Do size and unobservable company factors explain stock price reversals?

We use daily price data from the Egyptian stock market and a Loser portfolio of 20 IPOs from the late 1990s that experienced dramatic 1-day price falls in the period 2004 to 2007 to estimate a 2-way fixed effects model of CARs. Observable covariates are company size and turnover growth and unobservables company and period fixed effects. Our results provide evidence of significant price reversal over the first 40 post-event days. Firm size is negatively correlated with post-event CARs, consistently with the argument that small firms have a stronger tendency to price-reverse due to greater informational opacity. But permanent, unobservable company-specific factors, account for a much larger percentage of post-event variation in stock prices and indicate an underlying heterogeneity in investor responses to initial price falls not uncovered before in the literature. Strong negative company effects following a price fall are found to presage reinforcing ‘long term’ price falls and strong positive company effects to presage countervailing ‘long term’ price reversals. At the extremes these company effects are sufficiently large to suggest that a trading strategy based on them would be profitable.     

Technology Adoption and Its Impact on Production Performance of Dairy Operations

Data from the 1993 Agricultural Resource Management Study were used to examine the impact of technology adoption on production performance of a sample of dairy farms. Findings showed that the adoption of a capital- or a management-intense technology would measurably lower the likelihood of a farmer being in the lowest quartile of production performance. The economic costs of milk production by the top-performance group were estimated to be 53% lower than those by the low-performance group, providing evidence of the importance of improved production practices to the viability of many dairy operations.     

Price limit bands,asymmetric volatility and stock market anomalies: Evidence from emerging markets

I investigate the effects of imposing different bands of price limits on stock returns and volatility in the Egyptian (EGX), Thai (SET) and Korean (KRX) stock exchanges. In addition, the paper examines whether the switch from narrow price limits (NPL) to wider price limits (WPL) structurally alters volatility and the day of the week anomaly. Using the extended EGARCH and PARCH asymmetric volatility models, I found that the switch from NPL to WPL structurally altered both asymmetric volatility and the day of the week anomaly in the EGX, SET and KRX. I argue that the price discovery mechanism is disrupted due to the switch as closing prices do not fully reflect all information arrived in the market when prices hit the limits and that is reflected on volatility and market efficiency.     

Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition

In this work we introduce the forecasting model with which we participated in the NN5 forecasting competition (the forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this model is to utilize the concept of forecast combination, which has proven to be an effective methodology in the forecasting literature. In the proposed system we attempted to follow a principled approach, and make use of some of the guidelines and concepts that are known in the forecasting literature to lead to superior performance. For example, we considered various previous comparison studies and time series competitions as guidance in determining which individual forecasting models to test (for possible inclusion in the forecast combination system). The final model ended up consisting of neural networks, Gaussian process regression, and linear models, combined by simple average. We also paid extra attention to the seasonality aspect, decomposing the seasonality into weekly (which is the strongest one), day of the month, and month of the year seasonality.     

An enhanced approach for Trend Impact Analysis

Decision makers in governments, corporations and institutions all need to forecast the future. Usually, traditional quantitative forecasting techniques are applied for this purpose. But the limitation of such methods is well known since all quantitative methods that are built solely on historical data (whether time-series or causal methods) produce forecasts by extrapolating such data into the future ignoring the effects of unprecedented future events that could cause deviation from the original surprise-free forecast if they were to occur. In the meanwhile, pure qualitative methods that don't utilize historical data miss its sound foundation. In the field of future studies, attempts are often made to combine quantitative and qualitative approaches using various hybrid methods such as Trend Impact Analysis. This paper introduces an advanced algorithm to enhance Trend Impact Analysis that adds another level of sophistication to the current algorithm. This advanced algorithm takes into account not only the impact of unprecedented future events' occurrences on the future trend, but also the different severity degrees with which the event might occur. This idea of severity degrees is novel, and its implementation is the main contribution of this paper.     

A Fuzzy Logic based Trend Impact Analysis method

All Trend Impact Analysis (TIA) algorithms in literature conduct the analysis based on direct estimates provided by experts for the probability of occurrence of an unprecedented event as an input to the algorithm. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using Fuzzy Logic. We postulate that in some cases it is better not to estimate the probability of occurrence of an unprecedented event directly; but rather estimate it indirectly via its attributes, using Fuzzy Logic. The core idea of the paper is to customize the generic process of reasoning with Fuzzy Logic by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes, especially when they reach certain threshold values.     

Malmquist indices of productivity growth for Islamic and conventional banks in Malaysia

This paper studies the total factor productivity （TFP） of banks in Malaysia with the emphasis on comparing the relative productivities of Islamic and Conventional banks. The Malmquist index approach is used to decompose productivity growth into technical efficiency and technological change. The productivity growth is measured and decomposed into technical change and efficiency change. The efficiency change is further decomposed into pure efficiency change and scale efficiency. It is found that Islamic banks＇ productivity growth is limited by its lack of technological change compared to its conventional counterparts. Nonetheless, both types of banks are operating at the correct level in terms of scale or size.     

Determinants of economic well-being among U.S. farm operator households

Participation in government programs has a mild impact on the economic well‐being of U.S. farm households. Major factors that determine farm household prosperity are the primary operator's education level and ethnicity, education level of the spouse, and other characteristics such as forward purchasing of inputs, use of contract shipping of products, having a succession plan, farm ownership, and location in a metro area. This article uses the 2001 Agricultural Resource Management Survey (ARMS) as well as relative and an absolute measure to assess U.S. farm households' economic well‐being. The relative measure compares the income and wealth position of farm households relative to median income and median wealth of the general population. The absolute measure adds annualized wealth to a farm household's income.     

Do regulatory policies affect the flow of information in emerging markets?

In a previous paper we established that volatility is best explained by contemporaneous rather than lagged trading volume in the Egyptian stock exchange (EGX). The main objective of this paper is to investigate the effects of regulatory policies - namely the switch from price limit to circuit breaker - on the dynamic relationship between trading volume and stock returns volatility in the EGX. Using daily returns data for 20 actively traded companies as well as the EGX30 market index, the Generalised Method of Moments (GMM), results show that the volume-volatility relationship is not only endogenous but is also structurally altered by the switch.     

Macroeconomic factors and firm’s cross-border merger and acquisitions

This study investigates the impact of macroeconomic variables on the wealth effects of cross-border acquisitions by Malaysian multinational companies. The sample consists of 159 cross-border acquisitions by Malaysian MNCs bidding in 22 countries around the world from 2000 to 2007. The macroeconomic variables examined in this study are foreign economic condition, GNP correlation between countries, and level of economic development of target country. The findings indicate that, foreign economic condition affects the wealth effect negatively, while the level of economic development of target country affects the wealth effect positively. Aside from the macroeconomic variables, we also find that English or non-English language, government’s rules index and the telecommunication infrastructure all play certain roles in the Malaysian cross-border acquisitions. Implied from this study is that, value creation of the Malaysian cross-border acquisitions is a function of the foreign economic condition and the level of economic development of the target foreign country.