Editor’s introduction

The ninth issue of Financial Innovation (FIN), Volume 3, No. 4 (2017) presents eight papers contributed by sixteen authors from seven countries and areas: Canada, China, Hong Kong, India, Iran, Israel and United States as outlined below:

• The first paper is "Derived signals for S&P CNX nifty index futures" by B. Prasanna Kumar. Hedging effectiveness requires understanding key market signals such as trading margins, credit availability, and price discreteness. This study found that trading margins, credit availability, and price discreteness affect the variance of returns in the Indian futures markets. Through these signals, investors will be able to gain essential market knowledge and participate accordingly in trading for efficient returns.

• The second paper is "Performance evaluation of series and parallel strategies for financial time series forecasting" by Mehdi Khashei and Zahra Hajirahimi. This study aims to construct and evaluate five hybrid models to forecast stock prices. The empirical forecasting results for two benchmark datasets, the Shenzhen Integrated Index (SZII) and Standard and Poor’s 500 (S&P 500), indicate that although all hybrid models perform better than at least one of their individual components, the series combination strategy produces more accurate hybrid models for time series forecasting.

• The third paper is "Influence of meditation on estate planning decisions: evidence from Indian survey data" by Amarjit Gill, Harvinder S. Mand, John D. Obradovich and Neil Mathur. The purpose of this study is to examine the influence of meditation on estate planning decisions in India by extending previous findings on the influence of religious beliefs on the estate planning decisions of Canadians. A survey study indicates that mediation positively influences the estate planning decisions. The findings suggest that individual assets, family size, and education positively influence the estate planning decisions of Indians.

• The forth paper is "Optimal stopping investment in a logarithmic utility-based portfolio selection problem" by Xun Li, Xianping Wu, and Wenxin Zhou. In this paper, authors study the right time for an investor to stop the investment over a given investment horizon so as to obtain as close to the highest possible wealth as possible, according to a Logarithmic utility-maximization objective involving the portfolio in the drift and volatility terms. Numerical examples shed light on the efficiency of the theoretical results.

• The fifth and sixth papers are "The evolution and cross-section of the day-of-the-week effect" and "Day-of-the-Week Returns and Mood: An Exterior Template Approach" by Shlomo Zilca. In the former paper, the author studies the day-of-the-week effect across size deciles and in three 18-year subperiods, and the author examines the rule- and template-based pattern-recognition methods to identify various patterns in stock prices alongside more traditional econometric tools in the latter paper. Both papers together establish some interesting patterns with potentially valid explanations for the day-of-the-week phenomena that extend the knowledge boundary of behavioral finance.

• The seventh paper is "Value Investing or Investing in Illiquidity? The Profitability of Contrarian Investment Strategies Revisited" by Aron A. Gottesman,Gady Jacoby and Huijing Li. The authors of this paper investigate whether the success of contrarian investment strategies can be attributed to differences in the relative illiquidity of stocks categorized as value investments versus those categorized as glamour portfolios. They find strong evidence that those portfolios characterized as value investments are associated with dramatically greater levels of illiquidity than glamour portfolios.

• The final paper in this issue is "Teaching programming skills to finance students: How to design and teach a great course?" by Yuxing Yan. This paper explains seven critical factors for designing and teaching a programming course: strong motivation, a good textbook, hands-on learning environment, being data intensive, a challenging term project, multiple supporting R datasets, and an easy way to upload such R datasets.

Co-Editor-in-Chief.

J. Leon Zhao, Ph.D.

College of Business, City University of Hong Kong.