into a sequence of basic constituents or segments. As a part of this module, a special sub-module was developed to over-segment any constituent that is a candidate for a touching pair. The proposed touching pair segmentation submodule has been tested on three different datasets of handwritten numeral touching pairs: The CENPARMI Arabic [6], Urdu, and Dari [24] datasets. The final recognition rates of 92.22%, 90.43%, and 86.10% were achieved for Arabic, Urdu and Dari, respectively. Afterwards, the segments are preprocessed and sent to the classification module. In this stage, feature vectors are extracted and then recognized by an isolated numeral classifier. This recognition system has been tested in five different isolated numeral databases: The CENPARMI Arabic [6], Urdu, Dari [24], Farsi, and Pashto databases with overall recognition rates of 97.29% 97.75%, 97.75%, 97.95% and 98.36%, respectively. Finally, a date post processing module is developed to improve the recognition results. This post processing module is used in two different stages. First, in the date stage, to verify that the segmentation/recognition output represents a valid date image and it chooses the best date format to be assigned to this image. Second, in the sub-field stage, to evaluate the values for the date three parts: day, month and year. Experiments on two different databases of Arabic handwritten dates: CENPARMI Arabic database [6] and the CENPARMI Arabic Bank Cheques database [7], show encouraging results with overall recognition rates of 85.05% and 66.49, respectively

The Sovereign-Debt Listing Puzzle

The claim that stock exchanges perform certification and monitoring roles in securities offerings is pervasive in the legal and financial literatures. This article tests the validity of this “bonding hypothesis” in the sovereign-bond market—one of the oldest and largest securities markets in the world. Using data on sovereign-bond listings for the entire post-World War II period, we provide the first comprehensive report on sovereigns’ historical listing patterns. We then test whether a sovereign bond issue’s listing jurisdiction affects its yield at issuance, as the bonding hypothesis would predict. We find little evidence of bonding in today’s sovereign-debt market. Instead, we hypothesize that sovereign-bond listings are primarily a form of regulatory arbitrage. Because certain investors may be restricted to investing abroad only in listed securities, sovereigns are incentivized to list their bonds, but to seek out the least restrictive exchange that qualifies