Forecasting risk using auto regressive integrated moving average approach: An evidence from S&P BSE Sensex

Challa, Madhavi Latha; Malepati, Venkataramanaiah; Kolusu, Siva Nageswara Rao Article Forecasting risk using auto regressive integrated moving average approach: An evidence from S&P BSE Sensex Financial Innovation Provided in Cooperation with: Springer Nature Suggested Citation: Challa, Madhavi Latha; Malepati, Venkataramanaiah; Kolusu, Siva Nageswara Rao (2018) : Forecasting risk using auto regressive integrated moving average approach: An evidence from S&P BSE Sensex, Financial Innovation, ISSN 2199-4730, Springer, Heidelberg, Vol. 4, Iss. 1, pp. 1-17, https://doi.org/10.1186/s40854-018-0107-z This Version is available at: https://hdl.handle.net/10419/237140 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ RESEARCH Open Access Forecasting risk using auto regressive integrated moving average approach: an evidence from S&P BSE Sensex Madhavi Latha Challa 1* , Venkataramanaiah Malepati 2 and Siva Nageswara Rao Kolusu 1 * Correspondence: saidatta2009@ gmail.com 1 School of Management Studies, Vignan’s Foundation for Science, Technology & Research, Guntur, Andhra Pradesh, India Full list of author information is available at the end of the article Abstract The primary objective of the paper is to forecast the beta values of companies listed on Sensex, Bombay Stock Exchange (BSE). The BSE Sensex constitutes 30 top most companies listed which are popularly known as blue-chip companies. To reach out the predefined objectives of the research, Auto Regressive Integrated Moving Average method is used to forecast the future risk and returns for 10 years of historical data from April 2007 to March 2017. Validation accomplished by comparison of forecasted and actual beta values for the hold back period of 2 years. Root-Mean-Square-Error and Mean-Absolute-Error both are used for accuracy measurement. The results revealed that out of 30 listed companies in the BSE Sensex, 10 companies’exhibits high beta values, 12 companies are with moderate and 8 companies are with low beta values. Further, it is to note that Housing Development Finance Corporation (HDFC) exhibits more inconsistency in terms of beta values though the average beta value is lowest among the companies under the study. A mixed trend is found in forecasted beta values of the BSE Sensex. In this analysis, all the p-values are less than the F-stat values except the case of Tata Steel and Wipro. Therefore, the null hypotheses were rejected leaving Tata Steel and Wipro. The values of actual and forecasted values are showing the almost same results with low error percentage. Therefore, it is concluded from the study that the estimation ARIMA could be acceptable, and forecasted beta values are accurate. So far, there are many studies on ARIMA model to forecast the returns of the stocks based on their historical data. But, hardly there are very few studies which attempt to forecast the returns on the basis of their beta values. Certainly, the attempt so made is a novel approach which has linked risk directly with return. On the basis of the present study, authors try to through light on investment decisions by linking it with beta values of respective stocks. Further, the outcomes of the present study undoubtedly useful to academicians, researchers, and policy makers in their respective area of studies. Keywords: Akaike Information Criteria (AIC), Bombay Stock Exchange (BSE), Auto Regressive Integrated Moving Average (ARIMA), Beta, Time series JEL classification: G12, G14, G17 Financia l Innovation © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Challa et al. Financial Innovation (2018) 4:24 https://doi.org/10.1186/s40854-018-0107-z Introduction The investments in stock market would produce profits as well as losses. But investors want to maximize their profits instead of losses by analyzing stock market conditions. This can be achievable when the risk is predictable in scientific manner. Risk aversion is one of the interesting and important discussions among cult of investors, policy makers, researchers, financial practitioners etc. The movements of returns and risks in the stock market had noteworthy inferences on portfolio diversification and stock market vulnerability. Both risks and returns are two sides of the coin and every successful step undertaken by the investors could be turned into high returns which depend on efficient and rational behavior of investors (Kaufman, G., 1995). In 1986 the BSE Sensex was started. Gradually it has become the primary proxy for the Bombay Stock Exchange (BSE) and considered as barometer of Indian economy. The free float methodology has been used to calculate index level at any point of time frame and it influences the market movement of 30 largest and actively traded stocks or companies relative to base period. Every day Stock movements are changed because of existence of volatility in market conditions. The BSE Sensex prices are goes up ward direction, when the stocks of major companies on BSE prices gone up and vice versa. Statement of the problem and research gap In recent past, the performance of the stock markets is considered as barometer of economy of a country. But, the appraisal of the performance of the stock market is not so easy in given social, economical and political environment. The stock markets are exhibiting more volatile across the world economies. These fluctuations in the prices of stocks are very wide in developing economies where information asymmetry plays vital role. India, as developing country, it is also not exceptional to the menace of unwanted and excess volatility in the prices of stocks listed in its respective stock markets. Many a times, Indian stock market has witnessed nightmare to millions of investors where they had lost their lifetime savings in a day. In this juncture, the present inquiry concentrates on systematic risk and expected returns of stock in the time horizon because these are uncertain and influences more in behavior of investor. Prediction of return to the given risk associated with a particular security is a common phenomenon but prediction of appropriate stock for investment merely on beta and testing their validity is certainly a novel and it would contribute some addition to the existing literature. Objectives of the study The primary objective of the study is to test the validity of prediction of fruitful stocks on the basis of their beta values with reference to BSE Sensex. Further, the following are the specific objectives. These are: to (i) Find the beta values of each stock listed on BSE Sensex and determine their degree of risk on the basis of betas so found; (ii) Compare and contrast each individual stock beta with portfolio’s (BSE Sensex) for investment decision making; and (iii)Validate the accuracy of forecasting risk with actual values over the holding period (test period) of two years. Challa et al. Financial Innovation (2018) 4:24 Page 2 of 17 Literature review Chan and Kwok (2017) examined the roles of risk-sharing and other factors in stock price revaluation during liberalization period in connection to Shanghai Stock Exchange. They have found that risk-sharing was a significant mechanism in price revaluation of stocks. Further, they found strong evidence that market imperfections hindered the incorporation of firm-specific information into stock prices. Furthermore, they added that market liquidity, information asymmetry, and insider trading played a negative role in market efficiency for price adjustment (Brunnermeier, M., Pedersen, L., 2009, Moussa, A., 2011). Asset pricing theory predicts that an asset’s expected return is positively associated with its exposure to systematic risk (De Bandt, O., Hartmann, P., 2000, Hansen, L.P., 2013). However, empirical evidence supporting this prediction has been mixed, as it is often found that systematic risk is not priced cross-sectional (e.g., Fama and French, 2004). Chari and Henry (2004)notethat stock market liberalizations reduce systematic risk because the relevant source of systematic risk becomes the world market; therefore, liberalizations represent an exogenous change that enables us to test the prediction from theory (Chakrabarty, K.C., (2012). Jyothi et al. (2017) explains under the unconditional idiosyncratic volatility specification, the firm size, cash flows to price ratio and market capitalizations are significant factors. Moreover they found that size, Liquidity, momentum returns are the determinants under the conditional idiosyncratic volatility. They support under diversified portfolio and suggest based on empirical findings that firm specific fundamentals are significant determinants of unsystematic risk. The conventional portfolio theory of finance holds that rational investors in perfect capital markets diversify unsystematic risk completely by holding uncorrelated assets in their portfolio (Markowitz 1952), and early theoretical models hypothesize that systematic market risk is the sole determinant of expected stock returns (e.g., Sharpe 1964; Lintner 1965; Black 1976). Bianconi et al. (2017) examines the effect of information on the top ten financial institution’s tail risk and systematic risk by using the information of observed prices of put and call options or newspaper articles. They use Garcia’s(2013) data as a measure of CBOE (Chicago Broad of Exchange) volatility Index VIX and market sentiment. They found that very little significant causality using dynamic feedback in terms of volatility. Research design and methodology To reach out the predefined objectives of the study, the data of each company registered on BSE Sensex and the data of BSE Sensex (as a portfolio) from Bombay Stock Exchange Ltd. has been collected for the period of 10 years. For this work, daily open and close prices of stocks registered in BSE Sensex are used as a data. The collected data of each company registered is planned to compare with BSE Sensex as a whole so as to calculate beta values for each company. BETA calculation Beta is a measure to determine the stock market volatility in terms of portfolio or security fluctuations used in fundamental analysis. When evaluating risk, the stock price Challa et al. Financial Innovation (2018) 4:24 Page 3 of 17 variability is an important consideration. If the risk could be as possibility of reduce the stock value, certainly beta suits as a proxy for risk. The systematic risk of a portfolio measures by Beta. Beta is an important element for capital asset pricing model (CAPM), which calculates the expected return and cost of equity and it is also called beta coefficient. Mathematical formula for Beta is Beta ¼Coveriance ri;rm ðÞ Variance rm ðÞ r i = return of an asset or security (a company registered with Sensex Index) r m = return of benchmark or market (Sensex Index) If the beta value of a S&P BSE Sensex index is 1.0, and individual companies (registered under S&P BSE Sensex) ranks are found according to this value. An individual stock that slaps more than the S&P BSE Sensex over the given period of time has a beta could be greater than 1.0. If individual security moves less than the index, that means the beta is less than 1.0. Higher the beta higher the risk as well as higher returns, low risk implies the low risk and low returns. In portfolio management, the scrip selection is usually based on beta of stocks. Portfolio could be followed in many created ways those are differentiated in several sectors, sector wise, and beta wise portfolio. ARIMA method The ARIMA method was established in 1990s for the prediction of time series(Box and Jenkins 1976).The ARIMA model main notion is over the given period of time it form a predicted object as a random series, to take a data series. To describe the series, a particular statistical technique could be established according to autocorrelation analysis of the time series. Once the technique construct, the future values could be predict through the past and present values of time series. Moreover, in the prediction process of ARIMA model, it considers dependency of the investigated process and the noise of volatility. An ARIMA(p,d,q) model is defined as an I(d) process whose d th integer difference followsa stationary ARMA(p,q) process. In polynomial form: Yt¼Xi¼1 p φiYt−1þεtþXi¼1 q θiεt−1ð1Þ Where. Y t = differenced time series value. φand θ= unknown parameters. ε= independent identically distributed error terms with zero mean. Y t could be expressed as past and current values as well as past error terms. In ARIMA model, stationarity, invertibility and parsimony are the three important parameters are used to identification, estimation and diagnostic checking respectively (Asteriou and Hall 2015). The mean, variance and covariance are constant over the given time in stationarity process. This could be accomplished by differing either the integrated order one (I (Akaike 1973)) or two(I (Akaike 1974)). In the process of invertibility, Y t measured by convergent autoregressive process (moving average of finite Challa et al. Financial Innovation (2018) 4:24 Page 4 of 17 order). Box and Jenkins assumed that parsimony is a model, which produce well forecasting with extra coefficients and degree of freedom would be affected. This model is better than an over parameterized model. In this study the ARIMA forecast for the period of March 2017 to April 2019 was modeled by organizing the EViews software which is applied for econometric analysis of time series. Data To reach out the predefined objectives of the study, as the study demands, researchers used exclusively secondary data. The present investigation conducted using daily open and close prices of BSE Sensex. The required data was collected from several resources such as published and unpublished research, magazines, and official website of the BSE. Table 1 Average year-wise beta values of companies listed in BSE Sensex during 2007–2017 Date 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Average Tata Motors DVR 0 4.78 0.48 0.73 1.25 1.48 1.05 1.75 1.4 1.41 1.43 ICICI Bank 1.08 1.45 1.29 1.32 1.32 1.58 1.46 1.46 1.36 1.62 1.39 Tata Steel 1.05 1.09 1.36 1.14 1.21 1.28 1.11 1.28 1.4 1.36 1.23 State Bank of India 1.14 1.08 1.03 0.87 1.15 1.42 1.18 1.31 1.3 1.56 1.20 Tata Motors 0.94 0.64 1.44 1.14 1.2 1.34 1.01 1.49 1.37 1.39 1.20 Axis Bank 0.53 1.46 1.1 1.08 1.23 1.34 1.46 1.39 1.13 1.24 1.20 Larsen & Toubro 1.14 1.08 1.26 0.84 1.2 1.48 1.37 1.29 1.16 1.12 1.19 Kotak Mahindra Bank 1.37 1.36 1.12 0.87 1.2 1.03 1.33 0.92 0.92 0.72 1.08 Adani Ports & SEZ 1.59 0.56 0.69 0.89 0.71 0.95 0.75 1.69 1.05 1.61 1.05 Reliance Industries 1.24 1.16 0.99 0.87 1.14 0.9 1.1 1.11 1.07 0.74 1.03 Mahindra & Mahindra 0.84 0.84 1.13 0.93 1.13 0.78 0.89 0.93 1.04 1.13 0.96 HDFC Bank 1.02 0.99 0.63 0.81 0.93 0.96 1.29 0.93 0.69 0.74 0.90 Oil and Natural Gas Corporation 0.39 0.54 0.7 0.5 0.7 1.09 1.16 1.31 1.16 0.62 0.82 Maruti Suzuki 0.75 0.66 0.6 0.54 0.73 0.69 0.71 1 0.81 1.17 0.77 BhartiAirtel 0.97 0.78 0.56 0.64 0.81 0.72 1.13 0.45 0.57 0.81 0.74 NTPC 1.04 0.68 0.54 0.38 0.74 0.68 0.69 1.07 0.69 0.86 0.74 ITC 0.48 0.59 0.84 0.53 0.55 0.73 0.9 0.62 0.91 0.94 0.71 Coal India 0.68 0.53 0.47 0.63 1.1 0.75 0.6 0.68 Power Grid Corporation of India 1.13 0.7 0.52 0.36 0.54 0.69 0.63 0.74 0.49 0.86 0.67 Tata Consultancy Services 0.67 0.95 0.62 0.74 0.87 0.57 0.62 0.49 0.55 0.45 0.65 Hero MotoCorp 0.6 0.3 0.58 0.55 0.53 0.53 0.74 0.72 0.82 1.01 0.64 Infosys 0.64 0.7 0.56 0.63 0.79 0.62 0.43 0.3 0.82 0.69 0.62 Cipla 0.34 0.41 0.5 0.44 0.49 0.57 0.44 0.66 0.9 0.51 0.53 Lupin 0.58 0.52 0.25 0.59 0.36 0.37 0.32 0.41 0.78 0.87 0.51 Sun Pharmaceutical 0.32 0.28 0.37 0.48 0.46 0.41 0.54 0.34 0.94 0.86 0.50 Wipro −0.14 0.63 0.92 0.67 0.73 0.54 0.34 0.38 0.48 0.43 0.50 Asian Paints 0.21 0.09 0.25 0.19 0.25 0.73 0.72 0.85 0.71 0.82 0.48 Hindustan Unilever 0.5 0.38 0.37 0.41 0.55 0.53 0.6 0.15 0.58 0.45 0.45 Dr. Reddy’s Laboratories 0.34 0.42 0.4 0.27 0.49 0.26 0.52 0.27 0.67 0.56 0.42 Housing Development Finance Corporation 0.55 0.48 0.43 0.43 0.49 −0.79 0.6 0.49 0.72 −0.46 0.29 Source: Compiled by authors Challa et al. Financial Innovation (2018) 4:24 Page 5 of 17 The collected was processed and tabulated as per the needs of the study. It covers the period of 10 years from April 2007 to March 2017. Scope and limitations of the study No study is exceptional to the completeness. The present study covers only BSE Sensex leaving all the sectorial indices incorporated within otherwise these would have included under the coverage of the study. Further, along the BSE, there other national stock exchanges which are also considered as imperative stock exchanges in Indian economy. But due to time, difficulty to arrive uniformity in some variables they were ignored for the study. Results and discussion The following paragraphs are devoted to discuss the results arrived through the methodology adopted for the purpose. Table 1exhibits average year-wise beta values of companies listed in BSE Sensex during 2007–2017. It can be understand that those companies’beta values which are having more than one are more volatile in movement of prices of stocks; those who are having less than one are less volatile. If the beta is near to one, the volatility of the movement of the prices of the market equals to that of the movement of the prices of individual company of portfolio/index. Usually, investors prefer less than 1 beta value companies because these stocks are having less volatility, low risk and also lower Fig. 1 Summary of average year-wise beta values of companies listed in BSE Sensex during 2008–17 Challa et al. Financial Innovation (2018) 4:24 Page 6 of 17 returns. Generally, higher beta means firms with more returns. Hence, expected growth is also associated with high risk and uncertainty. A glance at the Table 1reveals that the average beta of Tata Motors Differential Voting Right Share (DVR) is1.43 followed by ICICI Bank (1.39), Tata Steel (1.23) SBI (1.20), Tata Motors (1.20), Axis Bank (1.20), L and T (1.19), Kotak Mahendra (1.08), Adani Ports and SEZ (1.05), Reliance Industries (1.03) serially. The companies exhibiting highest volatility as the beta values of these companies are more than one. Further, HDFC, Dr. Reddy’s Laboratories, Hindustan Unilever, Wipro, Sun Pharmacetical, Lupin and Cipla companies exhibits low average beta values. The beta value of the companies under the study demonstrates moderate beta values ranges from 0.96 to 0.62. Out of 30 listed companies in the BSE Sensex, 10 companies’exhibits high beta values, 12 companies are with moderate beta values and 8 companies are with low beta values. It is interesting to note that Housing Development Finance Corporation (HDFC) exhibits more inconsistency in Table 2 ADF test result Companies T - Stats Adani Ports & SEZ −8.89914 Asian Paints −7.73641 Axis Bank −5.83681 BhartiAirtel −8.06226 Cipla −6.63636 Coal India −4.7765 Dr. Reddy’s Laboratories −8.09778 HDFC Bank −12.0394 Hero MotoCorp −5.88376 Hindustan Unilever −5.19454 Housing Development Finance Corporation −6.72789 ICICI Bank −9.00227 Infosys −9.01065 ITC −6.04998 Kotak Mahindra Bank −7.82484 Larsen & Toubro −6.70005 Lupin −9.07992 Mahindra & Mahindra −5.79195 Maruti Suzuki −6.08925 NTPC −8.75293 Oil and Natural Gas Corporation −6.41211 Power Grid Corporation of India −6.77208 Reliance Industries −6.04827 State Bank of India −6.96081 Sun Pharmaceutical −7.34285 Tata Consultancy Services −9.43277 Tata Motors −6.33196 Tata Motors DVR −8.21388 Tata Steel −8.57442 Wipro −8.71443 Source: Compiled by authors Challa et al. Financial Innovation (2018) 4:24 Page 7 of 17 terms of beta values though the average beta value is lowest among the companies under the study. Furthermore, it can be observed that HDFC and Wipro are two companies which exhibit negative beta values over the period of study (see Fig. 1). The following paragraphs present the sequence of results i.e., Identification, Estimation, Diagnostic check, Forecasting and Validation. Identification In this stage, the Augmented Dickie Fuller (ADF) test is used to ensure the level of data series is stationary or not. The result of ADF test shows that the series has achieved a stationary state. The stationary could be identified according to the t-stats value. If the t-stats Table 3 Akaike Information Criteria value (AIC) Companies Selected ARMA model AIC value Adani Ports & SEZ (Akaike 1974; Akaike 1979)(0,0) 2.419996205 Asian Paints (0,1)(0,2) 1.18300953 Axis Bank (Akaike 1974; Akaike 1976)(0,0) 1.412734755 BhartiAirtel (Akaike 1974; Akaike 1979)(0,0) 1.126284533 Cipla (0,2)(0,0) 1.036260176 Coal India (2,0)(0,0) 1.383862705 Dr. Reddy’s Laboratories (Akaike 1974)(0,0) 1.021199203 HDFC Bank (4,0)(0,0) 0.865074133 Hero MotoCorp (Akaike 1973; Akaike 1976)(0,0) 1.398471031 Hindustan Unilever (0,2) (Akaike 1973) 0.912515275 Housing Development Finance Corporation (0,0)(0,0) 3.890160664 ICICI Bank (0,2) (Akaike 1973; Akaike 1974) 0.576514289 Infosys (Akaike 1976) (Akaike 1973) 0.784789316 ITC (0,2)(0,0) 1.75706519 Kotak Mahindra Bank (Akaike 1973; Akaike 1974)(0,0) 1.126330283 Larsen & Toubro (Akaike 1974)(0,0) 1.669414571 Lupin (1,0)(0,0) 1.201107512 Mahindra & Mahindra (Akaike 1979)(0,0) 1.26508819 Maruti Suzuki (0,1)(0,0) 1.595029239 NTPC (Akaike 1974; Akaike 1976) (Akaike 1973; Akaike 1974) 0.916387604 Oil and Natural Gas Corporation (0,1)(1,0) 2.473119679 Power Grid Corporation of India (1,0)(0,0) 1.028937003 Reliance Industries (Akaike 1974)(0,1) 0.472093637 State Bank of India (0,1)(0,0) 2.040875765 Sun Pharmaceutical (Akaike 1974; Akaike 1979)(1,0) 1.113411517 Tata Consultancy Services (Akaike 1974; Akaike 1976) (Akaike 1973) 1.105677727 Tata Motors (Akaike 1974; Akaike 1976) (Akaike 1973) 1.542142809 Tata Motors DVR (Akaike 1974; Akaike 1976)(0,2) 1.827594151 Tata Steel (Akaike 1974)(0,0) 1.449933246 Wipro (0,0)(1,0) 3.207891726 Source: Compiled by authors Challa et al. Financial Innovation (2018) 4:24 Page 8 of 17 when the value of DW is less than 2. In the present study almost all companies DW is very close to 2, this value indicates the presence of positive serial correlation in the residuals. F-Statistic represents the regression output from the hypothesis test that all of the slope coefficients are zero. The p-value denoted as prob. (F-Stat), which specifies the marginal significance level of F-test. The null hypothesis could be rejected when the p-value is less than the significance level i.e. 0.05, which shows that all slope coefficients are equal to zero. In this analysis, all the p-values are less than the F-stat values. Therefore, the null hypothesis could be rejected. The p-value is not significant in the case of Tata Steel and Wipro, remaining all companies are having 5% significance level. Validation To measure the forecast of accuracy, authors run Forecast Evaluation model using E-views software for the sample period of April 2012 to March 2015, and evaluation sample period of April 2015 to March 2017. From the analysis, the accuracy Fig. 4 Forecasted beta values of companies listed in BSE Sensex using Auto ARIMA Table 8 Top 5 ranked companies in 3 divisions Moderate companies Rank Aggressive companies Rank Defensive companies Rank Housing Development Finance Corporation 1 State Bank of India 1 Hero MotoCorp 1 Dr. Reddy’s Laboratories 2 Tata Motors 2 Mahindra & Mahindra 2 Tata Consultancy Services 3 Tata Motors DVR 3 Asian Paints 3 Hindustan Unilever 4 ICICI Bank 4 Reliance Industries 4 Wipro 5 Adani Ports & SEZ 5 Maruti Suzuki 5 Source: Compiled by authors Challa et al. Financial Innovation (2018) 4:24 Page 15 of 17 of the forecasting could be found for the validation purpose. To confirm the quality of accuracy Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were calculated based on errors between forecasted and actual data, which is presented in Table 7. Table 7shows the results of validation or test results between the forecasted and actual values. The MAE is always less than the RMSE values in all the cases of registered companies in BSE Sensex, which indicates that the error percentage is very less and the values of actual and forecast showing the almost same results. Therefore, the estimation ARIMA could be acceptable, and forecasted beta values are accurate. Findings From the analysis, authors have found the future beta values for the period of April 2017 to March 2019. From the Fig. 4three categories of betas has been found. First one is moderate beta, which indicates the same instability as compared with Sensex and it equals to one. Second category is aggressive beta, which shows more instability when compared to Sensex and it express the beta greater than one. Last one is defensive beta; it represents the less instability with Sensex comparison and it is less than one. According to the category the companies also segregated for the convenience of investors. Lower rank represents the less risk and higher vice versa. Higher risk taking investors might get good returns in the future. Table 8shows the less risky companies under three divisions (Moderate, Aggressive, Defensive) of companies. The investors can take investment decisions according to their choice. Conclusion Forecasting with Auto ARIMA provides a prediction based on historical data, in which data has been applied by first order difference to remove white noise problems. In this analysis Auto ARIMA estimated AIC values, which yielded the more accurate forecast over the ten years period. In validation, the forecasted values are compared with actual values over the hold back period of two years. From this analysis the more uncertainty has been found when the forecast period is long term period, less uncertainty exists in the case of short term period. From the analysis the different investors can choose companies according to their risk aversion. Abbreviations ACF: Auto Correlation Function; ADF: Augmented Dickie Fuller; AIC: Akaike Information Criteria; AR: Auto Regressive; ARIMA: Auto Regressive Integrated Moving Average; ARMA: Auto Regressive Moving Average; BSE: Bombay Stock Exchange; DW: Durbin –Watson; MA: Moving Average; MAE: Mean Absolute Error; PACF: Partial Auto Correlation Function; RMSE: Root Mean Square Error; S.E of Reg: Standard Error Regression; SC: Schwarz Criterion; Tata Motors DVR: Tata Motors Differential Voting Right Share Availability of data and materials Source of Data sets are available in http://www.bseindia.com and http://finance.yahoo.com. Analyzed data uploaded as supplementary material files. Authors’contributions Study of conception and design: MLC, VM, SNRK. Acquisition of data: MLC. Analysis and interpretation of data: MLC. Supervision: VM, SNRK. Drafting of manuscript: MLC. Critical revision: VM, SNRK. All authors read and approved the final manuscript. Authors’information CH. Madhavi Latha received her MBA degree in Finance from Jesus PG College, affiliated by JNTUH, Hyderabad, Telangana, India. She is pursuing Ph.D at Vignan’s Foundation for Science, Technology & Research, Guntur, Andhra Challa et al. Financial Innovation (2018) 4:24 Page 16 of 17 Pradesh, India. She is also working as Assistant Professor in University of Gondar, Gondar, Ethiopia. She has more than 5 publications in various international Journals / Conferences. Her research areas include Capital Asset Pricing, Dynamic changes in Stock market and Stock holders Interest. Dr. Venkataramanaiah Malepati obtained his M.Com., M.Phil., and PhD from Sri Venkateswara University, Tirupati and MBA from Pondicherry University. At present, he is Professor of Management, Department of Management Studies at Golden Valley Instigated Campus, Madanapally. So far, he has published 34 research papers/articles in reputed/referred national/ international journals. Almost, the same number of papers/research articles has been presented in different national and international conferences/seminars. At present five books on his credit. Dr. K. Siva Nageswara Rao is working as an Assistant Professor of School of Management Studies at Vignan Foundation for Science, Technology & Research, Guntur, Andhra Pradesh, India. He received his MBA degree in Finance from Acharya Nagarjuna University, Andhra Pradesh, India and M.Phil Degree in Finance from MS University, Tiruelveli, Tamilnadu. He received his Ph.D degree from Acharya Nagarjuna University Guntur, Andhra Pradesh, India. He has 15 years of teaching experience. He has more than 20 publications in various National and International Journals/ Conferences. His main research interest includes Finance and Entrepreneurship. Competing interests Authors declare that they have no competing interests. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Author details 1 School of Management Studies, Vignan’s Foundation for Science, Technology & Research, Guntur, Andhra Pradesh, India. 2 Institute of Management Studies, Golden Valley Integrated Campus (GVIC), Madanapalli, Chittoor 517 325, India. Received: 1 February 2018 Accepted: 1 October 2018 References Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csaki BF (eds) Second international symposium on information theory. AcademiaiKiado, Budapest, pp 267–281 Akaike H (1974) A new look at the statistical model identification. 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