Relationship between Stock Return Volatility and Operating Performance with Stock Returns

Transcription

1 Relationship between Stock Return Volatility and Operating Performance with Stock Returns Farzin Rezaei 1, Elaheh Afsari 2, * 1 Assistant Professor Of Accounting, Department Of Management And Accounting Qazvin Branch, Islamic University, Qazvin,Iran 2 Master Student Of Business Management Department Of Management And Accounting Qazvin Branch, Islamic University, Qazvin,Iran ABSTRACT The aim of this study is to examine the relationship between stock return volatility and stock returns and operating performance of the companies listed in Tehran Stock Exchange. To address this issue through the six hypotheses and information about 93companies listed in Tehran Stock Exchange for the period between 2004to 2011wereanalyzed. The results showed that the correlation between variable levels of operating performance and stock return volatility and adjusted operating performances negative. The results show that lowlevel stock return volatility variables had a significant positive correlation with adjusted operating performance. Also the result showed that return spread of the value variable had positive and significant effect on return spread of size had negative and significant effect on stock returns. KEYWORD Stock return volatility, operational performance, industryadjusted operating performance, return on equity, stable-lessvolatile, Strong-less-Weak, High-less-Low, Small-less-Big. F INTRODUCTION inancial markets are one of the basic markets of each country. One of the important components of financial markets is stock exchange market [1]. Prior studies have documented that low volatility stocks tend to outperform high volatility stocks. Low volatility indeed leads to stronger operating performance; the low volatility improves achieving company to capital. The lowest volatility stocks have high future return averagely compared to the stocks in other volatility groups [6]. We can say the link between low volatility and strong operating performance leads that low volatility stocks experience high stock return as providing the investigation of opposite the volatility rule (Contrarian effect) in various levels. THEORETICAL BASICS One of the main criteria in decision making in stock market is stock return. Stock return alone has information content and most of potential and actual investors use it in financial analysis and predictions. The stock return of various companies in stock market is defined as the percent of price changes in a definite period. The benefit or loss of capital is a part of return achieved by the investor due to investment on stock or another other investment as calculated by stock price change. Stock market volatilities play important role in decision making of market investors and managers of companies. Thus, exact and clear recognition (scientific identification) of stock volatilities and its relation with stock return is of great importance for stock market participants [5]. By analysis of volatilities, we can present the analysis of past, present and future trend to the stakeholders [14]. The benefits of the study of stock return volatility by the investors is as they consider stock return volatility as a criterion of risk and capital market policy makers can use this criterion as a tool to measure vulnerability of stock market [3]. Operating performance is a measure to evaluate achieving organizational goals. The evaluation of performance means assessment of activities, measurements and past decisions of a person or an organization. High inclination is to evaluate the performance of investment companies in capital market as performance assessment of investment companies is as the assessment of performance of investment experts [2]. Momentum strategy includes investment on market and states that positive or negative return of the past was continued in definite period of future. Contrarian strategy states that majority people in market are wrong and recent trends of prices will return [4]. Telephone Number: *Corresponding Author: Elaheh Afsari afsari.elaheh@yahoo.com

2 The usefulness of stock return volatility study from investors is as they consider stock return volatility as a criterion of risk and capital market policy makers use this criterion as a tool to measure stock market vulnerability [15]. Small companies experience high return volatilities. It is expected that there is a negative relation between book value to market value and abnormal return volatility as the companies with bigger growth opportunities, have high stock return volatility [12]. REVIEW OF LITERATURE Kang(2012) studied the stock volatilities. His findings showed that stock volatilities with low risk can achieve high return compared to the stock volatilities with high risk stocks. Martin (2012) in a study on operating performance showed that the companies with low volatilities can have better performance to the market expectations. Baker et al., (2011) evaluated stock volatility with return. Their findings showed that find that, for the US, stocks in the bottom volatility-quintile on average earn higher future returns than do stocks in the other volatility quintiles. Bohl & et al. (2009) evaluated the relationship between institutional investors and stock return volatility. Their findings showed that increase of ownership of institutional investors had fixing effect on stock return volatility as they regulated stock price rapidly with new information and made stock market as efficient. Parikakis & Syriopoulos (2008) in a study "Contrarian strategy and overreaction in foreign exchange markets found that overreaction was in US, Brazil and Turkey and under reaction in Britain. Core et al., (2006) evaluated stock volatility. The results showed that low volatility stocks had strong operating performance as low volatility improves attaining capital by company. Fakhari and Taheri (2010) in the study evaluated the relationship between institutional investment and stock return volatility of the companies listed on TSE. The findings showed that the presence of institutional investors increased the control on managers performance and reduced information asymmetry and by increasing ownership percent of this group of shareholders reduced stock return volatility. Fadayinejad and Sadeghi (2006) investigated the benefits of momentum and contrarian strategies. Their studies showed that in short-term time horizons (1-6 months), momentum strategies were useful and in long-term horizons (12 months or above), contrarian strategies led into high return. SAMPLE SELECTION CRITERIA The study population is all non-financial companies listed on TSE during The study samples are selected based on the following criteria: 2. The financial reporting period of the sample companies to 12329/13XX of each year. 3. The trading pause of maximum 6 months per year. 4. The data are available. 5. The equity of companies is not negative. DATA ANALYSIS METHOD This study is applied in terms of purpose and descriptive in terms of data collection method and is correlation in terms of type. For hypotheses test, pooled regression model is used and at first Kolmogrov-Smirnov test is performed to evaluate the variables normality. The results of S-K test show that study variables are distributed normally, then we can use regression model analysis. To evaluate the auto-correlation between the variables, test is used. To collect data of theoretical basics and review of literature, library study method is used and for financial collection information, the existing data in TSE are used. The required financial data are extracted from financial reports of companies, formal TSE site and databases software of Rahavard Novin. The measurement of study variables is shown in Table 1. STUDY HYPOTHESES A) There is a negative relationship between high volatility of stock return and performance. A,A) There is a negative relationship between high volatility of stock return and operating performance. A,B) There is a negative relationship between high volatility of stock return and adjusted operating performance. B) There is a positive relationship between low volatility of stock return and performance. B,A) There is a positive relationship between low volatility of stock return and operating performance. B,B) There is a positive relationship between low volatility of stock return and adjusted operating performance. C) Return spread of stability (Stable-less-volatile) is effective on stock return of companies. D) Return spread of operating performance (Strong-lessweak) is effective on stock return of companies. E) Return spread of value (High-less-low) is effective on stock return of companies. F) Return spread of size (Small-less-big) is effective on stock return of companies. HYPOTHESES TEST The study aimed to recognize the relationship between stock return volatility, operating performance with stock return. The study hypotheses test is raised as: 1. They are not non-manufacturing companies. Due to the difference in performance nature of manufacturing companies from others, insurance, investment, holding, banks, financial brokerage were excluded from study.

3 Tab 1: The measurement of study variables Calculation method Sign name OP it = FEBIT it FTA it FEBIT it = Profit before interest and tax of company I at the end of year t. FTA it Total assets of company I at the end of year t FEBIT it = NI+TAX+INT NI net income TAX Tax cost of company and INT financial costs of company. OP it FEBIT it Operating performance Profit before interest and tax It is extracted from profit and loss account. TAX Tax It is extracted from profit and loss account. INT Financial cost SIZE it = log TA it It is calculated via natural logarithm of total assets. FEBIT it MIEBIT it FTA it MITA it MIEBIT it =Profit before interest and tax of industry MITA it =Total assets of industry Dependent SIZE it Firm size Control Adj op Adjusted operating performance Dependent Stock market price Issued stock+ Total book assets-book value H.s.s MVA Assets market value QTOBIN it = MVA TA MVA TA = MVE + TD it TA it MVE=Stock market value I at the end of year t TD=Total debts of company I at the end of year t TA=Total assets of company I at the end of year t QTOBIN it Growth opportunity )Investment) Control CA it CR it = CL it CA it -The current assets of company I at the end of year t CL it =Current debts of company I at the end of year t CR it Control LEV it = TD it TA it LEV it Leverage (debt ratio) Control CAPEX it = TFA it TFA it 1 TA it TAF it :Total fixed assets of company I at the end of year t CAPEX it Capital expenditures Control Extraction from stock software (Rahavard Novin) R it Stock return Dependent R PL : Portfolio return with standard deviation of low monthly price return R PH : Portfolio return with standard deviation of high monthly price return R PL R PH Return volatility) Stable-less-volatile ) R PS : Portfolio return with weak operating performance R PW : Portfolio return with strong operating performance R PS R PW Operating performance ) Strong-less-weak ) R PS :Portfolio return with small size R PB : Portfolio return with big size R PS R PB ) Small-less-big ) R PH : Portfolio return with low stock return value R PL : Portfolio return with high stock return value R PH R PL Value ( High-less-low )

4 A) First sub-hypothesis test of the first main hypothesis independent variable (high stock return volatility) is less than The results of model test for first hypothesis are shown in 5% and by confidence interval 95% we can say there is a Table 2. negative and significant relationship between operating performance and high volatility of stock return. Tab 2: The first sub hypothesis test from the first main hypothesis OperPerformance i,t = + α 1 HVOL i,t 1 + α 2 CR i,t 1 + α 3 LEV i,t 1 + α 4 QTOBIN i,t 1 + α 5 SIZE i,t 1 + α 6 CAPEX i,t 1 + ε i,t HVOL i,t CR i,t LEV i,t QTOBIN i,t SIZE i,t CAPEX i,t 1 Capital expenditure ratio )( Chow F statistics (significance) )( Hausman statistics () Panel data with fixed effects () F Fisher statistics (significance) of determination Also, the results of Table showed that there was a significant relation between control variables of current ratio, debt ratio,, size, capital expenditure ratio with stock return. The value 1.69 for statistics (ranging 1.5 to 2.5) shows non-autocorrelation between the variables. F B) Second sub-hypothesis test of the first main hypothesis The results of model test for second hypothesis are shown in Table 3. Tab 3: second sub hypothesis test from the first main hypothesis Adj_OperPerformance i,t = + α 1 HVOL i,t 1 + α 2 CR i,t 1 + α 3 LEV i,t 1 + α 4 QTOBIN i,t 1 + α 5 SIZE i,t 1 + α 6 CAPEX i,t 1 + ε i,t HVOL i,t CR i,t LEV i,t QTOBIN i,t SIZE i,t CAPEX i,t 1 Capital expenditure ratio )0.888( Hausman statistics () Normal pooled data )( of determination 1.64

5 independent variable (high stock return volatility) is higher than 5% and we can say there is no significant relation between high volatility of stock return with adjusted operating performance. Also, the results of Table showed that there was a significant relation between control variables of current ratio, debt ratio,, size, capital expenditure ratio with stock return. The value 1.64 for statistics (ranging 1.5 to C) First sub-hypothesis test of the second main hypothesis The results of model test for first hypothesis are shown in Table 4. Tab 4: first sub hypothesis test from the second main hypothesis OperPerformance i,t = + α 1 LVOL i,t 1 + α 2 CR i,t 1 + α 3 LEV i,t 1 + α 4 QTOBIN i,t 1 + α 5 SIZE i,t 1 + α 6 CAPEX i,t 1 + ε i,t LVOL i,t CR i,t LEV i,t QTOBIN i,t SIZE i,t CAPEX i,t 1 Capital expenditure ratio )( 3.79 )( 46.1 Hausman statistics () Panel data with fixed effects )( of determination 1.68 independent variable (Low stock return volatility) is higher than 5% and we can say there is no significant relation between low volatility of stock return with operating performance. Also, the results of Table showed that there was a significant relation between control variables of current ratio, debt ratio,, size, capital expenditure ratio with stock return. Tab 5: The second sub hypothesis test from the second main hypothesis The value 1.68 for statistics (ranging 1.5 to D) Second sub-hypothesis test of the second main hypothesis The results of model test for first hypothesis are shown in Table 5. Adj_OperPerformance i,t = + α 1 LVOL i,t 1 + α 2 CR i,t 1 + α 3 LEV i,t 1 + α 4 QTOBIN i,t 1 + α 5 SIZE i,t 1 + α 6 CAPEX i,t 1 + ε i,t Capital expenditure ratio )0.886( --- Hausman statistics () Normal pooled data )( of determination 2.08

6 independent variable (Low stock return volatility) is less than 5% and by confidence interval 95%, we can say that there is apositive and significant relation between low volatility of stock return with adjusted operating performance. Also, the results of Table showed that there was a significant relation between control variables of current ratio, debt ratio,, size, capital expenditure ratio with stock return. The value 2.08 for statistics (ranging 1.5 to E) The test of other study hypotheses (third to sixth) The results of model test for hypothesis (third to sixth) are shown in Table 6. Tab 6: The test of hypothesis 3-6 Return i,t = + α 1 SLV i,t + α 2 SLW i,t + α 3 HLL i,t + α 4 SLB i,t + ε i,t SLV i,t Return spread of stability SLW i,t Return spread of operating performance HLL i,t Return spread of value SLB i,t Return spread of size )0.172( Hausman statistics () Normal pooled data )( of determination 1.94 A,A) The result of third hypothesis of study test independent variable (return spread of stability) is higher than 5% and it shows the lack of significant effect of return spread of stability on stock return. A,B) The result of fourth hypothesis of study test independent variable (return spread of operating performance) is higher than 5% and it shows the lack of significant effect of return spread of operating performance on stock return. A,C) The result of fifth hypothesis of study test independent variable (return spread of value) is less than 5% and it shows the positive and significant effect of return spread of stability on stock value. A,D) The result of sixth hypothesis of study test independent variable (return spread of size) is less than 5% and it shows the negative and significant effect of return spread of size on stock return. The value 1.94 for statistics (ranging 1.5 to CONCLUSION This study aimed to evaluate whether stock return volatility and operating performance was effective on stock return or not. To do this, six hypotheses are analyzed. Test 1 indicates negative significant relationship between high volatility of stock return with operating performance. This hypothesis is supported. Test 2 indicates no significant relationship between high volatility of stock return with adjusted operating performance. This hypothesis is not supported. Test 3 indicates no significant relationship between low volatility of stock return with operating performance. This hypothesis is not supported. Test 4 indicates a positive significant relationship between low volatility of stock return with adjusted operating performance. This hypothesis is supported. Based on the results, we can say the stocks in the bottom volatility-quintile on average earn higher future returns than do stocks in the other volatility quintiles. Also, the show that low volatility effect is as skillful investors should rely on a criterion and they are unable to use an arbitrage opportunity as it systematically is a way to obtain high return with less risk. Also, the stocks with low volatility have strong operating performance, as low volatility improves achieving to capital in a company.

7 In an efficient market, there is a relationship between stock return and unexpected nature of profit but not only between stock return and earnings per share. The result of this hypothesis is consistent with the study of Baker et al., (2011), Core et al., (2006). Test 5- The test of other study hypotheses (3-6) The result of third hypothesis test indicates the lack of significant effect of this variable on stock return. Thus, third hypothesis of study is not supported. The result of fourth hypothesis test indicates no significant effect of this variable on stock return. Thus, fourth hypothesis of study is not supported. The result of fifth hypothesis of study indicates positive and significant effect of this variable on stock return. Thus, fifth hypothesis of study is supported. The result of sixth hypothesis test indicates negative and significant effect of this variable one stock return. Thus, sixth hypothesis of study is supported. International Business and Finance, pp , [12] Petersen, M.A., Estimating standard errors in finance panel data sets:comparing approaches, Review of Financial Studies 22, pp , [13] Rajgopal, S., M. Venkatachalam, Financial reporting quality and Idiosyncratic return volatility, Jaournal ofaccounting and Economics 51: pp 1 20, [14] Zhu, J, Pricing volatility of stock returns with volatile and persistent components, Financ Mark Portf Manag 23: pp , [15] Zafar, N. Urooj, S.F.,Durrani, T.K., Interest rate volatility and stock return and volatility, European journal of economic, issue 14, REFERENCES [1] Davarzade Mahtab, The prediction of stock price index in TSE. An approach to technical analysis. MA thesis. Administrative and economy science department. Isfahan University, [2] Rai, Reza, Saeedi, Ali., The basics of financial engineering and risk management, Tehran University, Management: SAMT publications, Tehran: Samt, [3] Fakhari, Hossein, Taheri, Esmatolsadat, The investigation of the relationship between institutional investors and stock return volatility of the companies listed on TSE, The journal of financial accounting research, No. 4.,pp , [4] Fadayinejad, M., Sadeghi, M., The study of usefulness of momentum and contrarian strategies, Management Message. No. 18, pp. 7-31, [5] Motameni, M., The analysis of return volatility in stock market of Tehran. MA thesis. Economic and administrative sciences department of Mazandaran University, [6] Baker, M., Bradley, B., Wurgler, J., Benchmarks as limits toarbitrage:understanding thelow-volatility anomaly. Financial Analysts Journal 67, pp.40 54, [7] Bohl,M., Brzezcynski,J., Wilfling,B., Institutional investors and stock returns Volatility, Emperical evidence from a natural experiment Journal of Banking & Finance, Vol. 33, pp , [8] Core, J.E., Guay, W.R., Rusticus, T.O., Does weak governance cause weak stockreturns? An examination of firm operating performance and investor sexpectations. Journal of Finance 61, pp , [9] Kang, X., Evaluating Alternative Beta Strategy, S&P Dow Jones Indices Research Paper, [10] Martin, I., On the valuation of long-dated assets, Journal of Political Economy120, , [11] Parikakis,G & Syriopoulos, Contrarian strategy and overreaction in foreignexchange markets, Research in