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The present study intends to find out the strategic risk factors and their influence on the Indian capital market (stock market) by using monthly time series data from April 1999 to March 2015. It used factor analysis, lag length analysis, break point test, unit root test and Johansen conintegration analysis. Results show that global financial markets, price-earnings ratio, inflation, industrial production, forex rate and dividend yield have significant impact on Indian stock markets. The short run analysis suggests that Indian stock prices are adjusted monthly by its previous month levels as well as previous month’s global stock markets and consumer price index (inflation) in short run (monthly) basis. The study concludes that in long run global financial markets, price-earnings ratio, inflation, industrial production, forex rate and dividend yield acts as source of systematic risk factor for Indian stock markets while in short run previous levels of stock market and inflation acts as systematic risk factor for Indian stock markets.

The understanding of risk and its sources has been a serious area of concern for investors while making their investment decisions. For long scholars have been putting efforts to identify the risk factors for stock markets as equity risk premium can be quantified only after identifying these risk factors. The available literature of risk in the area of finance classifies risk factors in to two categories: systematic risk factors and unsystematic risk factors. Classification of this type of risk is done from portfolio diversification perspective and standard deviation of total returns has been taken as a proxy of total risk. Total risk is further classified into unsystematic and systematic risk factors. The systematic risk is also termed as market risk in finance literature and is defined as risk coming from external environment or market that affect all firms irrespective of their individual performance. This risk is also defined as uncontrollable risk. On the other hand, unsystematic risk factors are unique risk factors and specific to performance of individual firms. Systematic risk is considered as non-diversifiable risk while unsystematic risk is considered as diversifiable risk. It is also believed that expected return to any investor is a function of systematic risk and an investor is compensated for taking systematic risk.

The available literature observed that asset pricing theories talks about risk factors but failed to identify the exact risk factors for stock markets. There is no consensus among the scholars about the rational of selecting a set of risk factors and number of risk factors. Ross [

Asset pricing theories explains the relationship between risk and returns but no asset pricing theories specify the risk factor that drives the stock returns. Asset pricing models of Sharpe [

Gjerde and Saettem [

Based on literature review, this research found variables representing goods market, money market, foreign exchange market and domestic and global capital markets. Using relevant statistical test these sampled variables were finally reduced to manageable priori variables. The relationship of these variables is also hypothesized before reaching to conclusion and construction of a model.

The rationale of this research was to identify strategic risk factors for Indian stock markets between April 1999 to March 2015 using monthly data series using Eviews 8.0, as longer series of data was not available and monthly data series seemed to be satisfactory for intended empirical analysis. The study found initially 16 relevant macroeconomic variables from available finance literature as a proxy of systematic risk factors to find the relevant systematic risk factors for Indian stock markets. Data related with wholesale price index (WPI), consumer price index (CPI), the industrial production index (IPI), money supply (M3), imports and exports to derive net exports (NX), net foreign institutional investment (FII), Foreign exchange reserve of Government, foreign exchange rate of Indian rupee with US dollar(EXRATE), Yield on 91-days treasury bills, Average monthly call money rates, interest rate on 10 years government bond, fiscal deficit of government of India, dividend yield of Indian markets, price earnings ratio of Indian markets (PE) and International crude oil prices (ICOP) were collected from database of Reserve Bank of India. Morgan Stanley capital market international world index (MSCI) was the proxy of global stock markets reflecting influence of global factors.

Finally seven factors were extracted from these 16 variables appropriate scrutiny and validation process represented as a priori variables. These final seven factors for the study were industrial production index (IPI), interest rate (INT) on 10 years government bonds, foreign exchange rate of Indian rupee with US dollar (EXRATE), CPI as proxy of Inflation, MSCI global index (MSCI), dividend yield (DY) and price earnings ratio of Indian stock market(PER). To validate the selection of these seven factors the study performed principal component analysis using SPSS 20.0. Although factors analysis reduced the sampled variables to five factors (IPI, INT, EXRATE, CPI and DY) only but refereeing the available literature two more additional factors MSCI and PE were employed.

The industrial production index (IPI) is used as a proxy of goods market, interest rate (INT) on 10 years government bonds is used as a proxy of money market, foreign exchange rate of Indian rupee with US dollar (EXRATE) is used as a proxy of foreign exchange market, capital market is represented by SENSEX, dividend yield (DY) and price earnings ratio of Indian stock market (PER) and MSCI represented global financial markets. The hypothesized relationship of these variables is given in following

The relationship of IPI with stock price is hypothesized positive as higher production leads to better performance of corporate resulting in higher profitability and stock prices. An increase in production out will lead to increased cash

S. No. | Variable | Symbol |
---|---|---|

1 | Wholesale price index | WPI |

2 | Consumer price index | CPI |

3 | Industrial production index | IPI |

4 | Money supply | M3 |

5 | Net exports | NX |

6 | Foreign institutional investment | FII |

7 | Foreign exchange reserve of Government, | FOREX |

8 | Net foreign exchange rate of Indian rupee with US dollar | EXRATE |

9 | Price earnings ratio | P/E |

10 | Dividend yield | DY |

11 | MSCI global index | MSCI |

Variable | Hypothesized Relationship |
---|---|

Industrial production index (IPI) | Positive (+) |

Interest Rate (INT) | Negative (−) |

Exchange Rate (EXRATE) | Negative (−) |

Consumer Price Index (CPI) | Negative (−) |

MSCI | Positive (+) |

Dividend yield | Positive (+) |

Price Earnings ratio | Positive (+) |

flows in the future. The relationship of interest rate and stock prices is hypothesized as negative because higher interest rates will lead to higher required rate of return for equity investors resulting in fall in stock prices. Also increase in interest rate will increase cost of capital for corporate and will put pressure on their profit margins. The relationship of foreign exchange rate with stock prices is hypothesized negative as Indian economy has negative trade balance and there is negative impact of stock market, whenever rupee is depreciated against dollar. Inflation is a double edge sword as it has some positive as well as negative impact on stock prices. But in India context higher inflation is directly linked with higher interest rate and vice versa. So relationship of CPI (proxy of inflation) with stock prices is hypothesized negative. The proxy of global financial market MSCI may affect Indian stock markets in either way (positively or negatively) but the study hypothesized a positive relationship of MSCI with stock prices. The study hypothesized a positive relation of dividend yield and price earning with stock prices. In short run after dividend announcement stock price may fall but in long run it has a positive impact on stock prices. Similarly high PR will boost confidence of investors leading to higher stock prices.

A battery of econometric tests has been employed to analyse short-run and long- run integration of systematic risk factors for Indian stock market. Johansen cointegration method was used to analyze the long-run integration of Indian stock markets with identified systematic risk factors. The model chosen is mentioned below:

Δ SENSEX = f ( Δ IPI , Δ INT , Δ EXRATE , Δ CPI , Δ MSCI , Δ DY , Δ PER ) (1)

However for further analysis of vector autoregressive model (VAR) that has constant but no trend and having breakpoint dummy as exogenous can be presented as follows:

Y t = μ 0 + ∑ k = 0 n β i Y t − i + ϕ D t + u t (2)

where

Y t = ( LNSENSEX , LNIPI , LNINT , LNEXRATE , LNCPI , LNMSCI , LNDY , LNPER )

A 8 × 1 vector of I(1) was the internal variable in the model and D_{t} is breakpoint dummy external variable. μ_{0} and μ_{t} are constant and white noise respectively.

To conduct the Johansen cointegration analysis the VAR equation in (2) was turned into vector error correction model (VECM) by introducing an error correction term ECM_{-1} in to the model. The new model is as follows:

Y t = μ 0 + ∑ i = 1 p − 1 ' Γ i Δ i Y t − i + δ E C M − 1 + ϕ D t + ε t (3)

or,

Y t = μ 0 + ∑ i = 1 p − 1 ' Γ i Δ i Y t − i + α β ′ Y t − 1 + ϕ D t + ε t (4)

where ε t ~ i i d N ( 0 , π ) .

The descriptive statistics of the eight variables (which include the seven independent variables and the SENSEX) are reported in

The precondition of Johansen cointegration test was that the variables must be non-stationary at level and stationary at first difference. Conintegration analysis only uses variables those are non-stationary with unit root. The study tested that the used series are stationary or not with the help of unit root tests by using Augmented Dickey Fuller test (ADF) and Phillips-Perron test (PP). The study

LNSENSEX | LNIPI | LNINT | LNEXRT | LNCPI | LNMSCI | LNDY | LNPE | |
---|---|---|---|---|---|---|---|---|

Minimum | 7.9787 | 4.9857 | 1.6303 | 3.6731 | 6.0661 | 5.1797 | 0.7031 | 2.4749 |

Maximum | 10.2734 | 5.9953 | 2.4757 | 4.1550 | 7.1460 | 6.0695 | 2.8356 | 3.3807 |

Mean | 9.1663 | 5.5223 | 2.0667 | 3.8654 | 6.4842 | 5.7061 | 1.2351 | 2.9042 |

Std. Deviation | 0.7121 | 0.3143 | 0.1904 | 0.1145 | 0.3407 | 0.2170 | 0.2957 | 0.1843 |

Skewness | −0.2861 | −0.2302 | −0.0182 | 1.0291 | 0.5341 | −0.3911 | 0.9086 | 0.0173 |

0.1754 | 0.1763 | 0.1754 | 0.1754 | 0.1768 | 0.1754 | 0.1873 | 0.1873 | |

Kurtosis | −1.4440 | −1.4753 | 0.3305 | 0.4579 | −1.1667 | −0.4781 | 3.9992 | −0.1886 |

0.3491 | 0.3509 | 0.3491 | 0.3491 | 0.3518 | 0.3491 | 0.3725 | 0.3725 |

LNSENSEX | LNIPI | LNINT | LNEXRT | LNCPI | LNMSCI | LNDY | LNPE | |
---|---|---|---|---|---|---|---|---|

LNSENSEX | 1 | 0.951^{**} | 0.045 | 0.383^{**} | 0.878^{**} | 0.747^{**} | 0.586^{**} | 0.403^{**} |

LNIPI | 1 | −0.106 | 0.438^{**} | 0.414^{**} | 0.558^{**} | 0.370^{**} | 0.171^{*} | |

LNINT | 0.038 | 0.040 | 0.436^{**} | 0.145 | 0.430^{**} | |||

LNEXRT | 1 | 0.697^{**} | 0.221^{**} | −0.472^{**} | −0.435^{**} | |||

LNCPI | 1 | 0.542^{**} | 0.199^{**} | 0.110 | ||||

LNMSCI | 1 | 0.773^{**} | 0.716^{**} | |||||

LNDY | 1 | 0.661^{**} | ||||||

LNPE | 1 |

**Correlation is significant at the 0.01 level (2-tailed). *Correlation is significant at the 0.05 level (2-tailed).

Variables | Augmented Dickey-Fuller (ADF) | Phillips-Perron (PP) | ||||
---|---|---|---|---|---|---|

Model A: (intercept, no trend) | Model B: (intercept with trend) | Model C: (nointercept, no trend) | Model A: (intercept, no trend) | Model B: (intercept with trend) | Model C: (nointercept, no trend) | |

At Level | ||||||

LNSENSEX | −0.6123 | −2.2468 | 1.5644 | −0.7255 | −2.1090 | 1.8228 |

LNIPI | −1.1558 | −1.2804 | 1.8924 | −1.0025 | −5.0973 | 4.0377 |

LNINT | −2.3201 | −2.1930 | −0.9185 | −2.3659 | −2.2354 | −0.8932 |

LNEXRATE | −0.6811 | −1.4876 | −1.1023 | −0.2559 | −1.0877 | 1.3617 |

LNICPI | −0.3519 | −3.0923 | 1.1065 | −0.3758 | −0.2661 | 1.4213 |

LNMSCI | −1.4200 | −2.1245 | −0.4780 | −1.5679 | −2.3233 | −0.3938 |

LNDY | −2.0444 | −2.5523 | −0.5214 | −2.4346 | −2.7430 | 0.8648 |

LNPER | −1.8346 | −2.9635 | −0.2156 | −1.8475 | −2.5987 | 0.4182 |

At 1^{st} diff. | ||||||

ΔLNSENSEX | −10.2151 | −10.1922 | −10.0453 | −10.1331 | −10.1083 | −10.0077 |

ΔLNIPI | −2.6483 | −2.8019 | −1.7286 | −38.8598 | −37.6794 | −26.6551 |

ΔLNINT | −13.2368 | −13.2893 | −13.2402 | −13.2458 | −13.2868 | −13.2508 |

ΔLNEXRATE | −9.8222 | −9.8488 | −9.7512 | −9.8121 | −9.7945 | −9.7914 |

ΔLNCPI | −8.3789 | −8.7853 | −8.2461 | −10.3723 | −10.2898 | 10.5744 |

ΔLNMSCI | −11.7645 | −11.7619 | −11.7749 | −11.9421 | −11.9310 | −11.9560 |

ΔLNDY | −13.5936 | −13.5633 | −13.6356 | −24.5302 | −24.4684 | −24.6173 |

ΔLNPER | −4.4205 | −4.3887 | −4.4167 | −6.5535 | −−6.5088 | −6.5723 |

MacKinnon critical values at level for model A is −2.9851, model B −3.469, model C −1.9439 and at 1^{st} difference for model A −2.8995, model B −3.4626 and model C −1.9445.

tested unit roots at level and at first difference with all three possible model related with intercept and trend. These three models were intercept with no trend, intercept with trend and no intercept no trend (

The time period selected for the study was 1999 to 2015, so it was important to test the break point test for the period January 2008(start of global financial crisis) and march 2009 (reversal of markets after global financial crisis). The Chow break point test was used the test the break point effect and the results of F statistics and corresponding p-values are given below (

For conducting cointegration analysis consideration the choice of appropriate lag length is a very sensitive issue. There are many criteria for lag length selection. The study used Sequential modified like hood ratio test (LR), Final prediction error (FPE), Akaike information criterion (AIC), Schwarz Bayesian Criterion (SC) and Hannan-Quinn information criterion (HQ), and the outcome is mentioned in

We conducted conintegration analysis and results are given in _{max)} and Trace statistics (λ_{trace}). The long run equation is shown in part C. In maximum eigen value test null hypothesis of r = 1 is rejected against alternative hypothesis of r = 2 in maximum eigen value test and r = 0 against r = 1 in

Chow Breakpoint Test for 2008M01 | |||
---|---|---|---|

F-Statistics | 25.2544 | probability | 0.0000 |

Log likelihood ratio | 23.9418 | probability | 0.0000 |

Chow Breakpoint Test for 2009M03 | |||

F-Statistics | 10.1642 | probability | 0.0017 |

Log likelihood ratio | 10.0034 | probability | 0.0016 |

Lag | Log L | LR | FPE | AIC | SC | HQ |
---|---|---|---|---|---|---|

0 | 2284.521 | NA | 8.60E−23 | −28.1052 | −27.95272* | −28.04329 |

1 | 2429.966 | 274.7292 | 3.15E−23 | −29.1107 | −27.7384 | −28.55353* |

2 | 2501.687 | 128.3906 | 2.88E−23 | −29.206 | −26.614 | −28.1536 |

3 | 2568.865 | 113.6215 | 2.80e−23* | −29.24525* | −25.4334 | −27.69758 |

4 | 2620.982 | 83.0006 | 3.33E−23 | −29.0985 | −24.0669 | −27.05562 |

5 | 2683.357 | 93.17838* | 3.55E−23 | −29.0785 | −22.8271 | −26.54031 |

Hypothesized no of CE(s) | Eigenvalue | Trace Statistic | Critical Value (5%) | Prob.** |
---|---|---|---|---|

None* | 0.325617 | 196.5166 | 159.5297 | 0.0001 |

At most 1* | 0.208028 | 132.3016 | 125.6154 | 0.0183 |

At most 2 | 0.186779 | 94.28521 | 95.75366 | 0.0629 |

At most 3 | 0.141716 | 60.58466 | 69.81889 | 0.2178 |

At most 4 | 0.092595 | 35.67488 | 47.85613 | 0.4129 |

At most 5 | 0.07364 | 19.83671 | 29.79707 | 0.434 |

At most 6 | 0.040772 | 7.368416 | 15.49471 | 0.5352 |

At most 7 | 0.003572 | 0.583252 | 3.841466 | 0.445 |

Hypothesized no of CE(s) | Eigenvalue | Trace Statistic | Critical Value (5%) | Prob.** |
---|---|---|---|---|

None* | 0.325617 | 64.21504 | 52.36261 | 0.0021 |

At most 1 | 0.208028 | 38.01639 | 46.23142 | 0.287 |

At most 2 | 0.186779 | 33.70055 | 40.07757 | 0.2189 |

At most 3 | 0.141716 | 24.90978 | 33.87687 | 0.3911 |

At most 4 | 0.092595 | 15.83817 | 27.58434 | 0.6795 |

At most 5 | 0.07364 | 12.46829 | 21.13162 | 0.5022 |

At most 6 | 0.040772 | 6.785164 | 14.2646 | 0.5149 |

At most 7 | 0.003572 | 0.583252 | 3.841466 | 0.445 |

LNSENSEX = − 2 . 4631LNIPI ( 0. 2696 ) − 0.0 3 0 3LNINT ( 0. 1357 ) − 1 .0 37 0 LNEXRATE ( 0. 3792 ) + 0. 6 0 88LNCPI ( 0. 2897 ) + 0. 349 0 LNMSCI ( 0. 2433 ) − 0. 785 0 LNDY ( 0. 1713 ) − 0. 6971LNPE ( 0. 1581 ) . (6) |
---|

*denotes rejection of the hypothesis at the 0.05 level, **MacKinnon-Haug-Michelis [

trace test. The result depicts that only one stationary linear combination is integrated in long run. In Johansen [

Results of dynamic series are given in ^{’} upon normalization for LNSENSEX. The coefficient for LNSENSEX, LNIPI, LNGSECR, LNEXRATE, LNCPI, LNMSCI, LNDY and LNPE are −1.2446, −0.1337, 0.7494, −0.7112, 0.1248, −0.8281 AND −0.0685 respectively. The corresponding t-statistics are 10.6942, 0.5627, −15.2996, 7.53949, 3.74307, 7.22788 and 1.79791 respectively. The results suggest that except LNGSECR all other variables LNIPI, LNEXRATE, LNCPI, LNMSCI, LNDY AND LNPE are significant in and have long term conintegration with LNSENSEX.

Variables | Coefficient | SE | t-value* |
---|---|---|---|

LNSENSEX | 1.0000 | ||

LNIPI | −1.2446 | −0.1518 | 10.6942 |

LNGSECR | −0.1337 | −0.0792 | 0.5627 |

LNEXRATE | 0.7494 | −0.2268 | −15.2996 |

LNCPI | −0.7112 | −0.1635 | 7.5394 |

LNMSCI | 0.1248 | −0.1315 | 3.7430 |

LNDY | −0.8281 | −0.0858 | 7.2278 |

LNPE | −0.0685 | −0.0889 | 1.7979 |

*Critical values for t-statistics (2 sided test) are 1.96 and 1.58 at 5% and 1% level of significance.

Variables | Coefficient | Standard Error | t statistics | p-value |
---|---|---|---|---|

LHS variable: ΔLNSENSEX | ||||

ΔLNSENSEX (−1) | 0.2366 | 0.140568 | 1.6836 | 0.0944 |

ΔLNIPI(−1) | 0.1173 | 0.113577 | 1.0334 | 0.3031 |

ΔLNGSECR(−1) | 0.1307 | 0.105056 | 1.2444 | 0.2153 |

ΔLNEXRATE(−1) | −0.0282 | 0.35586 | −0.0793 | 0.9369 |

ΔLNCPI(−1) | −0.7898 | 0.414722 | −1.9044 | 0.0588 |

ΔLNMSCI(−1) | 0.5553 | 0.108698 | 5.1094 | 0.0000 |

ΔLNDY(−1) | 0.0429 | 0.043512 | 0.9867 | 0.3254 |

ΔLNPE(−1) | −0.1347 | 0.111891 | −1.2039 | 0.2305 |

ECM(−1) | 0.0787 | 0.061231 | 1.2858 | 0.2005 |

The short run dynamics estimated coefficient of α corresponding to ΔLNSENSEX, ΔLNIPI, ΔLNGSECR, ΔLNEXRATE, ΔLNCPI, ΔLNMSCI, ΔLNDY and ΔLNPE are 0.0787, −0.0979, −0.01053, 0.0590, −0.0559, 0.0098, −0.0652 and −0.0053 respectively. The corresponding t-statistics values are 0.8020, −1.4589, −1.8516, 0.4968, 0.2498, 2.0634, −0.2531, −3.1853 respectively. The details are given in following equation:

α ∧ = [ α 11 , α 21 , α 31 , α 41 , α 51 , α 61 , α 71 , α 81 ] = [ 0.07873 0.8020 , − 0.0979 − 1.4589 , − 0.0105 − 1.8516 , 0.0590 0.4967 , − 0.0559 0.2498 , 0.0098 2.0634 , − 0.0652 − 0.2531 , − 0.0053 − 3.1853 ] (7)

On the basis of all above results of long run dynamic and short run dynamics of Indian stock markets vector error correction model (VECM) for Indian stock market is given below in Equation (8) and the solved equation is given in (9):

Δ ( LNSENSEX ) = 0.0 787 * [ LNSENSEX ( − 1 ) − 1 . 2446 * LNIPI ( − 1 ) − 0. 1337 * LNGSECR ( − 1 ) + 0. 7494 * LNEXRT ( − 1 ) − 0. 7112 * LNCPI ( − 1 ) + 0. 1248 * LNMSCI ( − 1 ) − 0. 8281 * LNDY ( − 1 ) − 0.0 685 * LNPE ( − 1 ) + 0. 2532 ] + 0. 2366 * Δ ( LNSENSEX ( − 1 ) ) + 0. 1173 * Δ ( LNIPI ( − 1 ) ) + 0. 13 0 7 * Δ ( LNGSECR ( − 1 ) ) − 0.0 282 * Δ ( LNEXRT ( − 1 ) ) − 0. 7898 * Δ ( LNCPI ( − 1 ) ) + 0. 5553 * Δ ( LNMSCI ( − 1 ) ) + 0.0 429 * Δ ( LNDY ( − 1 ) ) − 0. 1347 * Δ ( LNPE ( − 1 ) ) + 0.0107 (8)

or

Δ ( LNSENSEX ) = 0.0 787 * LNSENSEX ( − 1 ) − 0.0 979 * LNIPI ( − 1 ) − 0.0105 * LNGSECR ( − 1 ) + 0. 5897 * LNEXRT ( − 1 ) − 0.0 559 * LNCPI ( − 1 ) + 0.0098 * LNMSCI ( − 1 ) − 0.0 651 * LNDY ( − 1 ) − 0.00 54 * LNPE ( − 1 ) + 0.0 199 + 0. 2366 * Δ ( LNSENSEX ( − 1 ) ) + 0. 1173 * Δ ( LNIPI ( − 1 ) ) + 0. 13 0 7 * Δ ( LNGSECR ( − 1 ) ) − 0.0 282 * Δ ( LNEXRT ( − 1 ) ) − 0. 7898 * Δ ( LNCPI ( − 1 ) ) + 0. 5553 * Δ ( LNMSCI ( − 1 ) ) + 0.0 429 * Δ ( LNDY ( − 1 ) ) − 0. 1347 * Δ ( LNPE ( − 1 ) ) + 0.0107 (9)

The above results can help in understanding return generating process in Indian stock markets. The long run conintegration analysis results suggests that industrial production, foreign exchange rate, inflation, dividend yield, price earnings ratio and global financial markets have significant impact on Indian stock markets.

The objective of this study was to identify risk factors for Indian stock markets. The results of the study are encouraging and highly useful for investors in pricing the market. These results are in line with existing finance literature with different on some points. The study identified seven relevant variables representing systematic risk factor for Indian stock markets, namely industrial production index (IPI), interest rate (INT) on ten years government bonds, foreign exchange rate of Indian rupee with US dollar (EXRATE), CPI as proxy of Inflation, MSCI global index (MSCI), dividend yield (DY) and price earnings ratio of Indian stock market (PER) for Indian stock market. Some of these variables have short term while some others have long term impact on return generation process of Indian stock markets. The results of short run analysis suggests that Indian stock prices are adjusted monthly by its previous month levels, previous month’s global stock markets and consumer price index (inflation). Accordingly, the study concluded that there are three major factors and their lagged values influencing short term return generating process namely previous month’s levels of markets index itself (SENSEX), global markets (MSCI global) and inflation (CPI). However, in long run there are six major factors influencing return generating process namely industrial production index, foreign exchange rate, inflation, dividend yield, price rearing ratio and global financial markets. Hence, while making short term and long term investment decision predictability of these risk factors will help investors in understanding return generation potential of Indian stock markets.

The study significantly contributes in existing literature on risk factors for stock market of emerging economy like India. The findings of the study are significant for investors, fund managers and analysts for pricing risk in their investment decisions and analysing systematic risk factors. Findings of this study cannot be the only criterion for any investment decision so investors must consider several others quantitative and qualitative factors that may affect return generating process of Indian stock markets.

Srivastava, A., Gupta, P. and Gupta, R. (2017) Strategic Risk Factors for Indian Stock Markets. Theoretical Economics Letters, 7, 1687-1701. https://doi.org/10.4236/tel.2017.76114