Asymmetric response and interaction of U.S. and local news in financial markets

This paper examines the extent to which financial returns on market indices exhibit mean and volatility asymmetries, as a response to past information from both the U.S. market and the local market itself. In particular, we wish to assess the asymmetric effect of a combination of local and U.S. market news on volatility. To the best of the authors knowledge, this joint effect has not been considered previously. We propose a double threshold non‐linear heteroscedastic model, combined with a GJR‐GARCH effect in the conditional volatility equation, to capture jointly both mean and volatility asymmetric behaviours and the interactive effect of U.S. and local market news. In an application to five major international market indices, clear evidence of threshold non‐linearity is discovered, supporting the hypothesis of an uneven mean‐reverting pattern and volatility asymmetry, both in reaction to U.S. market news and news from the local market itself. Significant, but somewhat different, interactive effects between local and U.S. news are observed in all markets. An asymmetric pattern in the exogenous relationship between the local market and the U.S. market is also found. Copyright © 2005 John Wiley & Sons, Ltd.     

On a General Theorem of Set Theory Leading to the Gibbs, Bose-Einstein, and Pareto Distributions as well as to the Zipf-Mandelbrot Law for the Stock Market

The notion of density of a finite set is introduced. We prove a general theorem of set theory which refines the Gibbs, Bose-Einstein, and Pareto distributions as well as the Zipf law.     

ANALYSIS OF FINANCIAL DERIVATIVES BY MECHANICAL METHOD (Ⅰ)—BASIC EQUATION OF PRICE OF INDEX FUTURES

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A coupling cutting stock-lot sizing problem in the paper industry

An important production programming problem arises in paper industries coupling multiple machine scheduling with cutting stocks. Concerning machine scheduling: how can the production of the quantity of large rolls of paper of different types be determined. These rolls are cut to meet demand of items. Scheduling that minimizes setups and production costs may produce rolls which may increase waste in the cutting process. On the other hand, the best number of rolls in the point of view of minimizing waste may lead to high setup costs. In this paper, coupled modeling and heuristic methods are proposed. Computational experiments are presented.     

Detecting and Analyzing Politically-Themed Stocks Using Text Mining Techniques and Transfer Entropy—Focus on the Republic of Korea’s Case

Politically-themed stocks mainly refer to stocks that benefit from the policies of politicians. This study gave the empirical analysis of the politically-themed stocks in the Republic of Korea and constructed politically-themed stock networks based on the Republic of Korea’s politically-themed stocks, derived mainly from politicians. To select politically-themed stocks, we calculated the daily politician sentiment index (PSI), which means politicians’ daily reputation using politicians’ search volume data and sentiment analysis results from politician-related text data. Additionally, we selected politically-themed stock candidates from politician-related search volume data. To measure causal relationships, we adopted entropy-based measures. We determined politically-themed stocks based on causal relationships from the rates of change of the PSI to their abnormal returns. To illustrate causal relationships between politically-themed stocks, we constructed politically-themed stock networks based on causal relationships using entropy-based approaches. Moreover, we experimented using politically-themed stocks in real-world situations from the schematized networks, focusing on politically-themed stock networks’ dynamic changes. We verified that the investment strategy using the PSI and politically-themed stocks that we selected could benchmark the main stock market indices such as the KOSPI and KOSDAQ around political events.     

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).     

Transition metal-free Suzuki type cross-coupling reaction for the synthesis of dissymmetric ketones

A simple, efficient and metal-free route for the synthesis of dissymmetric ketones through Suzuki type cross-coupling reaction has been established. This strategy signifies an attractive, cost-effective and operationally convenient tool for the synthesis of a wide range of dissymmetric ketones. Although conventional routes for the synthesis of ketones have been widely used, the potential challenge with these methods is functional group tolerance. The reported metal-free method represents a reaction with moderate functional group tolerance. The procedure is operationally convenient and shows broad substrate scope with good to excellent product yields.     

Base-catalyzed cross coupling of secondary alcohols and aryl-aldehydes with concomitant oxidation of alcohols to ketones: An alternative route for synthesis of the Claisen-Schmidt condensation products

Base-catalyzed C–C cross coupling of secondary alcohols and aryl-aldehydes was achieved, when an alcoholic solution of an aryl-aldehyde was stirred under reflux for 45 h in the presence of a catalytic (20 mol%) amount of K₂CO₃. The consistent formation of α,α′-bis-(benzylidene) alkanones was obtained in moderate to good yields using various secondary alcohols and substituted aryl-aldehydes. Herein, α,α′-bis-(benzylidene)alkanones, which are the classical products of Claisen-Schmidt (cross aldol) condensation, have been synthesized via an alternative strategy using secondary alcohols. Bis-(benzylidene) alkanones are an integral part of various drug regimes and the production of bis-(benzylidene) alkanones without using any precious metal is a major outcome of the present reaction.     

牛磺酸Schiff碱及其Cu（Ⅱ）配合物的合成与表征

用牛磺酸与2-吡啶甲醛反应合成了一种牛磺酸Schiff碱,并制备了它与Cu（Ⅱ）的配合物[CuL（H2O）].NO3,其中HL=C8H10N2O3S,利用元素分析,红外光谱,紫外光谱,差热-热重分析及摩尔电导的测定等方法对Schiff碱及金属配合物进行了表征。     

牛磺酸2吡-啶甲醛Schiff碱镧配合物的合成及表征

采用直接合成法用牛磺酸和2-吡啶甲醛合成牛磺酸Schiff碱,并以此为配体合成La（Ⅲ）的配合物,同时对Schiff碱及金属配合物进行元素分析,红外光谱,紫外光谱,差热-热重分析及摩尔电导的测定等表征,从而推测配合物的组成为[L aL2（H2O）2]NO3.H2O,HL=C8H10N2O3S。