Risk, return, and investor behavior in the Chinese equity market

This thesis comprises two chapters with a focus on volatility estimating, modeling and forecasting using intraday data in the Chinese stock market. The first chapter explores the performance of two types of estimators in volatility prediction: the realized volatility (RV) type and duration-based ones. This is motivated by the theoretical and empirical support for both categories of estimators that are distinct from each other. I use intraday data for 203 component stocks in the CSI 300 index and adopt a combination of volatility models and these two types of estimators to produce 1-, 5- and 22-day ahead forecasts. I show that, although empirically more efficient with US data, the duration-based volatility estimators fail to compete statistically with the traditional RV-type although in a portfolio setting both types of estimators generate similar economic value to a mean-variance investor. A comprehensive simulation exercise is undertaken to rationalize the poorer statistical performance of duration-based estimators. In the second chapter, I use daily and intraday data to examine the impact of crosssectional return dispersion on volatility forecasting in the Chinese equity market. I adopt traditional GARCH and HAR models and, by augmenting them with return dispersion measures, provide evidence that the return dispersion exhibits substantial information in describing the volatility dynamics by generating significantly lower forecasting errors at market and industry levels. Furthermore, the information content of the return dispersion tends to offer economic gain to a mean-variance utility investor. The findings are robust with respect to alternative volatility proxies and weighting scheme in constructing industry indices

Trends in Regional Inequality in China

Several recent studies have examined the tendency of regions within a nation to exhibit long-term convergence in per capita income levels. Barro and Sala-i-Martin (1991, 1992, 1995) have found a tendency towards convergence among the U.S. states, among Japanese prefectures, and among regions within Western Europe. In this paper we examine the tendency towards convergence among the provinces of China during the period 1952-1993. We find that real income convergence of provinces in China has been a relatively recent phenomenon, emerging strongly only since the reform period began in 1978. During the initial phase of central planning, 1952-1965, there is some evidence for convergence, but it is weak and sensitive to the time period being analyzed. During the cultural revolution, 1965- 1978, there is strong evidence of divergence rather than convergence. We find strong evidence for convergence during the reform period is associated with rural reforms, and is especially strong within the coastal regions where there has been liberalization of international trade and investment flows. However, since 1990 regional incomes have begun to diverge. Such a divergence is entirely explained by the variance between the coastal and interior provinces, rather than increase in variance within each other. Therefore, it seems that China is now on a dual track, with a prosperous and fast growing coastal region and a poor interior growing at a lower rate.

Economic Reforms in China and India: Selected Issues in Industrial Policy

In this paper we compare the reform experiences of China and India focusing on three specific areas of industrial policy. We begin with a comparison of the macro economic performance of the two countries and find that except on the inflation front China is better placed than India. China has grown at almost double the rate of India largely because of very high savings and investment rates. Of course, economic reforms have provided the necessary policy environment for China to attain and sustain high growth. First we compare the extent of deregulation in China and India in the areas of prices, labor and land laws, and exit policy for firms. We find that the non-state sector of China, which is the main driving force of China's impressive growth has performed so well largely because it operates primarily under market conditions. China has gone far ahead of India in respect of deregulation of their non-state sector. Price reforms have been extensive, labor is mobile, labor laws are liberal, and firms are free to enter and exit from the market. The results achieved in the process speak for themselves. In India, by contrast, much needs to be done for private sector deregulation, price reform has been limited, labor and land laws are stringent, and while firms have no entry barriers, they do, however, face strong exit barriers. Second, we draw comparison between the Chinese township and village enterprises and the Indian small scale industries. While these have been promoted in both the countries through preferential policies, however, their objectives have been different, and hence the results. Chinese small enterprises are given initial support only as against the policy in India wherein incentives are available as long as a firm remains in small scale. Moreover, in India, items are reserved to be produced exclusively by the small scale industry. While both have grown overtime, the ones in China have grown much faster. Finally, we compare special economic zones of China with export processing zones of India. We find that although both the countries have offered similar incentives to prospective investors, the story is very different in the two countries. The zones in China have been highly successful in attracting foreign investment, promoting exports, and generating employment than they have been in India. We identify a number of factors for the performance differentials in the two countries

Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning

Deep reinforcement learning (DRL) has been increasingly employed to handle the dynamic and complex resource management in network slicing. The deployment of DRL policies in real networks, however, is complicated by heterogeneous cell conditions. In this paper, we propose a novel transfer learning (TL) aided multi-agent deep reinforcement learning (MADRL) approach with inter-agent similarity analysis for inter-cell inter-slice resource partitioning. First, we design a coordinated MADRL method with information sharing to intelligently partition resource to slices and manage inter-cell interference. Second, we propose an integrated TL method to transfer the learned DRL policies among different local agents for accelerating the policy deployment. The method is composed of a new domain and task similarity measurement approach and a new knowledge transfer approach, which resolves the problem of from whom to transfer and how to transfer. We evaluated the proposed solution with extensive simulations in a system-level simulator and show that our approach outperforms the state-of-the-art solutions in terms of performance, convergence speed and sample efficiency. Moreover, by applying TL, we achieve an additional gain over 27% higher than the coordinate MADRL approach without TL.Comment: 6 pages, 8 figures, IEEE Global Communications Conference 202

MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition

Multilingual text recognition (MLTR) systems typically focus on a fixed set of languages, which makes it difficult to handle newly added languages or adapt to ever-changing data distribution. In this paper, we propose the Incremental MLTR (IMLTR) task in the context of incremental learning (IL), where different languages are introduced in batches. IMLTR is particularly challenging due to rehearsal-imbalance, which refers to the uneven distribution of sample characters in the rehearsal set, used to retain a small amount of old data as past memories. To address this issue, we propose a Multiplexed Routing Network (MRN). MRN trains a recognizer for each language that is currently seen. Subsequently, a language domain predictor is learned based on the rehearsal set to weigh the recognizers. Since the recognizers are derived from the original data, MRN effectively reduces the reliance on older data and better fights against catastrophic forgetting, the core issue in IL. We extensively evaluate MRN on MLT17 and MLT19 datasets. It outperforms existing general-purpose IL methods by large margins, with average accuracy improvements ranging from 10.3% to 35.8% under different settings. Code is available at https://github.com/simplify23/MRN.Comment: Accepted by ICCV 202

Inter-Cell Slicing Resource Partitioning via Coordinated Multi-Agent Deep Reinforcement Learning

Network slicing enables the operator to configure virtual network instances for diverse services with specific requirements. To achieve the slice-aware radio resource scheduling, dynamic slicing resource partitioning is needed to orchestrate multi-cell slice resources and mitigate inter-cell interference. It is, however, challenging to derive the analytical solutions due to the complex inter-cell interdependencies, interslice resource constraints, and service-specific requirements. In this paper, we propose a multi-agent deep reinforcement learning (DRL) approach that improves the max-min slice performance while maintaining the constraints of resource capacity. We design two coordination schemes to allow distributed agents to coordinate and mitigate inter-cell interference. The proposed approach is extensively evaluated in a system-level simulator. The numerical results show that the proposed approach with inter-agent coordination outperforms the centralized approach in terms of delay and convergence. The proposed approach improves more than two-fold increase in resource efficiency as compared to the baseline approach