Measuring Investor Outcomes

The Sharpe ratio is widely used as a performance measure for traditional (i.e., long only) investment funds, but because it is based on mean-variance theory, it only considers the first two moments of a return distribution. It is, therefore, not suited for evaluating funds characterised by complex, asymmetric, highly-skewed return distributions such as hedge funds. It is also susceptible to manipulation and estimation error. These drawbacks have demonstrated the need for new and additional fund performance metrics. The monthly returns of 184 international long/short (equity) hedge funds from four geographical investment mandates were examined over an 11-year period.This study contributes to recent research on alternative performance measures to the Sharpe ratio and specifically assesses whether a scaled-version of the classic Sharpe ratio should augment the use of the Sharpe ratio when evaluating hedge fund risk and in the investment decision-making process. A scaled Treynor ratio is also compared to the traditional Treynor ratio. The classic and scaled versions of the Sharpe and Treynor ratios were estimated on a 36-month rolling basis to ascertain whether the scaled ratios do indeed provide useful additional information to investors to that provided solely by the classic, non-scaled ratios.

Surprisingly to many investors, low volatility tends to be accompanied with an undesirable risk characteristic: lower or negative skewness. A stock or fund can rank well based on the standard Sharpe ratio but low on enhanced tail-risk-based Sharpe ratios that account for non-normal returns, and vice versa. The authors quantify these economically meaningful ranking differences and show that skewness dominates other variables in explaining the ranking variations for the Conditional Value-at-Risk (CVaR)-based Sharpe ratio. Both skewness and serial correlation play important roles in the ranking variations for the maximum drawdown-based Sharpe ratio. <b>TOPICS:</b>Analysis of individual factors/risk premia, VAR and use of alternative risk measures of trading risk

Efficient Use of Conditioning Information: A Sharpe Ratio Based Test of Return Predictability

Recent events have highlighted the potential importance of nonlinear efiects of flscal variables (notably debt and deflcits) on interest rates: While in times when government solvency is not a concern the standard crowding-out efiects are of moderate magnitude, in times when default risk becomes an issue the interest rate efiects can become very large. This paper provides new evidence on the magnitude of these efiects. For the case when default risk is not a concern, it uses an arbitrage-free term structure model to estimate the dynamic efiects of flscal policy shocks on interest rates along the entire maturity spectrum. For the case when default risk becomes a concern (thereby violating a central assumption of the term structure model), I present evidence based on EMU government bond spread regressions on time-varying efiects of national flscal policies on spreads as well as the time-varying sensitivity of yield spreads to international risk aversion as a function of the state of flscal policy. JEL classiflcation: E6, H6.

Active money managers provide returns through two types of activities: stock selection and market timing. This study gives new insights into both, which can improve portfolio returns as well as client understanding of investment performance. We measure the sensitivity of stock returns to several macroeconomic factors and examine the risk-adjusted return performances of four popular value and growth investment strategies. Sensitivity to the macroeconomic factors differs across the strategies, depending on how value (or growth) is defined. Thus, not all value (nor all growth) portfolios will perform the same under a given set of macroeconomic conditions. Also, sensitivity to a given macroeconomic factor differs, varying from positive to negative to non-existent, depending on monetary policy. So a particular macroeconomic risk factor does not consistently affect portfolio performance. Last, how value or growth strategies perform relative to the general market also depends on monetary policy: after controlling for several macroeconomic factors we find that all four (only two) value portfolios earn positive risk-adjusted returns under expansive (restrictive) monetary policy regimes and growth portfolios earn negative risk-adjusted returns only under expansive monetary policy regimes. <b>TOPICS:</b>Equity portfolio management, security analysis and valuation, legal and regulatory issues for structured finance

PurposeThe purpose of this paper is to examine whether superior risk-adjusted returns can be generated using monthly covered call option strategies in large capitalized Australian equity portfolios and across varying market volatility conditions.Design/methodology/approachThe authors construct monthly in-the-money (ITM) and out-of-the-money (OTM) S&P/ASX 20 covered call portfolios from 2010 to 2015 and use standard and alternative performance measures. An assessment of variable levels of market volatility on risk-adjusted return performance is also carried out using the spread between implied and realized volatility indexes.FindingsThe results of this paper show that covered call writing produces similar nominal returns at lower risk when compared against the standalone buy-and-hold portfolio. Both standard and alternative performance measures (with the exception of the upside potential ratio) demonstrate that covered call portfolios produce superior risk-adjusted returns, particularly when written deeper OTM. The 36-month rolling regressions also reveal that deeper OTM portfolios deliver greater risk-adjusted returns in the majority of the sub-periods investigated. This paper also establishes that volatility spread variation may be a driver of performance for covered call writing in Australia.Originality/valueThe authors suggest that deeper OTM covered call strategies based on large capitalized portfolios create value for investors/fund managers in the Australian stock market and can be executed in volatile market conditions. Such strategies are particularly useful for those seeking market neutral asset allocation and less risk exposure in volatile market environments.

Many large US and foreign banks have introduced performance evaluation methods designed to allocate capital among their di€erent businesses. Traditional performance measures such as return on capital have long been recognized as failing to take into account the risk of the underlying business and the value of future cash ows. The capital asset pricing model (CAPM) provides a framework for determining the net present value of a business. Expected future cash ows are discounted using a risk-adjusted expected rate of return. Risk is de®ned in terms of the covariance of changes in the market value of the business with changes in the value of the market portfolio. This form of risk measure is usually referred to as the beta coecient. For an introduction to the CAPM and discussion of the beta coecient, see Brealey and Myers (2000). For a portfolio of assets, risk in the CAPM is de®ned in terms of the standard deviation of the portfolio's return. The Sharpe ratio is a measure of the relative attractiveness of di€erent portfolios. It maps the expected return and the risk into a single measure. It is argued that the larger the Sharpe ratio, the better the portfolio, and hence the objective of portfolio manager's is to maximize the Sharpe ratio or, equivalently, the expected return on risk-adjusted capital. For many businesses, measuring the ®rm's beta and the beta of an investment presents a formidable challenge given the absence of market data. Practitioners have also expressed the concern that measures of risk such as beta, do not consider the risk and costs associated with default. Consequently, other forms of expected return/risk measures have been introduced (see Froot and Stein 1996). The stated objective of these risk-based capital allocation systems, which are often grouped together under the acronym RAPM (risk-adjusted performance

In addition to the well-established and most commonly used portfolio performance measures, both in theory and practice - the Sharpe ratio, the Treynor ratio and the Jensen's or alpha index, the financial literature also includes other alternative portfolio measures, such as: two modified versions of the Sharpe ratio - the information ratio and the M2 portfolio performance measure, one modified version of the Treynor ratio - the T2 portfolio performance measure, models that measure the market timing abilities of fund managers - the Treynor-Mazuy and the Henriksson-Merton model and a ratio based on the downside risk and the downside deviation as its measure - the Sortino ratio. The paper aims to inform the investors in the Republic of Serbia about the basic features of the aforementioned portfolio performance measures, as well as to point to the importance of understanding the decomposition of the actual portfolio performance of mutual funds.

Forecasting value at risk allowing for time variation in the variance and kurtosis of portfolio returns

Benchmarking, portfolio insurance and technical analysis: a Monte Carlo comparison of dynamic strategies of asset allocation

It Takes Two to Tango: Fundamental Timing in Stock Market

This paper examines the impact of the advance notice period on hedge fund performance persistence in investors’ portfolios. As investors in hedge funds face several trading restrictions related to the funds’ common investment terms and conditions, it is hypothesized that the advance notice period generates illiquidity for investors and impairs their ability to benefit from performance persistence in hedge funds. Using a sample of 4,788 hedge funds over a period from 1994 to 2008 and contingency table based standard methodology for returns and Sharpe ratios, the results suggest that accounting for individual funds’ advance notice periods has a negative impact on the performance persistence of hedge funds. The proportion of significantly persistent funds declines when incorporating the advance notice period, especially for short time horizons. Furthermore, a considerable part of hedge fund performance persistence is related to asset class-specific terms and conditions, reducing the amount of short-term performance persistence in practice.

Recent trends in relative performance of global equity markets

Semivariance below the maximum: Assessing the performance of economic and financial prospects

The study focuses on improving the quality of using recurrent neural networks (RNNs) to predict cryptocurrency prices. The formula of the target variable for the model based on the arithmetic mean is developed, which allows us to better take into account the dynamics of cryptocurrency exchanges. The factors affecting this variable were grouped into features based on the volume of daily cryptocurrency trading, the volatility of the relevant prices, and the pre-calculated and selected signals of technical indicators. As part of the study, an algorithm for processing daily data was developed for the model. The results obtained made it possible to create a holistic model for forecasting stock prices. Two recurrent neural networks were trained: one with a long short-term memory (LSTM) and the other with a recurrent gate unit (GRU). To determine the efficiency of the models, the analysis was carried out using two key indicators: the Sortino coefficient, which measures the relative risk/reward for each additional unit of unwanted volatility, and the Sharpe ratio, which measures the return on assets, subtracting the free risk. As a result, it was found that both models have similar results in terms of accuracy (~69 %). Still, the GRU-based model showed significantly better values of the Sortino coefficients (3.13) and Sharpe’s coefficient (2.45), which allows us to conclude that it is effective on cryptocurrency exchanges. At the same time, the LSTM model requires more parameters for training than the GRU model with an identical structure, which leads to a longer training time. The obtained scientific and practical results are aimed at more efficient use of recurrent neural networks in price forecasting on cryptocurrency exchanges