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There is a broad consensus in the literature that spinoffs tend to create value for shareholders and exhibit positive long‐run excess returns. However, most of the prior studies are confined to the US and the European cases. The spinoff problems in Hong Kong are surprisingly under‐studied despite its important role as a global center of capital formation. In this paper, we find that there is a short‐run value creation for the Hong Kong spinoffs. However, the financial health of the spinoff companies, measured by various financial ratios, tends to deteriorate in the long‐run. In general, Hong Kong spinoffs generate negative returns to investors.

Outlines the development of genetic algorithms (GA), explains how they generate solutions to problems and applies four GA models incorporating different factors (e.g. risk, transaction costs etc.) to financial investment strategies. Uses 1987‐1996 share price data from the Madrid Stock Exchange (Spain) and a buy‐and‐hold strategy in the IBEX‐35 index as a benchmark. Shows that all four GA models generat superior daily returns of long positions with lower risk; and discusses the variations between them in detail.

The stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors frequently use technical analysis or fundamental analysis. Data mining techniques coupled with fundamental and technical analysis types have the potential to give satisfactory results for stock market prediction. In the current paper, an effort is made to investigate the accuracy of stock market predictions by using the combined approach of variables from technical and fundamental analysis for the creation of a data mining predictive model.

We chose 381 companies from the National Stock Exchange of India's CNX 500 index and conducted a two-stage data analysis. The first stage is identifying key fundamental variables and constructing a portfolio based on that study. Artificial neural network (ANN), support vector machines (SVM) and decision tree J48 were used to build the models. The second stage entails applying technical analysis to forecast price movements in the companies included in the portfolios. ANN and SVM techniques were used to create predictive models for all companies in the portfolios. We also estimated returns using trading decisions based on the model's output and then compared them to buy-and-hold returns and the return of the NIFTY 50 index, which served as a benchmark.

The results show that the returns of both the portfolios are higher than the benchmark buy-and-hold strategy return. It can be concluded that data mining techniques give better results, irrespective of the type of stock, and have the ability to make up for poor stocks. The comparison of returns of portfolios with the return of NIFTY as a benchmark also indicates that both the portfolios are generating higher returns as compared to the return generated by NIFTY.

As stock prices are influenced by both technical and fundamental indicators, the current paper explored the combined effect of technical analysis and fundamental analysis variables for Indian stock market prediction. Further, the results obtained by individual analysis have also been compared. The proposed method under study can also be utilized to determine whether to hold stocks for the long or short term using trend-based research.

Fair value pricing is a critical issue for mutual funds with international market exposure because trading in the underlying foreign securities is not synchronous with US market trading. Using a sample of Japanese open‐end mutual funds that trade in the USA, this paper explores the potential for exploitation of common mutual fund pricing practices and identifies much larger pricing errors than previously reported. A simple, objective solution to the fair value pricing quandary is proposed. The solution, based on foreign exchange‐traded funds and the S&P 500, provides a timely, objective pricing alternative that is less exploitable than current mutual fund pricing practices.

The purpose of this paper is to study the pattern of long-run performance of small and medium enterprises (SMEs) initial public offerings (IPOs) and examine the firm- and issue-related determinants of long-run performance of SME IPOs in India.

The 3 6, 9 and 12 months share returns of Indian SME IPOs is studied using event time methodologies, i.e. buy and hold returns, cumulative abnormal returns and wealth relatives on a sample of 375 SME IPOs issued during February 2012 to May 2018. Additionally, ordinary least square regression has been used to investigate the determinants of long-run performance of SME IPOs on a reduced sample of 104 because of non-availability of price observations.

The findings reveal that Indian SME IPOs exhibit long-run overperformance contradicting the international evidences of underperformance, and this overperformance is significantly evident using buy and hold abnormal return (BHAR). Furthermore, based on the divergence of opinion hypothesis, fads theory and windows of opportunity hypothesis, the results reveal that on one hand, issue size and oversubscription negatively affect BHAR, while on the other hand, auditor reputation, underwriter reputation, hot market, underpricing, inverse of issue price, profits prior to listing positively affect long-run performance. However, firm age, firm size, debt equity ratio, volatility and long-run performance computed through BHAR lacks significant relationship.

The study relied on event time methodology of measuring aftermarket performance of one year because of the limited availability of price offerings. Hence, the study could be extended to analyze aftermarket returns over a period of three to five years to enable reaching the vivid conclusions. Calendar time methodology may also be used to compute abnormal returns.

The results based on the study provides an implication to the investors by providing them an opportunity to bank higher long-run returns by engaging in active and timely trading strategies. Nevertheless, the results also show that investors should be cautioned while taking investment decisions.

The study contributes to rising body of international literature by analyzing the larger and recent sample of IPOs issued from 2012 to 2018 listed on SME exchange.

The pupose of the paper is to study the usefulness of Piotroski (2000)'s F-score in separating winners and losers in Vietnam.

The authors adopt a portfolio analysis and regression analysis on a sample of 501 of listed firms between 2009 and 2019 in Vietnam.

The authors find that a hedge strategy that buys high-F-score firms and sells low-F-score firms yield market-adjusted return of over 30 percent annually, which is statistically and economically significant. The hedge strategy based on F-score is not only profitable for value (high book-to-market [BM]) firms but also earn abnormal returns in a sample of growth (low BM) firms, suggesting that the usefulness of F-score strategy is not just a phenomenon in value firms as documented in previous literature.

Whilst the authors' paper documents economically significant returns obtained from the F-score strategy, the authors do not examine what drives the abnormal returns.

The results provide supporting evidence for the use of financial statement analysis as a screening tool to improve the performance of value investment in Vietnam stock market and for the training of financial reporting and fundamental analysis in universities.

The authors' research is the first study examining the F-score strategy in Vietnam that provides insights about the usefulness of fundamental analysis in separating winners and losers in a frontier market and contributes to the literature on fundamental analysis and market efficiency in emerging and frontier markets.

A well-documented pattern in the literature concerns the outperformance of small-cap stocks relative to their larger-cap counterparts. This paper aims to address the “small-cap versus large-cap” issue using for the first time data from the exchange traded funds (ETFs) industry.

Several raw return and risk-adjusted return metrics are estimated over the period 2012-2016.

Results are partially supportive of the “size effect”. In particular, small-cap ETFs outperform large-cap ETFs in overall raw return terms even though they fail the risk test. However, outperformance is not consistent on an annual basis. When risk-adjusted returns are taken into consideration, small-cap ETFs are inferior to their large-cap counterparts.

This research only covers the ETF market in the USA. However, given the tremendous growth of ETF markets worldwide, a similar examination of the “small vs large capitalization” issue could be conducted with data from other developed ETF markets in Europe and Asia. In such a case, useful comparisons could be made, so that we could conclude whether the findings of the current study are unique and US-specific or whether they could be generalized across the several international ETF markets.

A possible generalization of the findings would entail that profitable investment strategies could be based on the different performance and risk characteristics of small- and large-cap ETFs.

This is the first study to examine the performance of ETFs investing in large-cap stock indices vis-à-vis the performance of ETFs tracking indices comprised of small-cap stocks.

Economic theory suggests that profits of firms in industries with higher competition are less persistent and more volatile than in industries with lower competition (Stigler, 1963; Mueller, 1977). Extending this reasoning, I hypothesize that accounting-based fundamentals are more effective in predicting performance in industries with lower competition. I find that a measure of fundamentals (Piotroski’s F-score) has greater ability to identify potentially mispriced securities in industries with lower competition. The results are robust to using a variety of competition measures and imply that industry competition is an important consideration in the application of fundamental analysis.

The purpose of this paper is to investigate the influence of capital structure changes by target firms on the outcome and ex post performance of firms targeted by proxy contests.

The influence is examined by using predictions of control‐driven model developed by Harris and Raviv and signaling theory of debt in capital structure.

The results are consistent with the predictions of both control‐driven model and signaling theory. Significant differences are found between two groups of target firms – management victory targets and dissident victory targets. Specifically: management victory targets feature proxy contests that are accompanied by leverage increasing changes in target firms' capital structure; the same group also realizes better long‐run stock performance compared to dissident victory targets; and the long‐run abnormal stock performance of management victory targets is significantly positively related to the increases in leverage in the capital structure during proxy contest period.

This paper is the first to directly address the relationship between leverage change and the outcome and long‐run performance of proxy contest targets, thus confirming both the defensive and the signaling role of debt on firm's capital structure decision.

The analysis aims to explore how momentum return changes with alternative computational methods and the extent to which the portfolio structure is important in the momentum context.

The focus reflected in the prior research emphasises the method used by Jegadeesh and Titman and various extensions to test whether momentum returns exist. This study uses alternative methods of buying previous Winners and short‐selling previous Losers to determine if this significantly changes the returns.

The current study clarifies the impact of several contributory factors that impact upon estimated momentum returns. The large sample of cleaned data upon which this study is based provides a higher degree of confidence that the findings are sound and not just a statistical anomaly.

The research is important from a practitioner perspective as details of momentum return are presented for each country using different methods, providing information regarding the most profitable country in which to invest and whether the momentum return is sustainable under different formative approaches.

One of the important contributions of this study is a detailed empirical analysis, presenting results in a global context rather than on a single country basis.