Is the Emerging Asian Stock Markets Really Predictable Based on the Operations and Information Management?

Abstract—This paper examines the weak-form of market efficiency for six emerging Asian markets by using daily, weekly and monthly indices data based on the information management. The returns are not normally distributed, because they are negatively skewed and leptokurtic, and also found conditional heteroscedasticity. Findings suggest that none of the sample markets follow Random-walk and hence all are weak-form efficient markets except South Korean Markets. Additionally, short-term variants of the technical trading rules have better predictive ability than long-term variants. The results also reveal that these markets do not follow the same trend; the prices predictability is not analogous in all the sample markets

Uncertainty shocks of Trump election in an interval model of stock market

This paper proposes a new class of nonlinear interval models for interval-valued time series. By matching the interval model with interval observations, we develop a nonlinear minimum-distance estimation method for the proposed models, and establish the asymptotic theory for the proposed estimators. Superior to traditional point-based methods, the proposed interval modelling approach can assess the change in both the trend and volatility simultaneously. Within the proposed interval framework, this paper examines the impact of the 2016 US presidential election (henceforth Trump election) on the US stock market as a case study. Considering the validity of daily high-low range as a proxy of market efficiency, we employ an interval-valued return to jointly measure the fundamental value movement and market efficiency simultaneously. Empirical results suggest a strong evidence that the Trump election has increased the level/trend and lowered the volatility of the S&P 500 index in both ex ante and ex post analysis. Furthermore, a longer half-life period for the impact on fundamental value (62.4 days) than high-low range (15.9 days) has shown that the impact of Trump's victory on fundamental value is more persistent than its impact on market efficiency

Conditional Volatility Nexus between Stock Markets and Macroeconomic Variables: Empirical Evidence of G-7 Countries

Purpose: The purpose of this paper is to analyse the relation between stock market volatility and macroeconomic fundamentals for G-7 countries using monthly data over the period from July 1985 to June 2015.  Methodology: The empirical methodology is based on two steps: in the first step, we obtain the conditional volatilities of stock market returns and macroeconomic variables through the GARCH family of models. We also incorporate the impact of early 2000s dotcom and the global financial crises. In the second step, we estimate multivariate vector autoregressive (VAR) model to analyze the dynamic relation between stock markets return and macroeconomic variables.  Findings: The overall results for G-7 countries indicate a weak volatility transmission from macroeconomic factors to stock market volatility at individual level but the collective impact of volatility transmission is highly significant. Although, the results of block exogeneity indicate a bidirectional causality except for the UK, but the causal linkage is quite weak from stock market to macroeconomic variables. Moreover, the local financial variables excluding interest rate are closely integrated, and the volatility of industrial production growth and oil price are identified as the most significant macroeconomic factors that could possibly influence the directions of stock markets.  Originality: This research establishes the nature of the links between stock market and macroeconomic volatility. Research to date has been unable to satisfactorily establish the empirical nature of such links. We believe this paper begins to do this

Solving a type of biobjective bilevel programming problem using NSGA-II

AbstractThis paper considers a type of biobjective bilevel programming problem, which is derived from a single objective bilevel programming problem via lifting the objective function at the lower level up to the upper level. The efficient solutions to such a model can be considered as candidates for the after optimization bargaining between the decision-makers at both levels who retain the original bilevel decision-making structure. We use a popular multiobjective evolutionary algorithm, NSGA-II, to solve this type of problem. The algorithm is tested on some small-dimensional benchmark problems from the literature. Computational results show that the NSGA-II algorithm is capable of solving the problems efficiently and effectively. Hence, it provides a promising visualization tool to help the decision-makers find the best trade-off in bargaining

Market inefficiencies associated with pricing oil stocks during shocks

The assumption that market efficiency informs the pricing of oil stocks is critical to understanding the co-movement between stock markets and oil markets. To test this assumption in relation to various types of real oil price changes, this article proposes a two-stage analysis method that starts with a quantile regression to identify oil shocks and develop interval-valued factor pricing models. These interval-based methods, relative to traditional point-based methods, can produce more efficient parameter estimations by providing more information. The results show that oil stocks tend to be overpriced following negative oil price shocks, which partially violates the efficient market hypothesis. Yet oil stocks are efficiently priced in response to moderate changes or positive oil price shocks, such that in most cases, the market remains efficient in pricing oil stocks. (C) 2019 Elsevier B.V. All rights reserved

An Equilibrium Bid Markup Strategy

Setting a bid markup strategy is a very difficult task. Nevertheless, it is important to construction firms or consulting engineering companies because the development of successful bidding strategies is a key factor to their survival in business. Based on the two bidding criteria of the conditional profit ratio and the work force continuity, this short paper first presents the explicit expression of an equilibrium bid markup strategy in procurement auctions. However, the two bidding criteria conflict with each other and tradeoffs must be made. To make tradeoffs between the two bidding criteria, a new bid markup selection making model is then developed by Cobb-Douglas utility function. The model generalizes the classical expected profit model in the sense that the latter's objective function is only a special case of the former. The result shows that the relative importance of a bidding criterion to another has significant effect on the selection of the equilibrium bid markup strategyThis work was supported partly by the National Natural Science Foundation of China (No. 71001097 and 71171052) and China Postdoctoral Science Foundation (No. 201104167)S

Securities Transaction Tax and Stock Market Behavior in an Agent-based Financial Market Model

AbstractAs highly related to the investors’ earnings expectations and trading decision-making behavior, securities transaction tax (STT) has long been regarded as a typical regulatory mechanism exploited by policy makers. However, neither theoretical analysis nor empirical studies reach consensus about the role and policy effect of the securities transaction tax. Within the framework of agent-based computational finance, this paper presents a new artificial stock market model with heterogeneous agents, which allows us to assess the impacts of varying STTs on market behavior to come to robust conclusions. First we investigate the dynamics of benchmark market with no tax levied, and then market behaviors with different STTs are thoroughly checked. The results show that a modest transactions tax does contribute to stabilize markets by reducing market volatility, but its negative effects on market efficiency cannot be ignored at the same time. The findings suggest that regulatory authorities should introduce STT discreetly to strike a balance between stability and efficiency

Pricing Moving Window Parisian Option and Applications in Convertible Bonds

AbstractParisian options are complex path-dependent options developed by barrier option. Moving window Parisian options are higher path-dependent options, which are widely used in the field of convertible bonds in recent years. In this work we propose to price moving window Parisian option by use of hitting time. A simulation algorithm of the pricing is presented. As an application, we provide the pricing equations of convertible bonds with reset clause. Furthermore our simulation method is applied to price convertible bonds with reset clause using the data in China mainland stock exchange. The results show that this algorithm can undoubtedly improve the accuracy of the convertible bonds pricing