Learning equilibrium mean-variance strategy

We study a dynamic mean-variance portfolio optimization problem under the reinforcement learning framework, where an entropy regularizer is introduced to induce exploration. Due to the time–inconsistency involved in a mean-variance criterion, we aim to learn an equilibrium policy. Under an incomplete market setting, we obtain a semi-analytical, exploratory, equilibrium mean-variance policy that turns out to follow a Gaussian distribution. We then focus on a Gaussian mean return model and propose a reinforcement learning algorithm to find the equilibrium policy. Thanks to a thoroughly designed policy iteration procedure in our algorithm, we prove the convergence of our algorithm under mild conditions, despite that dynamic programming principle and the usual policy improvement theorem failing to hold for an equilibrium policy. Numerical experiments are given to demonstrate our algorithm. The design and implementation of our reinforcement learning algorithm apply to a general market setup.     

Generalized beta distributions for describing and analysing intraday stock market data: testing the U-shape pattern

There is an impressive body of empirical evidence which indicates the existence of an intraday U-shaped curve in stock prices. In an effort to shed additional light on the U-shaped curve a new procedure for U-shape testing is introduced. From careful analysis of intraday data it is observed that minimum or maximum stock prices can occur several times during the day. Here, attention is focused on the first time during the day that the maximum or minimum stock price occurred. Because of the importance of the first time during the day that the maximum or minimum stock price occurred, an attempt is made to model these two characteristics with probability distributions. The objective of this study is to use a generalized beta distribution to examine the intradaily behaviour of stocks, using closing stock prices for each one-minute interval, using data from Athens Stock Exchange (ASE). This generalized beta distribution has not been used before to model U-shaped behaviour. The results are consistent with the intraday U-shaped curves, i.e. the time to first maximum (or minimum) stock prices follows a U-shaped pattern. In addition, some potential applications of the generalized beta distribution are discussed and exemplified by analysing the relationship between herd behaviour and U-shaped.     

The puzzle of greenhouse gas footprints of oil abundance

The present inquiry lays a groundwork for the analysis of the net greenhouse gas (GHG) footprint of oil in the oil-abundant settings. To address the research question, the study puts forward a three-sector decision model, which provides a common ground for the assessment of the interaction of the structuralist and institutional factors influencing environmental pollution in the oil-reliant economies. The study shows that fossil-fuel abundance triggers forces, which induce diametrically opposed effects concerning atmospheric pollution. These are the rising carbon-intensive oil extraction and processing and fossil-fueled power generation versus shrinkage of the carbon-intensive manufacturing and growth of the low-carbon tertiarization. The theoretical analysis enables compartmentalization of the essential factors, which determine GHG emissions in the respective countries. To assess the significance of the proposed theoretical framework, the study employs multivariate panel co-integration techniques and two-stage fixed effects estimations for a dataset of 38 oil-producing countries for the time period between 1960 and 2018. In contrast to the existing literature, this study drives apart from the black box approaches that employ just one omnibus variable, per capita income.     

Generalized affine transform on pricing quanto range accrual note

This paper was to price and hedge a quanto floating range accrual note (QFRAN) by an affine term structure model with affine-jump processes. We first generalized the affine transform proposed by Duffie et al. (2000) under both the domestic and foreign risk-neutral measures with a change of measure, which provides a flexible structure to value quanto derivatives. Then, we provided semi-analytic pricing and hedging solutions for QFRAN under a four-factor affine-jump model with the stochastic mean, stochastic volatility, and jumps. The numerical results demonstrated that both the common and local factors significantly affect the value and hedging strategy of QFRAN. Notably,  the factor of stochastic mean plays the most important role in either valuation or hedging. This study suggested that ignorance of these factors in a term-structure model will result in significant pricing and hedging errors in QFRAN. In summary, this study provided flexible and easily implementable solutions in valuing quanto derivatives.     

Structural breaks in the mean of dividend-price ratios: Implications of learning on stock return predictability

In their out-of-sample predictions of stock returns in the presence of structural breaks, Lettau and Van Nieuwerburgh (2008) implicitly assume that economic agents’ perception of the regime-specific mean for the dividend-price ratio is time-invariant within a regime. In this paper, we challenge this assumption and employ least squares learning with constant gain (or constant-gain learning) in estimating economic agents’ time-varying perception for the mean of dividend-price ratio. We obtain better out-of-sample predictions of stock returns than in Lettau and Van Nieuwerburgh (2008) for both the U.S. and Japanese stock markets. Our empirical results suggest that economic agents’ learning plays an important role in the dynamics of stock returns.     

ARE LONG-HORIZON STOCK RETURNS PREDICTABLE? A BOOTSTRAP ANALYSIS

This paper examines empirical evidence of predictability of long-horizon real and excess stock returns in the UK using univariate as well as multivariate Variance Ratio tests. In order to estimate the sampling distribution of the test statistics, artificial histories ofstock returns are generated from their empirical distribution using the bootstrap method. This allows the construction of significance levels of the test statistic which are free from distributional assumptions. The empirical results indicate that there is no evidence of mean reversion in stock prices even if a wider information set to forecast stock returns is used and that the significance of historical Variance Ratio statistics has been overstated by previous studies.     

Are Golden Parachutes an Optimal Contracting Response or Evidence of Managerial Entrenchment?: Evidence from Successful Takeovers

Two models that attempt to explain the adoption of golden parachutes are examined. The first model views golden parachutes as an optimal contracting response to a takeover, the other perceives them as an outgrowth of severe managerial entrenchment that results in contracts for the benefit of managers. Using a sample of 169 successful acquisitions of NYSE targets from 1981 through 1989, I document that targets that have adopted golden parachutes experience significantly higher excess returns around the announcement of a takeover than targets without these contracts. I find similar increased excess returns for the bidder/target portfolios. In addition, bidder excess return is independent of the existence of golden parachutes in targets. Additional results suggest that golden parachutes do not reduce managerial resistance to takeovers. The results are consistent with the managerial entrenchment hypothesis and inconsistent with the optimal contracting hypothesis. Sensitivity tests confirm these results.     

金花茶自然保护区森林防火管理对策探讨

文章介绍了防城金花茶国家级自然保护区的概况,分析了金花茶保护区森林防火现状及存在的问题,就今后如何开展好森林防火工作提出了对策。     

指数导数的微分中值定理

本文证明了反函数的指数导数求导法则.在指数导数意义下建立了著名的罗尔定理,拉格朗日中值定理和柯西中值定理。     

黄金周假日旅游存在的问题及对策研究综述

近年来,黄金周假日旅游蓬勃发展,满足了国民日益增长的旅游需求,促进了旅游业及社会经济的发展;但同时,黄金周假日旅游中也暴露出来很多问题,引起了各方面的强烈关注,引发了各界特别是旅游学界对黄金周假日旅游及黄金周制度的研究和探讨.本文试图对近年来有关黄金周假日旅游中存在的问题、成因及解决问题的对策的研究做一回顾和总结.