The comparative risk and performance analysis of Hungarian and Romanian exchange indices

MINISTRY OF NATIONAL EDUCATION THE ANNALS OF THE UNIVERSITY OF ORADEA ECONOMIC SCIENCES TOM XXII 2nd ISSUE / DECEMBER 2013 ISSN 1222-569X (printed format) ISSN 1582-5450 (electronic format) 451 THE COMPARATIVE RISK AND PERFORMANCE ANALYSIS OF HUNGARIAN AND ROMANIAN EXCHANGE INDICES Tibor Tarnóczi, Edina Kulcsár University of Debrecen, Faculty of Applied Economics and Rural Development, Debrecen, Hungary University of Oradea, Faculty of Economic Sciences, Oradea, Romania University of Debrecen, Faculty of Applied Economics and Rural Development, Debrecen, Hungary tarnoc[email protected] k[email protected] Abstract: Nowadays, the most dominant characteristics of the financial environment are instability, variability, riskiness and uncertainty. It is difficult to find a field where the decision making process is risk-free. This statement is especially true in case of financial investments according to which risk taking is rewarded. But it is also true that the financial market participants cannot be completely avoided risks, but there are many options for managing and minimizing them. One of the most well-known theories of financial instruments' risk minimization is the modern portfolio theory, which is the collection of tools and techniques by which a risk-averse investor may construct an optimal portfolio. In portfolio theory it is also known the possibility of risky assets diversification to obtain the optimal return/risk ratio. Consequently, this paper aims to examine the efficient portfolio alternatives by determination of performance ratios based on CAPM model and modern portfolio theory, such as Sharpe ratio, Jensen’s alpha and Treynor ratio and risk measuring methods, such as Value at Risk, or Expected Shortfall. In present research we concentrate to a comparative analysis of portfolios consist in main stock indices shares of two neighboring countries from Central and Eastern Europe: Hungary and Romania. The analysis was performed on the Romanian BET and Hungarian BUX stock market indices using the six-month daily closing prices. Data of the analysis were downloaded from the official websites of Romanian and Hungarian stock exchanges. The statistical analysis was made in R statistical system. Using such tools to uncover information and ask better questions will support the investors to make better and better investment decisions. The results of present research show a greater performance level for Romanian portfolio, but also a higher level of risk, with lower volatility toward market changes and major specific risk. For the Hungarian portfolio, the performance is more temperate, the level of risk is also smaller and the volatility to market factors is more relevant, so the specific risk is moderate in this case. Keywords: diversification, portfolio theory, risk, efficient portfolio, return, performance analysis JEL classification: G10, G11, G12, G19 452 1. Introduction In our days, economic environment is characterized by risk, volatility and uncertainty. The most of economic entities are profit oriented, therefore during their activity developing the risk taking is inevitable. In the case of company which carries out financial investments, the risk taking has a special role. According to well-known principle “who doesn’t risk, doesn’t win”, in order to achieve higher returns, investors need to take higher risk on capital market. The risk level of financial assets is different, while the treasury bills could be considered the low risk or risk-free assets, the shares return and also risk level is higher. In accordance with modern portfolio theory, the rational investor doesn’t invest its capital exclusively in one type of financial asset. He establishes its investment decision on the base of relationship between return and risk, so as to achieve maximum return with minimum risk possible. Measuring portfolio performance is one of the most important tools for portfolio optimization. Therefore, this article aims to examine two neighboring Central and Eastern Europe situated countries’, major indices shares using three performance-based indicators and risk measuring methods. 2. Review of literature In Hungarian, Romanian and international literature can be found various theories about general risk concept. We want to underline the most relevant of them. The one of the most known definition of general, overall risk is the likelihood of an adverse event occurs. Alastair in his Mastering Risk Modelling book gives more definition to risk. The most frequently mentioned are the follows: the probability of occurring different outcomes; deviations from the expected results; the chance of symmetric occurrence of profit or loss (Alastair, 2009: 59). Reto Gallati in his work, which is called Risk management and capital adequacy, defines risk like a “situation in which there is a possibility that the received results deviate from the expected results” (Gallati, 2003: 8). According to Gallati, the deviance from the expected results must be understood in positive and also in negative way. We consider that in the case of financial assets, the second definition is most characteristic, because the frequency and the amplitude of deviance from expected outcomes are larger. It is clear that in the case of risky assets the time factor plays a very important role, too. 453 One of the most well-known theories about risk is the Knight’s theory, according to which there is a significant difference between the concept of risk and uncertainty. Knight’s work (1921) especially is oriented by distinction between risk and uncertainty. According to him, the main difference between risk and uncertainty lies in the possibility of measurement, so while the risk can be measured, the uncertainty could not be. He also says, that if the risk could be quantified it also could be managed while in the case of uncertainty this is not specific, because it couldn’t be measured and managed. Knight’s risk quantification theory most strongest criticism comes from Keynes (1937), who said “the economic uncertainty of future cannot be solved by looking at statistical patterns of the past” and “the future human decisions (…) does not depend on strictly mathematical expectations, because these types of calculations have no basis.” According to Keynes and his followers, the developments of future decisions will not be affected by “strictly mathematical expectations” (Bélyácz, 2011: 380). Nowadays economic environment’s essential feature is riskiness. The distinction between risk and uncertainty is especially important in decision making process, so in his point of view, the risk refers to a situation in which the decision-maker could assign probabilities to random events, while in the case of uncertainty this is not possible. In the case of uncertainty, can’t attach probability to a random event, because chance and odds characterize it better (Szász, 2011). While some authors deals with the dilemma between risk and uncertainty, another try to define the components of the risk, namely the uncertainty and variability (Molak, 1997; Cullen–Frey, 1999). Wilson and Shlyakhter (Molak, 1997) consider that the variability means the temporal and spatial heterogeneity of values. Because uncertainty is related with the lack of information, knowledge means that with information and knowledge acquisition it could be reduced. However, the variability couldn’t be reduced with further information and knowledge. At financial assets, information and knowledge plays an essential role, because certain economic news and information records sudden, unpredictable changes. In our opinion, in the case of financial assets, the information serves not only the risk minimization, but sometimes they even increase the risk level. It is clear, the riskiness and the return of financial assets, highly depends on kind, quantity and quality of information. Vose (2008) also considers risk consists of two parts, but he regards that variability is the special case of uncertainty. This kind of uncertainty and variability together is called by Vose total uncertainty. We can see therefore, in the foreign literature becomes more and more involved setting the components of risk, rather than the distinction between risk and uncertainty in the foundation of economic decisions (Tarnóczi-Fenyves, 2010). According to Tapiero (2004), the global financial crisis is not the consequence of lack of information, knowledge, but the investors and decision-makers’ “mental deficiency”, because they overestimated certain information and in the context of economic crisis, they overreacted it (Bélyácz, 2011). For investors who invest in risky assets, the risk is unavoidable, and the more they want to gain, the more they have to risk. About the financial investments’ risk, we consider the Molak and Cullen-Frey approach is more closely, because in the case of share prices, returns, risk displays in forms of variability and volatility. The risk, variability can’t be completely eliminated, but there are various risk minimization techniques, among which the best known is the diversification which is presented in famous work of Harry Markowitz “Portfolio 454 Selection” (Illés, 2007). In accordance with modern portfolio theory, a rational investor would not invest his money into a single financial assets, he shares it between various risk levels assets. In fact, this is the central role of portfolio theory. The investor can decide in accordance to relationship between risk-return, on how much is profitable to him to buy from some risky assets. The modern portfolio theory has a major impact on Capital Assets Pricing Model (CAPM) developing. The CAPM model developed a new guidance to relationship between risk and return. Based on Markowitz modern portfolio theory, Sharpe, Lintner and Treynor through their research leads to the conclusion that there is a strong correlation between market risk and assets’ expected returns. In this context, it is essential mapping and assessment of general and market risk. The one of the best known risk measurement method is the variance and standard deviation, which could be also, calculated function of probability. The variance, or squared deviation, could be defined like weighted average of the squared deviations between possible values, which in finance could be returns, losses and expected value. But neither the variance, neither the standard deviation are not a direct method of risk measurement, because express risk with deviation of return. Is cannot put equality between risk and deviation of return, so we can interpret the deviation of return like a proxy for risk (Holton, 2004). Both the high result of variance and standard deviations shows a high risk level, while low value shows the contrary. The standard deviation and variance it is also used for determining the risk of financial assets, but these methods express risk in absolute value, which is suitable only for comparing the identical returns’ assets (Illés, 2002). The relative standard deviation or coefficient of variance is one of the quantifying methods, which is more recommended by experts in risk measurement. The coefficient of variance is the ratio between assets standard deviation and assets return. A key role in financial instruments’ risk quantification plays a beta coefficient (β). Beta has an especially important significance in application of Capital Assets Pricing Model (CAPM), because measures the systematic, non-diversifiable, market risk level, using only one number. In fact, by knowing the systematic risk, the return of portfolio and the risk-free asset return, we could calculate the expected return of portfolio or asset. So, the beta coefficient is an expression of market risk level and also shows the sensitivity of financial asset to movements of market benchmark portfolio. A higher value of beta relates a higher level of risk and return (Mun, 2006). Mathematically, the value of beta is calculated like “ratio of covariance between an asset and market portfolio and market portfolio variance" (Illés, 2002: 141). When beta is equal with 1, it means that the asset return is near to market return. If beta value is less than 1 indicates a low sensitivity, otherwise the change of market factors has a little effect on asset return. If beta is greater than 1, it means that the asset is very sensitive to market changes, so changes of market risk factors cause more significant variation in return evolution (Aven, 2010: 45). The application of CAPM model was widely criticized, because according to some experts it’s impossible to characterize the systematic, macroeconomic risk factors through one number. During the CAPM model application, the model developer assumed that the financial markets are perfectly balanced, the investors have homogeneous expectations, but the current economic environment and recently developed financial turbulences has strongly refuted these assumes. A very serious weakness 455 of model is that market and inherent macroeconomic risk factors are completely static (Altăr, 2002: 70-71). A key component of financial decisions foundation is the portfolio performance measurement. The performance measurement has an essential role to investment decisions foundation and contributes to the adding value of successfulness of investment and risk minimization. Portfolio performance ratios answers for three very important questions: what is the return on asset, why has the portfolio performed that way, how can be performance improved (Bacon, 2008: 1). 3. Research methodology In the comparative analysis of two neighboring countries shares portfolio we used the shares basket of Romanian (BET) and Hungarian (BUX) main stock exchange indices. The data included in present study are these two countries main stock indices shares daily closing prices, for 6 months back. The data used were collected from the official databases of Hungary, Budapest Stock Exchange website: www.bet.hu and Romania, Bucharest Stock Exchange website: www.bvb.ro. The statistical analysis was built on the R statistical software system. In the R statistical system there are available all the packages (modules) which is necessary for this analysis. The R statistical system is open source software, that ensure many analyzing, modeling and visualization facilities and another advantage is that it could be connected with Excel spreadsheet, which permits the usage of different databases. In this study, we used the ‘PerformanceAnalytics’ module, because this package aims to aid us in using the latest research for analysis of return streams, such as stock returns and portfolio performance ratios. In portfolio’s financial assets selection, managing and establishing of efficient financial decisions, the risk, return and the relationship between risk-return determinations has an important function. In addition, in decision-making and risk minimization, the portfolio performance has an important role. Based on CAPM model, these two closely related concepts are used in portfolio performance ratios calculation. Foreign literature presents more performance based indicators, of which the well-known are Sharpe ratio, Treynor ratio and Jensen’s alpha. William Sharpe’s (1966) indicator is based on modern portfolio theory and the essence of ratio consist in showing how much is the reward for variability, so this is why in foreign literature this ratio is also called as “reward-to-variability ratio”. The Sharpe performance ratio is calculated according to (1) formula: ୔ൌ୉൫ୖ౦൯ିୖూ ஢ሺୖౌሻ (1) where, E(RP) – the expected return of the portfolio; RF – the return on the risk-free asset; σ(RP) – standard deviation of the portfolio returns. As it can be seen from the formula, the Sharpe ratio compares excess return above risk-free asset with total risk of portfolio (Amenc - Le Sourd, 2003: 109). The indicator can also be understood as the return per unit of variability. According to this, the higher value of Sharpe ratio indicates a more favorable risk-return combination (Bacon, 2008: 67). While Sharpe ratio is based on modern portfolio theory, the Jensen’s ratio or alpha (1968) is based on Capital Assets Pricing Model (CAPM) and can be described by 456 the following (2) correlation: Ƚ୔ൌ ሺ୔ሻെ୊െȾ୔ሺሺ୑ሻെ ୊ሻ (2) where, E(RP) – the expected return of the portfolio; RF – the return on the risk-free asset; βP – the systematic risk of portfolio; E(RM) – the expected return of market portfolio. Jensen assumed that the portfolios are not perfectly diversified and therefore there is part of portfolio return which is missed from CAPM model, which in fact will be explained by Jensen, through Jensen’s alpha. Essentially, the Jensen’s performance ratio compares the portfolio excess return above risk-free rate with return received by application of market model. If Jensen’s alpha has a positive result, means that the portfolio return is higher than the return received by using the CAPM model. The major weakness of this ratio consists in the fact that permits only the comparison of portfolios with similar risk levels. The Treynor’s performance ratio (1965), or otherwise “reward-to-volatility ratio” is also closely related with CAPM model. The indicator is very similar with Sharpe’s ratio, with the difference that Treynor compares the excess return above risk-free rate with the systematic risk, and not with the total risk of portfolio, how it is presented in the (3) formula: ୔ൌ୉൫ୖ౦൯ିୖూ ஒౌ (3) where, E(RP) – the expected return of the portfolio; RF – the return on the risk-free asset; βP – the systematic risk of portfolio; The indicator can be also explained as the return per unit of volatility. In case of this ratio, the portfolio with higher value will be preferable (Amenc - Le Sourd, 2003: 108). The Treynor ratio is a well-known indicator, but in practice it is rarely used, because not take into account the specific risk. If the portfolio is well diversified the Sharpe ratio and the Treynor ratio shows similar results. In this study, at computation of last two ratios we have used as benchmark portfolio the returns of Hungarian stock indices (BUX) return, for analyzed period. 4. Results of the research In analyzing of Hungarian and Romanian main indices shares basket returns, we start with presentation of portfolios returns distribution for studied period, which is illustrated in the Figure 1. 457 Figure 1: The distribution of Romanian and Hungarian portfolios returns Source: Own computation At first we can see, that the Hungarian portfolio returns is much closer to normal distribution then the Romanian. 50% of Hungarian portfolio returns are situated between -0.00630 and 0.00640, while 50% of Romania portfolio returns are situated between -0.0027 and 0.0050. We can also observe in the case of Romania, that the distribution of portfolio returns is much more right skewed, because there are some outliers returns near to 0.003, which is indicated by the value of skewness too, upper than 0. In the case of Hungarian portfolio this is not specific; here the value of skewness is closer to 0, and the histogram is moderate tailed. In terms of kurtosis, neither in two cases is not specific the normal distribution kurtosis, which represent a kurtosis value at 3. At Romanian returns distribution, this is upper than 3, which illustrates a slightly leptokurtic distribution, closer to normal distribution, while at Hungarian data kurtosis we can observe a kurtosis value lower than 3 and upper than -3, which is also further by recommended value. Table 1: Hungarian and Romanian returns statistics Hungarian returns Returns Density -0.02 -0.01 0.00 0.01 0.02 0 10 20 30 40 Romanian returns Returns Density -0.01 0.00 0.01 0.02 0.03 0 10 20 30 40 50 60 70 458 Source: Own computation In terms of data deviation between maximum and minimum value of returns, we can see the greater deviation in the case of Hungarian portfolio. Standard deviation illustrates the same fact, which means more significant variability, uncertainty in case of Hungarian portfolio returns. The LCL (Lower Confidence Level) and the UCL (Upper Confidence Level) Mean compute a confidence interval mean based on the StdDev (standard deviation) of analyzed data and the z value of 95% confidence interval. The lower and upper confidence level estimation gives an indication of how much is the uncertainty in true mean computation. The LCL Mean and the UCL Mean is more significant at Romanian portfolio in comparison with Hungarian portfolio. The results show that at Romanian shares returns, the uncertainty is greater than in the Hungarian case, on this aspect. For analyzing the portfolio performance and risk, it is important to interpret the indicators from Table 2. First three indicators illustrate the Sharpe ratios which measure the return per unit of risk by using different risk measure indicators as denominator: StdDev (standard deviation), VaR (value at risk) and ES (expected shortfall). By analyzing these three indicators, we can see that “the reward to variability” has greater values in the case of Romanian portfolio, what means a better combination of risk and return. The negative results for Hungarian Sharpe ratios are caused primarily by the negative values of returns. Analyzing Jensen’s alpha, we have see positive value in both of situations, which means that there is a part of return which isn’t it explained by using of CAPM model. Table 2: Performance and risk ratios of Hungarian and Romanian returns Hungarian portfolio returns Romanian portfolio returns StdDev Sharpe -0.03865 0.21857 VaR Sharpe -0.02244 0.23021 ES Sharpe -0.01842 0.15353 Jensen Alpha 0.02512 0.48000 Treynor Ratio -0.09639 3.75783 Semivariance 0.00965 0.00559 Hungarian portfolio returns Romanian portfolio returns Minimum -0.0231 -0.0114 Quartile 1 -0.0063 -0.0027 Median -0.0002 0.0004 Arithmetic Mean -0.0004 0.0015 Quartile 3 0.0064 0.0050 Maximum 0.0242 0.0307 LCL Mean (0.95) -0.0021 0.0002 UCL Mean (0.95) 0.0014 0.0029 StdDev 0.0094 0.0071 Skewness -0.1390 1.3089 Kurtosis -0.3682 3.3447