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The purpose of this paper is to empirically analyze the “market-neutral” characteristics of the market-neutral strategy hedge funds in Chinese A-share market.

The analyses in the paper are conducted to study the market-neutral characteristics by means of index analysis, correlation analysis, β-neutral analysis and the three-factor model analysis.

The results show that the performance advantage of the market-neutral strategy hedge funds is obvious. Most market-neutral strategy funds are exposed to market risks and the α strategy funds also have obvious style factor exposure; strictly speaking, all of the market-neutral strategies have not reached the “market-neutral” requirements. This paper also finds that Chinese trading restrictions on stock index futures in September 2015 have a significant impact on Chinese market-neutral strategy hedge funds.

The conclusion of this paper has a certain reference value for understanding the risk characteristics and possible problems of hedge funds in emerging markets, and also has important reference value for investors.

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.

Academic research has shown that diversification today may not only include stocks and bonds but also alternative investments like hedge funds. However, practical and effective methods to identify the hedge fund styles that really enhance the risk return characteristics of a traditional portfolio as well as optimal allocation sizes are not available. The aim of the paper is to try to close this gap by proposing a portfolio optimization approach based upon the traditional market exposures of the different hedge fund strategies.

For this purpose, the paper first measures the bull and bear market betas of the main hedge fund strategies (equity market neutral, event driven, global macro, relative value, and managed futures). Based on the strategy characteristics, the authors then develop a systematic framework that calculates what percentage of each basic asset should be substituted for by hedge fund strategies to achieve the maximum results. The paper uses hedge fund index data from Hedge Fund Research and Barclay Hedge for the January 1999‐April 2011 sample period.

The empirical results show that this approach leads to an improvement in the annualized return of the optimized portfolio.

The paper adds to the existing literature by showing that it is possible to substitute traditional assets with hedge fund indices based on their exposures (beta) in varying market environments as a way to optimize the overall portfolio.

Purpose – This chapter aimed to investigate hedge funds market risk. One aims to go further the traditional measures of risk that underestimates it by introducing a more appropriate method to hedge funds. One demonstrates that daily hedge fund return distributions are asymmetric and leptokurtic. Furthermore, volatility clustering phenomenon and the existence of ARCH effects demonstrate that hedge funds volatility varies through time. These features suggest the modelisation of their volatility using symmetric (GARCH) and asymmetric (EGARCH and TGARCH) models used to evaluate a 1-day-ahead value at risk (VaR).

Methodology/Approach – The conditional variances were estimated under the assumption that residuals t follow the normal and the student law. The knowledge of the conditional variance was used to forecast 1-day-ahead VaR. The estimations are compared with the Gaussian, the student and the modified VaR. To sum up, 12 VaRs are computed; those based on standard deviation and computed with normal, student and cornish fisher quantile and those based on conditional volatility models (GARCH, TGARCH and EGARCH) computed with the same quantiles.

Findings – The results demonstrate that VaR models based on normal quantile underestimate risk while those based on student and cornish fisher quantiles seem to be more relevant measurements. GARCH-type VaRs are very sensitive to changes in the return process. Back-testing results show that the choice of the model used to forecast volatility has an importance. Indeed, the VaR based on standard deviation is not relevant to measure hedge funds risks as it fails the appropriate tests. On the opposite side, GARCH-, TGARCH- and EGARCH-type VaRs are accurate as they pass most of the time successfully the back-testing tests. But, the quantile used has a more significant impact on the relevance of the VaR models considered. GARCH-type VaR computed with the student and especially cornish fisher quantiles lead to better results, which is consistent with Monteiro (2004) and Pochon and Teïletche (2006).

Originality/Value of chapter – A large set of GARCH-type models are considered to estimate hedge funds volatility leading to numerous evaluation of VaRs. These estimations are very helpful. Indeed, public savings under institutional investors management then delegate to hedge funds are concerned. Therefore, an adequate risk management is required. Another contribution of this chapter is the use of daily data to measure all hedge fund strategies risks.

The purpose of this paper is to examine the possibility of creating hedge funds “clones” using liquid exchange traded instruments.

Authors analyze the performance of fixed weight and extended Kalman filter generated clone portfolios (EKF) for 14 hedge fund strategies from February 2004 to September 2009. EKF approach does not indeed impose any normality constraints on the error terms which allow the filter to find the optimal recursive process by itself. Such models could adjust even faster to sudden shifts in market conditions vs a standard Kalman filter.

For five strategies out of 14, this work finds that EKF clones outperform their corresponding indices. Thus, for certain strategies, the possibility of cloning hedge fund returns is indeed real. Results should be however considered with caution.

This paper suggests that the most important benefits of clones are to serve as benchmarks and to help investors to better understand the various risk factors that impact hedge fund returns.

Rather than using fixed‐weight and rolling windows approaches (as Hasanhodzic and Lo), this work considers an extended version of the Kalman filter, a computational algorithm that better captures the time changing dynamics of hedge fund returns. Also, in order to be practical, this research considers investable factors and that the models themselves could not be constant over time.

The purpose of this paper is to present an alternative approach to equity trading that is based on cointegration. If there are long-run equilibria among financial assets, a cointegration-based trading strategy can exploit profitable opportunities by capturing mean-reverting short-run deviations.

First, the author introduces an equity indexing technique to form cointegration tracking portfolios that are able to replicate an index effectively. The author later enhances this tracking methodology in order to construct more complex portfolio-trading strategies that can be approximately market neutral. The author monitors the performance of a wide range of trading strategies under different specifications, and conducts an in-depth sensitivity analysis of the factors that affect the optimal portfolio construction. Several statistical-arbitrage tests are also carried out in order to examine whether the profitability of the cointegration-based trading strategies could indicate a market inefficiency.

The author shows that under certain parameter specifications, an efficient tracking portfolio is able to produce similar patterns in terms of returns and volatility with the market. The author also finds that a successful long-short strategy of two cointegration portfolios can yield an annualized return of more than 8 percent, outperforming the benchmark and also demonstrating insignificant correlation with the market. Even though some cointegration-based pairs-trading strategies can consistently generate significant cumulative profits, yet they do not seem to converge to risk-less arbitrages, and thus the hypothesis of market efficiency cannot be rejected.

The primary contribution of the research lies within the detailed analysis of the factors that affect the tracking-portfolio performance, thus revealing the optimal conditions that can lead to enhanced returns. Results indicate that cointegration can provide the means to successfully reproducing the risk-return profile of a benchmark and to implementing market-neutral strategies with consistent profitability. By testing for statistical arbitrage, the author also provides new evidence regarding the connection between the profit accumulation of cointegration-based pairs-trading strategies and market efficiency.

In early January 2008, a senior VP with LAAMCO, a fund of hedge funds known for alternative investments, was conducting due diligence on an equity market-neutral hedge fund. The hedge fund used an option strategy known as a collar (also known as a bull spread or split-strike conversion). The track record of the hedge fund had been stellar. The fund's performance had not only beaten that of the S&P 500 Index over the same period but had done so with much lower monthly return volatility. As part of the due diligence, it was necessary to backtest the collar strategy and try to quantify how much value the manager, BLM Investment Securities, LLC, (BLM) had added. The case is a disguised representation of an actual hedge fund—the true identity of BLM is revealed to students at the end of the case discussion.

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.

The Treynor and Mazuy framework is a widely used return-based model of market timing. However, existing corrections to the regression intercept can be manipulated through derivatives trading. Because they are conceptually flawed, these corrections produce biased performance measures. This paper aims to get back to Henriksson and Merton’s initial idea of option replication to overcome this issue and adapt the market timing model to various kinds of trading strategies and return-generating processes.

This paper proposes a theoretical adjustment based on Merton’s option replication approach adapted to the Treynor and Mazuy specification. The linear and quadratic coefficients of the regression are exploited to assess the cost of the replicating option that yields similar convexity for a passive portfolio. A similar reasoning applies for various timing patterns and in multi-factor models.

The proposed framework induces a potential rebalancing risk and involves the delicate issue of choosing the cheapest option. This paper shows that these issues can be overcome for reasonable tolerance levels. The option replication approach is a workable approach for practical applications.

The adaptation of Merton’s reasoning to the Treynor and Mazuy model has surprisingly never been proposed so far. This paper has the potential to correct for a pervasive bias in the estimation of the performance of a market timer in the context of this very popular quadratic regression setup. Because of the power of the option replication approach, the reasoning is shown to be applicable to multi-factor models, negative timing and market neutral strategies. This paper could fuel empirical studies that would shed new light on the genuine market timing skills of active portfolio managers.