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We highlight key assumptions implicit in the models used by academics conducting research on market efficiency. Most notably, many academics assume that investors can borrow unlimited amounts and construct long-short portfolios at zero cost. We relax these assumptions and examine the attractiveness of long-short strategies as stand-alone investments and as a part of a diversified portfolio. Our analysis illustrates that the key benefit of long-short investing is adding diversification to a portfolio beyond what the market provides. We show that as stand-alone investments, nontrivial risk remains in the “hedge” strategies and that the returns generally do not beat the market in a head-to-head contest. Our findings raise questions about the degree of inefficiency in anomaly studies because plausible measures of costs generally offset strategy returns. The ability to achieve greater diversification may be, but is not necessarily, due to market inefficiency. We also highlight the key role of the generally ignored but critically important short interest rebate and show that absent this rebate, the long-short strategies we examine generally yield insignificant returns.

With the development of the modern portfolio theory and the advancement of information technology, the employment of quantitative approaches to practically measure asset risks and returns, and the construction of portfolios (even dynamic portfolios) has become possible and popular. Therefore, the purpose of this paper is to construct a multi‐factor model for Taiwan stock universe using fundamental technical descriptors and then to apply the equity market neutral investing using multiple‐factor models as a tool.

This study constructs a Taiwan equity multi‐factor model using cross‐sectional fundamental technical approach.

The model involves 28 explanatory factors (including 20 industry factors), and the results of the estimations are satisfactory. The model's explanatory power is 58.6 per cent on average. Furthermore, this multi‐factor model is feasible, modulized, dynamic (i.e. modified over time) and updating.

The multi‐factor model, constructed and utilized in this study, is a useful and feasible tool. It generates important inputs into the applications of building market neutral portfolio.

Taiwan OTC market is an electronic, order driven, call market. The purpose of this paper is to gain understanding of whether trade size or number of transaction provides more information on explaining price volatility and market liquidity in this market. The paper also aims to investigate how market condition can affect the relationship between information type and trading activities.

The paper uses data from the Taiwan OTC market to run the empirical tests. It divides firms into five size groups based on their market capitalization. Regression equations are run to test: whether number of transactions has a more significant impact on price volatility on the Taiwan OTC market; the impact of market information on number of transactions; the relative impact of firm specific and market information on number of transactions; and the impact of number of transaction of bid‐ask spread.

Findings show that the larger the number of transactions, the higher the price volatility. Smaller firms on the Taiwan OTC market are traded based on firm‐specific information. This relation is further affected by market trends. Especially for the larger firms, when the market is up and the amount of market information increases, number of transactions increases. When the market is down and the amount of market information increases, number of transactions decreases. Finally, it is found spread size is more likely to be influenced by number of transactions, instead of trade size. Overall, based on these empirical results, the information content of number of transactions seems to be higher than that of trade size in the Taiwan OTC market.

Investors now understand that number of transaction actually carry more information than trade size does.

The relation between market information and number of transaction, also that between market information and trade size is influenced by market condition. The paper fills a gap in the literature to show that market condition has an impact on the relation between information type and trader's behavior. A number of transactions are identified that provide more information than trade size does. It is also shown that market conditions can further affect the impact of information on trading activities.

The main purpose of this study is to examine the role of quality as a determinant of a cross-sectional variation in country-level stock returns. The study attempts to address the question: Is there any special premium for top-quality stock markets with decent profitability, indebtedness and liquidity ratios?

The computations are based on the listings of 66 country portfolios over the period between 2000 and 2013. Long/short country portfolios from sorts on characteristics related to quality are examined with asset-pricing models.

The inter-market variation in returns may be explained with profitability and debt ratios: the more profitable and the less indebted is the stock market, the better is its performance. Moreover, the performance of country-level value, size and momentum strategies may be improved by double sorting on quality characteristics.

The practical implications include such issues as the global asset allocation, the development of investment products, asset pricing and investment performance measurement. The country selection strategies that are based on leverage and profitability prove to be a useful tool for investors with a global investment mandate. Furthermore, additional sorting on quality metrics may markedly improve the performance of inter-market value, size and momentum strategies.

This paper examines the role of quality metrics related to financial leverage, profitability and liquidity in explaining the cross-sectional variation in country returns.

The authors are interested in building descriptive – real life – models of successful investors’ investment reasoning and decision-making. Models designed to be useful for trying to replicate and evolve their reasoning and decision-making. The purpose of this paper, a case study, is to take the substantial material – on innovating the investing tools – published in four books (2006/2012, 2010, 2011, 2015) by a US stock investor named Kenneth Fisher (CEO of Fisher Investments, Woodside, California) and sketch Fisher’s investment innovating reasoning model.

To sketch Fisher’s investment innovating reasoning model, the authors used the Radical constructivist theory of knowledge, a framework for analyzing human action and reasoning called Symbolic interactionism and a qualitative analytic technique called Conceptual analysis. The authors have done qualitative research applied to the study of investment decision-making of a single professional investor.

In the paper, the authors analyzed and described the heuristics used by Fisher to build subsequent generations of investing tools (called by Fisher “Capital Markets Technology”) to try to make better forecasts to beat the stock market. The authors were interested in studying the evolutive dimensions of the tools to make forecasts of a successful investor: the “how to build it” and “how to evolve it” dimension.

The paper offers an account of Kenneth Fisher’s framework to reason the innovation of investing tools. The authors believe that this paper could be of interest to professional money managers and to all those who are involved in the study and development of the tools of investing. This work is also an example of the use of the Radical constructivist theory of knowledge, the Symbolic interactionist framework and the Conceptual analysis to build descriptive models of investment reasoning of individual investors, models designed to enable the reproduction/approximation of the conceptual operations of the investor.

Investors are inattentive to continuous information as opposed to discrete information, resulting in underreaction to continuous information. This paper aims to examine if the well-documented return predictability of the strategies based on the ratio of short-term to long-term moving averages can be enhanced by conditioning on information discreteness. Anchoring bias has been the popular explanation for the source of underreaction in the context of moving averages-based strategies. This paper proposes and studies another possible source based on investor inattention that can potentially result in superior performance of these strategies.

The paper uses portfolio sorting as well as Fama-MacBeth cross-sectional regressions. For examining the role of information discreteness in the return predictability of the moving average ratio, the sample stocks are double-sorted based on the moving average ratio and information discreteness measure. The returns to these portfolios are computed using standard approaches in the literature. The regression approach controls for various well-known return predictors.

This study finds that the equally-weighted monthly returns to the long-short moving average ratio quintile portfolios increase monotonically from 0.54% for the discrete information portfolio to 1.37% for the continuous information portfolio over the 3-month holding period. This study observes a similar pattern in risk-adjusted returns, value-weighted portfolios, non-January returns, large and small stocks, for alternative holding periods and the ratio of 50-day to 200-day moving average. The results are robust to control for well-known return predictors in cross-sectional regressions.

To the best of the authors’ knowledge, this is the first paper to document the significant role of investor inattention to continuous information in the return predictability of strategies based on the moving average ratios. There are many underreaction anomalies that have been reported in the literature, and the paper's results can be extended to those anomalies in subsequent research.

The findings of this paper have important practical implications. Strategies based on moving averages are an extremely popular component of a technical analyst's toolkit. Their profitability has been well-documented in the prior literature that attributes the performance to investors' anchoring bias. This paper offers a readily implementable approach to enhancing the performance of these strategies by conditioning on a straightforward measure of information discreteness. In doing so, this study extends the literature on the role of investor inattention to continuous information in anomaly profits.

While there is considerable literature on technical analysis, and especially on the performance of moving averages-based strategies, the novelty of this paper is the analysis of the role of information discreteness in strategy performance. Not only does the paper document robust evidence, but the findings suggest that the investor’s inattention to continuous information is a more dominant source of underreaction compared to anchoring. This is an important result, given that anchoring has so far been considered the source of return predictability in the literature.

The paper's aim is to explore the impact of statistical arbitrage and high-frequency trading as hedge fund investment strategies that have a significant impact on the environment of corporations.

The paper is a meta-analysis of the role of investment strategies within complex systems.

The growth of hedge fund investment activity based on statistical arbitrage tends to produce a vulnerability; more funds using the strategy helps to create the profitable outcomes that the strategy relies upon. However, the growth also reduces the time lines of profitability and produces an underlying instability based on overlapping holdings and the use of leverage. The shortened timelines also create a further impetus towards technological competition and promotes high frequency trading, which then introduces further vulnerabilities based on “stop-loss cascades”.

Much of the trading creates a superficial form of liquidity, which gives a limited sense of market vulnerabilities. The basis of complex interactions between high frequency traders is also not clearly understood. Researchers and agents of policy ought to pay greater attention to the issues than is currently the case.

The area is one that is under-researched.

This chapter aims to determine whether diversification benefits accrue from adding emerging market hedge funds (EMHFs) to an emerging market bond/equity portfolio, and subsequently whether the type of exposure hedge funds provide is justified by their fees. We use multivariate cointegration analysis to show that the advantages of adding hedge funds to balanced portfolios are limited for the three regions of Asia, Eastern Europe, and Latin America, as well as for the entire global emerging market universe. In summary, we find that emerging market hedge funds are generally redundant for diversifying long-only emerging market investment portfolios with long-term investment horizons. This result also holds when we extend our sample by the global financial crisis in 2008 and 2009 and allow for structural breaks according to the Gregory-Hansen (1996) test. Hence, even during the global financial crisis in 2008 and 2009, when risk diversification was most needed, long-term comovements between hedge funds and traditional assets is, with the exception of the Eastern European region, not disrupted. Because EMHF returns are heavily influenced by the emerging market equity and bond markets, we conclude that the “alpha fees” charged by EMHFs may not always be appropriate for the three main regions under consideration. This also holds, however, to a lesser extent, for a global diversification among hedge funds and traditional assets in emerging markets.

As the world's largest emerging market, the evidence of momentum effect in China is also mixed. Meanwhile, prior studies mainly examined individual stock momentum in China, with little concern for industry momentum and its relationship with trading volume. The motivation of this study is to investigate industry momentum in China and examine whether trading volume can enhance its profitability.

Firstly, the authors test the existence of industry momentum in China; secondly, the authors test the correlation between trading volume and momentum returns using the double ranking method; finally, the authors test whether trading volume enhances the momentum returns using Fama–French five-factor model.

The authors find that there is a significant industry momentum effect in China, and the momentum returns jointly come from winner and loser portfolios. The intervals between the formation and holding periods have an impact on the performance of momentum portfolios. In terms of trading volume, the authors find that high-volume industries have industry momentum effects while low-volume industries do not. The industry momentum strategies achieve higher excess returns in high-volume industries.

Prior literature found higher momentum returns in low-volume stocks in China, but the research in this study suggests that implementing an industry momentum strategy in low-volume industries will miss out on higher returns or even bring losses, and instead the investors should invest in high-volume industries to get the best performance.

This study extends existing research by focusing on industry momentum and its relationship with trading volume in the Chinese stock market and finds an interesting relationship between industry momentum returns and trading volume, which is different from related studies.