Paskalis Glabadanidis

Market Timing with Moving Averages

I present evidence that a moving average (MA) trading strategy has a greater average return and skewness as well as a lower variance compared to buying and holding the underlying asset using monthly returns of value-weighted US decile portfolios sorted by market size, book-to-market, and momentum, and seven international markets as well as 18,000 individual US stocks. The MA strategy generates risk-adjusted returns of 3–7% per year after transaction costs. The performance of the MA strategy is driven largely by the volatility of stock returns and resembles the payoffs of an at-the-money protective put on the underlying buy-and-hold return. Conditional factor models with macroeconomic variables, especially the default premium, can explain some of the abnormal returns. Standard market timing tests reveal ample evidence regarding the timing ability of the MA strategy.

Evaluation of Malaysian mutual funds in the maximum drawdown risk measure framework

Purpose - This paper aims to evaluate the risk-adjusted performance of the management styles of Malaysian mutual funds using nine modified performance evaluation measures generated by the maximum drawdown risk measure (M-DRM) based on the modern portfolio theory. The purpose is to report the findings in a manner which is realizable by the average investors and portfolio managers. Design/methodology/approach - This paper evaluates the performance of more than 400 Malaysian mutual funds using risk-adjusted returns over the two sub-periods of 2000-2005 and 2006-2011. The M-DRM, as a different measure from downside risk, is applied to improve nine risk-adjusted performance measures of Sortino, Treynor, M-squared, Jensens alpha, information ratio (IR), MSR, upside partial ration (UPR), FPI, and leverage factor. It proposes a new single-factor model to test the maximum drawdown beta and alpha in the M-DRM framework. Findings - The evidence clearly indicates that the replacement framework in terms of MDB, the maximum drawdown beta, and the maximum drawdown CAPM can be replaced by the conventional frameworks in terms of MVB, beta, and the CAPM and also MSB, downside beta, and D-CAPM for modifying nine performance evaluation measures from the management styles of Malaysian mutual funds. Practical implications - The research evidence reported in this paper can be applied as input in the process of decision making by small and average investors and portfolio managers who are seeking the possibility of participating in the global stock market through mutual funds. Originality/value - This paper is the first study to estimate a new regression model in the M-DRM framework to evaluate the performance of Malaysian mutual funds. In addition, it proposes nine modified performance evaluation measures in the M-DRM framework for the first time.

Measuring the economic significance of mean-variance spanning

This paper investigates the economic significance of mean-variance spanning tests in the Bayesian framework. The use of Bayesian inference significantly facilitates the derivation of the finite sample distributions of quantities of interest which are hard to obtain in the classical framework. I propose a battery of Bayesian tests that shed new insights into the economic significance of statistical deviations from mean-variance spanning. The proposed tests measure the economic gains of a mean-variance investor from expanding a set of risky assets in a number of alternative ways that seem to have been largely ignored in the literature.

The Market Timing Power of Moving Averages: Evidence from US REITs and REIT Indexes

I present evidence that a moving average (MA) trading strategy dominates buying and holding the underlying asset in a mean-variance sense using monthly returns of value-weighted and equal-weighted US REIT indexes over the period January 1980 until December 2010. The abnormal returns are largely insensitive to the four Carhart factors and produce economically and statistically significant alphas of between 10 and 15% per year after transaction costs. This performance is robust to different lags of the MA and in subperiods while investor sentiment, liquidity risks, business cycles, up and down markets, and the default spread cannot fully account for its performance. The MA strategy works just as well with randomly generated returns and bootstrapped returns. The substantial market timing ability of the MA strategy appears to be the main driver of the abnormal returns. The returns to the MA strategy resemble the returns of an imperfect at-the-money protective put strategy relative to the underlying portfolio. The lagged signal to switch has substantial predictive power over the subsequent return of the REIT index. The MA strategy avoids the sharp downturn at the beginning of 2008 and substantially outperforms the cumulative returns of the buy-and-hold strategy using all of the 20 REIT indexes. The results from applying the MA strategy with 274 individual REITs largely corroborate the findings for the REIT indexes.

Average Drawdown Risk and Capital Asset Pricing

Practitioners and academics have spent the past few decades debating the validity and relevance of the capital asset pricing model (CAPM). One of the attributes of the model is an estimate of risk by beta, which in equilibrium describe the behavior of mean-variance (MV) investors. In the MV framework, risk is measured by the variance of returns which is a questionable and restrictive risk measure. In contrast, the average drawdown risk is a more acceptable risk measure and can be applied to modeling an alternative behavioral hypothesis, namely mean-drawdown behavior with a replacement risk measure for diversified investors, the average drawdown beta leading to an alternative pricing model based on this beta. Our findings clearly support the average drawdown beta and the pricing model of average drawdown CAPM versus the conventional beta and CAPM in a sample of Malaysian mutual funds.

An extensile method on the arbitrage pricing theory based on downside risk (D-APT)

Purpose - – The purpose of this paper is to propose a new and improved version of arbitrage pricing theory (APT), namely, downside APT (D-APT) using the concepts of factors’ downside beta and semi-variance. Design/methodology/approach - – This study includes 163 stocks traded on the Malaysian stock market and uses eight macroeconomic variables as the dependent and independent variables to investigate the relationship between the adjusted returns and the downside factors’ betas over the whole period 1990-2010, and sub-periods 1990-1998 and 1999-2010. It proposes a new version of the APT, namely, the D-APT to replace two deficient measures of factors beta and variance with more efficient measures of factors’ downside betas and semi-variance to improve and dispel the APT deficiency. Findings - – The paper finds that the pricing restrictions of the D-APT, in the context of an unrestricted linear factor model, cannot be rejected over the sample period. This means that all of the identified factors are able to price stock returns in the D-APT model. The robustness control model supports the results reported for the D-APT as well. In addition, all of the empirical tests provide support the D-APT as a new asset pricing model, especially during a crisis. Research limitations/implications - – It may be worthwhile explaining the autocorrelation limitation between variables when applying the D-APT. Practical implications - – The framework can be useful to investors, portfolio managers, and economists in predicting expected stock returns driven by macroeconomic and financial variables. Moreover, the results are important to corporate managers who undertake the cost of capital computations, fund managers who make investment decisions and, investors who assess the performance of managed funds. Originality/value - – This paper is the first study to apply the concepts of semi-variance and downside beta in the conventional APT model to propose a new model, namely, the D-APT.

An Exact Test of the Improvement of the Minimum Variance Portfolio: GMV Test

I propose an exact finite sample test of the risk reduction of the global minimum variance (GMV) portfolio. The GMV test statistic is proportional to the reduction in the variance of the GMV portfolio and has a straightforward geometric and portfolio interpretation and complements the celebrated GRS test in Gibbons et al. (1989). In practical applications, the GMV test leads to a rejection of the null hypothesis of no improvement in the GMV portfolio more often than the GRS test rejects the null hypothesis of no improvement in the risk‐return profile of the tangent portfolio. The power of the GMV test increases with the variance reduction of the GMV portfolio. Using test asset returns scaled by predetermined predictive variables is equivalent to increasing the overall number of test assets and leads to substantial power gains.

Another Test of the Efficiency of a Given Portfolio

I propose a new finite sample mean-variance efficiency test based on the risk reduction of the global minimum variance (GMV) portfolio. The GMV test statistic has a straightforward geometric and portfolio interpretation and complements nicely the celebrated GRS test in Gibbons, Ross and Shanken (1989). In practical applications, the GMV test leads to a rejection of the null hypothesis of mean-variance efficiency much more often than the GRS test. This imposes a higher bar on testing the efficiency of a given portfolio. The power of the GMV test increases in the improvement of the global minimum variance portfolio. Using test asset returns scaled by pre-determined instrumental variables is equivalent to increasing the overall number of test assets and leads to power gains. I also present the asymptotic versions of both tests as well as joint test leading to a test of mean-variance spanning.

Asset Pricing Models

Consider investing a current value of V0 for T periods at the compound periodic rate of r. The future value of the initial investment is given simply by the following: (1.1) .

Absence of arbitrage valuation : a unified framework for pricing assets and securities

1. Asset Pricing Models 12 1.1. Future Value 1.2. Present Value 1.3. Perpetuities 1.4. Annuities 1.5. Capital Asset Pricing Model 1.5.1. The Case of Two Risky Securities 1.5.2. The Case of Multiple Risky Securities 1.6. Fama-French Three-Factor Model 1.7. Carhart Four-Factor Model 1.8. Arbitrage Pricing Theory 1.9. Macroeconomic Multi-Factor Models 2. Discounted Cash Flow Valuation 2.1. Dividend Growth Models 2.1.1. Single-Stage Models 2.1.2. Two-Stage Models 2.1.3. Three-Stage Models 2.2. Equity Free Cash Flow 2.2.1. Single-Stage FCFE Model 2.2.2. Two-Stage FCFE Model 2.2.3. Three-Stage FCFE Model 2.3. Firm Free Cash Flow 2.3.1. Single-Stage FCFF Mode 2.3.2. Two-Stage FCFF Model 2.3.3. Three-Stage FCFF Model 3. Relative Valuation with Equity and Value Multiples 3.1. Equity Multiples 3.1.1. Price-Dividend Ratio 3.1.2. Price-Earnings Ratio 3.1.3. Price-to-Book Ratio 3.1.4. Price-Sales Ratio 3.2. Value Multiples 3.2.1. Value-to-Income Ratio 3.2.2. Value-to-Book Ratio 3.2.3. Value-to-Sales Ratio 4. Financial Options 4.1. Equity Calls and Puts 4.2. Examples of Option Strategies 4.2.1. A Protective Put Strategy 4.2.2. A Straddle Example 4.2.3. A Butterfly Example 4.3. Option Valuation 4.3.1. Bounds on Option Values 4.4. Option Pricing 5. Real Options 5.1. Equity and Bond Pricing as Options on Asset 5.2. Pricing Convertible Bonds 5.3. Option to Wait 5.4. Option to Abandon 6. Fixed Income Securities 6.1. Bond Characteristics 6.2. Bond Pricing 6.2.1. Basics 6.2.2. Bond Pricing Example 6.2.3. Bond Prices at Different Times to Maturity and YTM 6.2.4. Bond Yields 6.2.5. Bond Yields on Callable Bonds 6.2.6. Credit Risk 6.3. Spot and Forward Interest Rates 6.3.1. The Yield Curve 6.4. Term Structure of Interest Rates 6.4.1. Expectations Hypothesis 6.4.2. Liquidity Preference 6.4.3. Market Segmentation 6.4.4. Preferred Habitat Theories 6.4.5. Interpreting the Term Structure 6.4.6. Measuring the Term Structure 6.4.7. More Bonds than Time Periods 6.5. Fixed Income Arbitrage Strategies 6.6. Duration 6.7. Convexity 6.8. Bond Portfolios 7. Fixed Income Derivatives 7.1. Interest Rate Models 7.1.1. Traditional Term Structure Models 7.1.2. Term Structure Consistent Models 7.2. Binomial Term Structure Models 7.2.1. Pricing a Fixed Coupon Risk-Free Bond 7.2.2. Pricing a Risk-Free Floating-Rate Note (FRN) 7.2.3. An Interest Rate Swap 7.2.4. Adjustable-Rate Mortgages (ARM) 7.2.5. Pricing an Interest Rate Cap/Caption 7.2.6. Pricing an Interest Rate Floor/Flotion 7.2.7. Pricing a Reverse Floater 8. Foreign Exchange 8.1. Spot and Forward Commodity Prices 8.1.1. Purchasing Power Parity 8.2. Spot and Forward Exchange Rates 8.2.1. Triangular Arbitrage with Bid-ask Spread 8.2.2. Interest Rate Parity 8.3. Foreign Exchange Capital Budgeting 8.4. Currency Option Valuation 8.5. Currency Option Put-Call Parity 8.6. Pricing Currency Futures Options 8.7. Currency Futures Option Put-Call Parity 9. What Next? 9.1. Contingent Convertible Securities 9.2. Longevity Swaps 9.3. Acts of God versus Acts of Man Index