Current Issue

Capital Markets Review Vol. 28, No. 2, pp. 1-18 (2020)

Performance of ProShares Triple-Leveraged Equity ETFs

Ramesh Adhikari1, Humnath Panta1 & M. Kabir Hussan3
1School of Business, Humboldt State University, USA.
2Department of Economics and Finance, University of New Orleans, USA.

Abstract: Research Question: How well do triple leveraged exchange-traded funds (ETFs) issued by ProShares perform and track their underlying indices?  Motivation: Prior research on the relative performance and tracking ability of leveraged and inverse ETFs (LETFs) is not conclusive. We re-examine and extend the findings of prior research (Charupat and Miu, 2011; Loviscek et al., 2014). We have a unique set of LETFs, more recent and historical price data over a longer sample period, and a unifying methodological framework. Our findings help many investors understand the implications of including LETFs in their portfolios for up to six months. Idea: We examine how well LETFs achieve their objectives using mean deviations and tracking errors of cumulative and holding period returns over various non-overlapping holding periods. Our holding period ranges from one day to six months. We also study how much LETFs deviate from their benchmark indices during high volatile markets. Data: We use daily prices of LETFs and values of their underlying indices from the inception date of each LETF to May 29, 2020. We have data for 2,749 trading days. We retrieve all data from Method/Tools: We use three sets of statistical tools to examine the return deviations. They include univariate tests, pooled regression-based alphas, betas, and tracking errors and bootstrapping. Findings: Although tracking errors increase with the number of holding periods, LETFs do well up to six months, and the magnitude of deviations are different for bull and bear ETFs. But, LETFs fail to deliver promised returns during high volatile markets. Our findings complement some prior research and contrast with others and bring some new insights on the performance of LETFs. Contributions: We provide new evidence that LETFs provide premiums or discounts beyond one month up to six months, but they fail to track their indices during high volatile markets.


Updated on 1 September 2020

Capital Markets Review Vol. 28, No. 2, pp. 19-27 (2020)

Max-Effect in the Indonesian Market

Leo Julianto1 & Irwan Adi Ekaputra1
1Faculty of Economics and Business, Universitas Indonesia, Indonesia.

Research Question: Following the well-documented MAX-effect anomaly in different markets, we inquire whether the MAX-effect occurs in the Indonesian market. Motivation: The MAX-effect is the following month negative return when investors long the highest decile portfolio and short the lowest decile portfolio. The decile portfolios are sorted and created based on the stocks highest previous month daily return (Alkan and Guner, 2018; Bali, et al., 2011; Seif et al., 2018). The anomaly has been documented in different countries and regions, such as Australia (Zhong and Gray, 2016), Turkey (Alkan and Guner, 2018), and European countries (Walkshäusl, 2014). We conduct this study because the Indonesian market has different features from other markets, namely the relatively low proportion of the retail investors in comparison to the whole population and the limit to short-sell stocks. Idea: Based on the extant literature, if the MAX-effect is robust, we deduce that the MAX-effect will exist in the Indonesian market. Data: We create ten portfolios sorted on the maximum previous month’s daily return (MAX) using the Indonesian market data from July 2013 till June 2018. Method/Tools: Our study utilizes descriptive statistics, Fama French Three-Factor Model, and Fama-Macbeth regressions. Findings: Our portfolio analysis suggests that a combination of long (short) position in high(low) MAX decile stocks will generate a raw return and a risk-adjusted (Fama-French Three-Factor) return of -1.6% per month. Also, our stock level analysis shows that MAX and market capitalization (SIZE) are negatively associated with the subsequent monthly stock return. In contrast, stock market beta (BETA) and book-to-market ratio (BM) do not significantly influence the subsequent monthly return. Contributions: Although the Indonesian market has different features, our study corroborates the existence of MAX-effects in different markets. We also find that the previous month MAX positively affects the current month MAX, indicating some investors’ preference for lottery-type stocks


Updated on 7 November 2020


Capital Markets Review Vol. 28, No. 2, pp. 29-41 (2020)

Predicting SMEs Failure: Logistic Regression vs Artificial Neural Network Models

Juraini Zainol Abidin1, Nur Adiana Hiau Abdullah1 & Karren Lee-Hwei Khaw2
1College of Business, Universiti Utara Malaysia, Malaysia.
2Faculty of Business and Accountancy, University of Malaya, Malaysia.

Abstract: Research Question: This study compares the power of logit and artificial neural network (ANN) models in predicting the failure of SMEs in the hospitality industry and identifies the predictors that are significant in determining business failure. Motivation: SMEs are an important segment of the Malaysian economy and contribute significantly to the country’s economic growth. However, SMEs are riskier and associated with a high failure rate. In Malaysia, around 3.5% of the SMEs in the hospitality industry fail within the first two years and 54% of them cease operations within four. Idea: The use of ANN to model business failure, particularly in the hospitality industry, is relatively unexplored in the emerging markets. Based on the literature, this study hypothesizes that ANN models outperform logit models because of less stringent model assumptions. Data: Excluding missing information, a matched sample of 41 failed and 41 non-failed SMEs in the hospitality industry was identified from the year 2000 to 2016. The accounting ratios, firm-specific characteristics and governance variables are selected as potential predictors of SMEs failure in the hospitality industry. Method/Tools: Stepwise logit regression and multilayer perceptron ANN models were used to determine significant predictors to predict business failure. Each model’s predictive power was compared. Findings: The ANN model was found to consistently outperform the logit model in classifying the failed and non-failed SMEs in the hospitality industry. Furthermore, the ANN model ranked liquidity as the most important predictor, followed by profitability and leverage, in determining business failure. Board size was also found to be a significant predictor in addition to the financial variables. The stepwise logit model also suggests a significant relationship between board size and the failure of SMEs. Therefore, in addition to financial predictors, a firm’s governance is also key to business survival. Contributions: The findings of this study contribute to the limited literature on SMEs in the hospitality industry by providing empirical evidence from an emerging market perspective. The failure prediction model can be utilized to warn of potential business failure so that strategic measures can be taken to mitigate the risk of failure.


Updated on 13 November 2020


Capital Markets Review Vol. 28, No. 2, pp. 43-71 (2020)

Stock Markets’ Integration in Post Financial Crisis Era: Evidence from Literature

Muhammad Hanif1,2 & Ariba Sabah1
1College of Business Administration, Ajman University,United Arab Emirates
2Department of Management Studies, Bahria University Islamabad, Pakistan

Abstract: Research Question: This study is conducted to organize the literature on long-run equilibrium of stock markets during the post-financial crisis era (2010-17), to document the latest developments. Motivation: Integration-status of markets contribute significantly to the decision of investment-diversification by a portfolio manager. Studies on the topic exist for pre-financial crisis period (e.g. Sharma and Seth (2012); however, we intend to organizes literature on the subject in post-financial crisis era. Financial crisis is a significant event of 21st century and knowing about developments in the area of market-integration is expected to enhance portfolio decision making. Idea: A collection and organisation of the published literature for review period to present a broader picture as opposed to empirical results of few selected markets. Data: Multiple studies have been published, however, we include 76 research articles [published in indexed Journals] in the area of market integrations during the period under review (2010-17). Method/Tools: We classified the publications based on country of origin; sample countries studied, sample periods, data frequency & econometric techniques used, and yearly publication trends, during the period under review. A selected review of findings is incorporated. Findings: Our findings suggest that developed and developing, larger GDP as well as Smaller GDP, countries have been researched during the period under review. Certain economic regions—Central Asia, East Europe, Africa (North, South, and Central), South America—have got less focus in research during the period under review, and offer potential research avenue. A period ranging from 9-17 years is the most widely used study period during the review period. Most widely used techniques to study market equilibrium are Correlation cointegration, regression and Granger causality. On the issue of integration, results indicate increase in integration of markets in various regions. Contributions: Our findings serve as a reference point for future researches in the area of portfolio diversification, potentially. To the best of our knowledge, no study has been conducted to organize the literature on market integration, covering post-financial crisis era.


Updated on 31 December 2020