1. Faculty of Management, Shanker Dev Campus, Tribhuvan University, Kathmandu, Nepal.

* Correspondence: munaranapal25@gmail.com

Keywords: MPS, DPS, BVPS, EPS, P/E Ratio

1. INTRODUCTION

Investment in equity share is one of the liquid forms of investment. Market price of the share is one the most important factor that affects investment decision of investors. It is also suggested from the theories that market price of the share depends upon many factors, such as earning per share, dividend per share, Book value per share etc.[1]. Stock prices are determined by the supply of and demand. Stock prices adjust to keep supply and demand in balance (or equilibrium). Determining share prices is a complex and conflicting task. Stock market price determinants depended on many basic internal factors; the results indicated that the internal factors explained the extent of the influence of these factors on stock market prices, and the relatively strong relation between stock market prices and the independent internal factors [2]. Earnings per share is the profit that the company made per share on the last quarter. If a company releases a healthy-looking earnings report, then investors will likely feel more safe and optimistic about its potential return on their investment in this company. Demand for the stock will climb, and so will its price. On the other hand, if a company reports negative earnings or is the subject of bad results and image, its stock price can quickly fall. The changes in EPS have the major influence on share price over the long run reason being share price of company generally increases when earnings of the company grow and decrease when company earnings decline, but in short run the relationship between EPS and MPS may inverse [3]. Dividend is the portion of the net earnings of the firm, which is distributed to the shareholders either in the form of cash or stock, as per its dividend policy, Dividend payments are usually part of the company`s strategic policy to rerun a portion of its profits to its shareholders. The dividend ratio provides information about the financial strength and maturity of the company along with reflections about its investor`s expectations. The dividend can be distributed either in cash or by capitalization of profit as bonus shares. Dividend paid to the shareholders is one of the best indicators of profitability; it is generally believed that dividend plays a crucial role in determining market price. The relationship between dividend and share price in not yet clear in the literature of finance and it is still a controversial issue in underdeveloped countries like Nepal [4]. Similarly, [5] implies that if the company declares a dividend payment that is higher or lower than expected, market sentiment may shift causing the stock price to rise or drop accordingly. Book value is one of the important variables, which affect the market value of equity share. It is the value of own funds of a company per share. The book value of a company tells the worth of each share in a company. The book value is a reflection of the past earnings, dividend distribution policy of the company and investment decisions. A high book value indicates that a company has huge reserves and is a potential bonus candidate. A low book value signifies a liberal distribution policy of bonus and dividends, or a poor record of accomplishment of profitability. Reinvesting retained earnings in profitable business opportunities of the company results in growth of the company. This is turn results in enhancement of market price of share. However, book value is depleted because of investment in new business opportunity. In addition, re-investing retained earnings in unprofitable business will adversely affect both market price of share and book value [6]. Thus, both dividend decision and investment decisions affect book value. In addition, both dividend decision and investment decisions affect market value. Market price of the share is one the most important factor that affects investment decision of investors. Market price of the share depends upon many factors. Therefore, the task of predicting share prices is far from simple. Share price movement is not independent in nature and both intrinsic as well as extrinsic factors have been established to exercise influence over stock price movements [7]. Stock Market is a market for long-term capital where both new capitals can be raised by companies and where existing shares can also be bought and sold. By providing a second hand market for investors to sell their shares, it facilitates the raising of new capital on the new issues market. The stock exchange also provides a market for government loans and securities and increasingly involved in the buying and selling of securities in the overseas companies. On the market, the main operators are the market makers who trade in groups of share, and the stockbrokers who act as agents for their clients, who are the investors who are actually buying and selling shares. New York stock Exchange, London stock Exchange, Tokyo Stock Exchange, Paris Stock Exchange, Frank fruit Exchange, Toronto Stock Exchange are the biggest stock Exchange of the world. Nepal Stock Exchange is only Organized Stock Exchange in Nepal [8]. According to Nepal Rastra Bank Financial report 2022, 227 public companies are listed in Nepal Stock Exchange Ltd. (NEPSE). All companies have listed in Nepal Stock Exchange (NEPSE) which is the main secondary or capital market in Nepal which is regulated by Securities Exchange Board in Nepal (SEBON) because SEBON is the regulatory body of secondary market. All listed companies have their stock or share prices determined on the basis daily transaction of their stock in NEPSE i.e. the price is determined based on demand and supply of shares of the organizations. For this stock market fair many investors, securities brokers and other concerned parties involve in daily trading to gain their own return. In this study it is mainly focused on the price determinants of stock of listed companies in Nepal for this it would have to be examined the secondary stock market of Nepal and it also be identified how the price of banks’ stock fluctuate in daily or what is the reason behind price shift of stock. This study is attempted to identify the quantitative factors that influence share prices for the listed companies in NEPSE using empirical analysis of a set of independent and dependent variables. Several prior empirical studies from developed economies have shed light on the effect of financial performance, dividend payout and financial advantage on the share price of firms.  The same is not true in developing economies like Nepal. Similarly, findings from prior studies in developing countries indicate phenomenon has a disagreement. So, all of these facts create the need for further studies. Therefore, this study is an attempt to examine the relationship between EPS, DPS, P/E Ratio and BVPS with MPS for listed companies in NEPSE with entitled “Stock Price Determinants in NEPSE”.

2. MATERIAL AND METHODS

To achieve the objective of this study, descriptive and analytical research design has been used. Some financial, Statistical tools have been applied to examine facts, and descriptive techniques have been adopted to determine the relationship between EPS, DPS, BVPS and P/E Ratio with stock price of listed companies in the NEPSE.As per the data of December 2022(NEPSE), 227 public companies are listed in Nepal Stock Exchange Ltd. consisting 20 from finance companies, 19 from manufacturing and Processing, 26 from commercial banking sectors, 47 from hydropower, 4 from trading, 30 from insurance companies, 16 from Development Banks, 51 form microfinance, 5 from hotels, 26 from Mutual fund, 1 from preferred stock and 2 from others sectors. This study is based on secondary data. Secondary data are collected from their respective annual reports other publication and journals of the selected companies. For the research 6 sample companies of different categories are selected. 2 commercial banks are chosen as the representative of the banks and financial institution. Similarly 2 insurance companies are taken as the representative of the insurance companies. 2 companies from hydropower are selected as a sample from the hydropower sector. Thus, the populations in this study are all the companies listed in NEPSE.  Six companies are selected for sample as per convenience sampling. Thus sample companies are Nabil Bank Ltd(NABIL), Sanima Bank Ltd (SANIMA), Surya life Insurance Company (SLICL), Gurans life Insurance Company(GLICL), Butwal Power Company Ltd (BPCL) & Chilime Hydro Company Ltd (CHCL). The study is mainly based on secondary data. Some sources of data are annual report of respective company, review of previous thesis, journal and article, Nepal Security Exchange, Handbooks and published books of accounts, periodical report published in newspapers, websites of respective companies and regulating bodies and article, books etc. Firstly, data are acquired from the annual reports of respective companies and put them in a sheet. Then data are entered into the spreadsheet to work out the financial ratios and prepare necessary figures, according to the need and requirement of the study. For this purpose, collected data have been processed using computer programs like MS Excel as per the necessity. Several tools and techniques and used to analyze Secondary data collected from various sources for obtaining the logical conclusion. The following financial as well as statistical tools have been used to analyze the data: Financial analysis is the process of identifying the financial strengths and weaknesses of the organization by properly establishing relationships between the items of the balance sheet and the profit and loss account.  Earnings per share or EPS is an important financial measure, which indicates the profitability of a company. It is calculated by dividing the company’s net income with its total number of outstanding shares. It is a tool that market participants use frequently to gauge the profitability of a company before buying its shares. EPS is the portion of a company’s profit that is allocated to every individual share of the stock. It is a term that is of much importance to investors and people who trade in the stock market. The higher the earnings per share of a company, the better is its profitability

EPS is calculated to know the earning capacity and to make comparison between concerned companies.

Dividend per share is a measure of the dividend payout per share of a company’s common stock. The measure is used to estimate the amount of dividends that an income investor might expect to receive if he or she were to buy a company’s common stock.

DPS indicates the part of earning distributed to the shareholders on per share basis and calculated by dividing the total dividend to equity shareholders by the total no. of equity shares.

The dividend payout ratio shows how much of a company’s earnings after tax. Dividend payments signal that a business is earning enough to share a portion of its gains with its owners, encouraging shareholder confidence in the management team.  DPR is calculated to indicate percentage of the profit on share that is distributed as dividend.

The price to earnings ratio (PE Ratio) is the measure of the share price relative to the annual net income earned by the firm per share. PE ratio shows current investor demand for a company share. A high PE ratio generally indicates increased demand because investors anticipate earnings growth in the future. The PE ratio has units of years, which can be interpreted as the number of years of earnings to pay back purchase price.

PE Ratio reflects the price currently paid by the market for each rupee of currently reported earnings per share. It is calculated dividing the market value per share by earning per share.

Earning Yield and Dividend Yield both are expressed in terms of the market value per share. Earning Yield and Dividend yield are two important profitability ratios from the point of view of the ordinary shareholders. The earning yield may define as the ratio of earning per share to the market value per ordinary share and earning yield is calculated as; 

Dividend yield is the financial ratio that measures the quantum of cash dividends paid out to shareholders relative to the market value per share. The dividend yield reflects percentage relationship between dividend per share and market value per share. It is calculated through dividing the dividend per share by the market value per share.

The market value represents the value of a company according to the stock market. While market value is a generic term that represents the price an asset would get in the marketplace, it represents the market capitalization in the context of companies. It is the aggregate market value of a company represented as rupee amount. Since it represents the “market” value of a company, it is computed based on the current market price (CMP) of its shares. This price varies throughout the day, based on the level of demand for the stock. The price will rise when more investors want to buy it than are willing to sell, while the price will decline in the reverse situation. Value investors closely follow this figure to determine when it makes sense to acquire shares at a sufficiently low price. An issuing company’s treasurer also tracks the market price to determine when the price is high enough to justify a new stock issuance that maximizes the amount of cash raised by the entity in proportion to the number of shares sold.

It is calculated as;

Book value per share (BVPS) is the ratio of equity available to common shareholders divided by the number of outstanding shares. This figure represents the minimum value of a company’s equity and measures the book value of a firm on a per-share basis. Book value per share compares the amount of stockholders’ equity to the number of shares outstanding. The book value per share (BVPS) is calculated by taking the ratio of equity available to common stockholders against the number of shares outstanding. When compared to the current market value per share, the book value per share can provide information on how a company’s stock is valued. If the value of BVPS exceeds the market value per share, the company’s stock is deemed undervalued. The book value is used as an indicator of the value of a company’s stock, and it can be used to predict the possible market price of a share at a given time in the future.

It is calculated as;

Statistical tools measure the data and give the result in numeric form, which helps to analyze the data in logical way. The following statistical tools have been used in this study. Average, in general, is calculated by adding all the numbers of all observations and dividing by the total number of observations. It is in fact, a value, which is represented to stand for whole group of which it is a part, as typical of all the values in the group.

The standard deviation (σ) is the other measure of investment risk. It is absolute measures of dispersion. The smaller the standard deviation the lower will be the degree of risk of the stock. In other words, a small standard deviation means a high degree of uniformity of the observations as well as homogeneity of a series and vice versa. The formula for calculating the standard deviation is:

The coefficient variation (CV) is the other useful measure of risk. It is the standard deviation divided by the expected return, which measures risk per unit of return. It provides a more meaningful basis for comparison when the expected returns on two alternatives are not the same. If investors believe that the rate of return should increase as the risk increase, then the coefficient of variation provides a quick summary of the relative trade-off between expected return and risk. It is hence used to compare the variability between two or more series.

“Karl Pearson’s Coefficient of Correlation is a statistical tool for measuring the intensity or magnitude of linear relationship between the two variables series. Karl Pearson’s measure, known as Pearson Correlation Coefficient between two variables (Series) X and Y, usually denoted by ‘r (X, Y)’ or ‘rxy’ or simply ‘r’ can be obtained as;

Where, n	:	Number of observations in series X and Y
∑X	:	Sum of observations in series X
∑Y	:	Sum of observations in series Y
∑X2	:	Sum of squared observations in series X
∑Y2	:	Sum of squared observations in series Y
∑XY	:	Sum of product of observations in series X and Y

The value of correlation coefficient ‘r’ lies between -1 to 1, i.e. -1r <1.

If r = 1, there is perfect positive relationship. If r = -1, there is perfect negative relationship. If r = 0, there is no correlation at all.

The closer the value of ‘r’ is 1 or -1, the closer the relationship between the variables and the closer ‘r’ is to 0, the less close relationship. The coefficient of determination between the two variable series is a measure of linear relationship between them and indicates the amount of one variable which is associated with or accounted for another variable. It gives the percentage variation in the dependent variable that is accounted for by the independent variable. Moreover, it gives the ratio of the explained variance to the total variance and it is given by square of the correlation coefficient, i.e.” r². Thus,

Regression is the estimation of unknown values or prediction of one variable from known values of other variables. It is a mathematical measure of the average relationship between two or more variables in terms of the original units of the data. The known value which is used for prediction (or estimation) is called independent (predictor or explanatory) variables and the unknown value that we are going to predict is called dependent (or regressed, predicted or explained) variable. The line of regression of X on Y is the line which gives the best estimates of X for any given amount of Y. The regression equation is expressed as:

Y= B₀ +B₁X₁+B₂X₂+B₃X₃+B₄X₄

Where, Y = the value of dependent variable (MPS)

B = Y-intercept and it gives the value of MPS when all other independent variables      are zero

X = Value of independent variable (EPS, DPS, BVPS, P/E Ratio)

X₁= Earnings per share (EPS)

B₁=Coefficient of EPS

X₂=Dividend per share (DPS)

B₂=Coefficient of DPS

X₃=Book value per share (BVPS)

B₃=Coefficient of BVPS

X₄=P/E ratio

B₄=Coefficient of P/E ratio

In this study, independent variables are DPS, BVPS, P/E ratio and EPS. Similarly, dependent variable is MPS. In the study, MPS is only one dependent variable, which has significant relationship with DPS, BVPS, P/E Ratio and EPS. MPS is assumed to be increased as the value of independent variables (DPS, BVPS, P/E Ratio and EPS) of listed companies. Here, DPS is independent variable which has impact on market price of listed companies. MPS of sample companies is assumed largely to be dependent on dividend distributed by respective companies. The market price of stocks depends on how much dividend a company’s distribute dividend to its shareholders. Here, BPS is independent variable, which affects the market price. It means MPS depends on the book value of stock of listed companies. The market price of stock is assumed to increase as the book value of share increases and vice versa. In this study, EPS is an independent variable, which has effect on the stock price of Nepalese listed companies. It means MPS depends on the earning of common stock of listed companies. The market price of stock is expected to increase as the earning per share increases and vice versa.

3. RESULTS AND DISCUSSION

Table 01: Market price per share

Fiscal year	NABIL	SANIMA	GLICL	SLICL	BPCL	CHCL
2020/21	1359	485	759	922	506	687
2019/20	765	330	465	448	359	398
2018/19	800	348	465	456	409	521
2017/18	921	324	855	600	457	790
2016/17	1523	431	1020	1070	620	798
2015/16	2344	750	920	940	894	1440
2014/15	1910	555	582	709	617	1683
2013/14	2535	638	650	750	859	2736
2012/13	1815	260	152	166	830	1191
2011/12	1355	225	106	140	577	910
Mean	1532.7	434.6	600.4	620.1	612.8	1115.4
SD	618.88	170.94	311.49	318.73	191.44	696.01
CV	40.38%	39.33%	51.88%	51.40%	31.24%	62.40%

Table 02: Earning price per share

Fiscal year	NABIL	SANIMA	GLICL	SLICL	BPCL	CHCL
2020/21	33.40	23.51	6.14	14.29	17.1	11.17
2019/20	37.48	19.35	10.12	12.19	26.94	13.10
2018/19	50.57	28.04	8.22	16.46	31.89	15.04
2017/18	49.51	23.33	9.63	11.08	32.11	23.68
2016/17	58.41	18.85	5.23	14.78	35.86	28.03
2015/16	59.27	32.55	4.47	5.68	34.39	31.57
2014/15	57.24	24.47	3.44	3.41	31.21	29.67
2013/14	83.68	19.29	4.19	8.21	24.69	28.03
2012/13	95.14	15.13	5.65	8.79	28.21	23.68
2011/12	83.57	6.04	8.17	7.44	55.27	15.04
Mean	60.83	15.12	5.10	7.78	31.77	21.92
SD	20.46	7.27	2.36	4.2389	9.87	7.64
CV	33.64%	34.51%	36.09%	41.42%	31.085	34.86%

Comparing the Mean, Standard Deviation and CV of the sample companies. Highest average value is 60.83 of NABIL and lowest is 5.10 of GLICL, which indicates that NABIL is better performing company among others. NABIL has the highest value of SD i.e. 20.46 and GLICL has the lowest SD of 2.36 between the six companies. Lesser the SD value, lesser the risk in investment, GLICL is less risky company. Similarly, SLICL has maximum value of CV i.e. 41.42% and NABIL has lowest CV of 33.64%. The less figure of CV means the more consistent and higher the CV means less consistency. Here, NABIL is more consistence and SLICL is less Consistent.

Table 03: Dividend per share

Fiscal year	NABIL	SANIMA	GLICL	SLICL	BPCL	CHCL
2020/21	38	17.89	0	13.37	20	15
2019/20	35.26	13.6	0	10.53	25	20
2018/19	34	21.05	5.79	0	28	25
2017/18	34	14	11.05	12	28	25
2016/17	48	16	8.42	12.63	20	25
2015/16	45	15.79	8.42	10	27	20
2014/15	36.84	21.05	10.52	5	20	27
2013/14	65	15.79	0	0	15	35
2012/13	65	10.53	0	0	18	40
2011/12	65	5.5	6.84	14.25	25	50
Mean	46.61	21.06	6.53	10.23	22.6	28.2
SD	13.48	4.68	4.65	5.93	4.58	10.55
CV	28.92%	30.93%	91.05%	76.21%	20.24%	37.41%

Comparing the Mean, Standard Deviation and CV of the sample companies. Highest average value is 46.61 of NABIL and lowest is 6.53 of GLICL, which indicates that NABIL is better performing company among others. NABIL has the highest value of SD i.e. 13.48 and BPCL has the lowest SD of 4.58 between the six companies. Lesser the SD value, lesser the risk in investment, BPCL is less risky company. Similarly, GLICL has maximum value of CV i.e. 91.50% and BPCL has lowest CV of 20.24%. The less figure of CV means the more consistent and higher the CV means less consistency. Here, BPCL is more consistence and JLICL is less consistence.  

Table 04:P/E ratio

Fiscal year	NABIL	SANIMA	GLICL	SLICL	BPCL	CHCL
2020/21	40.68	20.62	123.61	64.52	29.59	61.5
2019/20	20.41	17.05	45.94	36.75	13.32	30.38
2018/19	15.81	12.41	56.56	27.7	12.82	34.64
2017/18	18.6	13.88	88.78	54.15	14.23	33.36
2016/17	26.07	22.86	195.02	72.39	17.28	28.46
2015/16	39.54	23.04	205.81	165.49	25.99	45.61
2014/15	33.36	22.68	169.18	207.91	19.76	56.72
2013/14	30.29	33.07	155.13	91.35	34.79	97.6
2012/13	19.07	17.18	26.9	18.88	29.42	50.29
2011/12	16.21	37.25	12.97	18.81	10.43	60.5
Mean	26.0097	22.0079	107.995	75.7987	20.7682	49.9105
SD	2.8378	2.3766	21.4062	19.1504	2.5645	6.273
CV	10.91%	10.79%	19.82%	25.26%	12.34%	12.568%

Comparing the Mean, Standard Deviation and CV of the sample companies. Highest average value is 107.95 of GLICL and lowest is 20.7682 of BPCL, which indicates that GLICL is better performing company among others. GLICL has the highest value of SD i.e. 21.4062 and SANIMA has the lowest SD of 2.3766 between the six companies. Lesser the SD value, lesser the risk in investment, SANIMA is less risky company. Similarly, SLICL has maximum value of CV i.e. 25.26% and SANIMA has lowest CV of 10.79%. The less figure of CV means the more consistent and higher the CV means less consistency. Here, SANIMA is more consistent and SLICL is less consistent.

Table 05: Book value per share

Fiscal year	NABIL	SANIMA	GLICL	SLICL	BPCL	CHCL
2020/21	240.73	153.6	130.17	132.39	237.15	161.4
2019/20	258.24	144.83	144.95	128.07	268.96	175
2018/19	261.08	148.73	133.92	138.49	283.55	195.81
2017/18	257	134.31	141.41	129.77	304.34	225.67
2016/17	256	131.32	132.51	154.69	227.33	243.14
2015/16	270	146.27	137.16	131.98	229.85	266.36
2014/15	244	134.79	133.09	127.97	220.18	234.36
2013/14	259	129.76	125.16	123.69	214.59	243.14
2012/13	315	120.76	119.19	111.08	290.1	225.67
2011/12	310.09	111.1	117.61	116.89	298.86	195.81
Mean	267.12	135.55	131.52	130.60	257.49	216.64
SD	25.35	13.21	8.86	14.65	35.12	33.34
CV	09.49%	9.75%	6.74%	11.21%	13.64%	15.39%

Comparing the Mean, Standard Deviation and CV of the sample companies. Highest average value is 267.12 of NABIL and lowest is 216.64 of CHCL, which indicates that NABIL is better performing company among others. BPCL has the highest value of SD i.e. 35.12 and GLICL has the lowest SD of 8.86 between the six companies. Lesser the SD value, lesser the risk in investment, SANIMA is less risky company. Similarly, CHCL has maximum value of CV i.e. 15.39% and GLICL has lowest CV of 6.74%. The less figure of CV means the more consistent and higher the CV means less consistency. The JLICL is more consistent and CHCL is less consistent.  Correlation measures of the strength of linear association between two variables. Correlation will be always between -1.0 and +1.0. If the correlation is positive, we have a positive relationship. If it is negative, the relationship is negative. The Pearson co-efficient of correlation is used to assess the relationship between market price of share and dividend per share, earnings per share, Book Value per share at 1% and 5% level of significance. In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables) where case of one explanatory variable is called simple linear regression.  The following table shows the correlation coefficient between market price of share and dividend per share, earnings per share, Book Value per share denoted by r.”R²” indicates the coefficient of determination, t-Cal, and t tab refers to calculated value of t- statistic and tabulated value of t-statistic at 5% level of significance at 8 degree of freedom two-tailed test for respectively. The following results are worth highlighting.

Table 06: Correlation Coefficient of NABIL

	MPS	EPS	DPS	BVPS	P/E Ratio
MPS	1
EPS	0.553471	1
DPS	0.566179	0.917114	1
BVPS	0.095456	0.781154	0.734364	1
P/E Ratio	0.599428	-0.28355	-0.14234	0.5192	1

Table 07: Multiple Regression

Modal	R	R square	Adjusted R square	Std. Error of the Estimate
	0.98	0.9604	0.9288	165.046

1. Dependent Variables: MPS
2. Independent Variables: EPS, DPS and BVPS

Table shows R² = 0.9288, this mean that the 92.88 % variation in MPS is explained by EPS, DPS,

P/E ratio and BVPS and remaining 7.12% by other factor which we have not included in the model.

Table 08: ANOVA

	df	SS	MS	F	Significance F
Regression	4	3310902	827725.5	30.3882	0.001055
Residual	5	136191.9	27238.39
Total	9	3447094

1. Dependent Variable: MPS
2. Independent Variable: EPS, DPS and BVPS

The ANOVA table shows significant f value of 0.0010, which is less than α i.e (0.05) .It shows that the overall linear regression model of MPS on EPS,DPS, P/E ratio, BVPS is significant .

Table 09: Coefficient

	Coefficients	Standard Error	t-stat	t-value	Result
Intercept	359.9479	1015.5184	0.3544475	0.737462	Insignificant
EPS	34.18513	7.4981963	4.5591140	0.006062	Significant
DPS	-7.120509	11.18497	-0.636614	0.552376	Insignificant
BVPS	-6.931726	4.1637784	-1.6647681	0.156841	Insignificant
P/E Ratio	49.10095	7.6180681	6.4453277	0.001337	Significant

Table 10: Correlation Coefficient of SANIMA

	MPS	EPS	DPS	BVPS	P/E Ratio
MPS	1
EPS	0.65413198	1
DPS	0.52606441	0.80015363	1
BVPS	0.43146263	0.82488105	0.74384406	1
P/E Ratio	0.20349448	-0.5558021	-0.4670495	-0.5794773	1

Table 11: Multiple Regression   

Modal	R	R square	Adjusted R square	Std. Error of the Estimate
	0.946	0.895	0.8111	74.2937

1. Dependent variables: MPS
2. Independent Variables: EPS, DPS, P/E ratio and BVPS

Table shows R² = 0.8950, this mean that the 89.50 % variation in MPS is explained by EPS, DPS,

P/E ratio and BVPS and remaining 10.5% by other factor which we have not included in the model.

Table 12: Anova

	df		SS	MS	F	Significance F
Regression		4	235390.58	58847.6449	10.661647	0.01154983
Residual		5	27597.820	5519.564
Total		9	262988.4

1. Dependent Variable: MPS
2. Independent Variable: EPS, DPS and BVPS

The ANOVA table 4.12 shows significant f value of 0.0010, which is less than α i.e (0.05) .It shows that the overall linear regression model of MPS on EPS, DPS, P/E ratio and BVPS is significant.

Table 13: Coefficient

	Coefficients	Standard Error	t Stat	P-value	Result
Intercept	446.322	423.1359626	-1.05479465	0.3398	Insignificant
EPS	25.6677	7.01988219	3.656422139	0.01464	Significant
DPS	2.52655	9.112930473	0.277248472	0.79268	Insignificant
BVPS	0.62235	3.538923472	-0.175857899	0.86731	Insignificant
P/E Ratio	17.570	3.892005881	4.514488579	0.00631	Significant

Table 14: Correlation Coefficient of GLICL

	MPS	EPS	DPS	BVPS	P/E Ratio
MPS	1
EPS	0.255420464	1
DPS	0.375200302	-0.0541684	1
BVPS	0.587993248	0.36523265	0.2711948	1
P/E Ratio	0.835997255	0.70994872	0.3485421	0.27897766	1

Table 15: Multiple Regression

Modal	R	R square	Adjusted R square	Std. Error of the Estimate
	0.967403	0.935869	0.88456	104.6014

1. Dependent variables: MPS
2. Independent Variables: EPS, DPS, P/E ratio and BVPS

Table shows R² = 0.88450, this mean that the 88.45 % variation in MPS is explained by EPS, DPS, P/E ratio and BVPS and remaining 11.55% by other factor which we have not included in the model.

Table 16: ANOVA

	df	SS	MS	F	Significance F
Regression	4	798345.2	199586.3	18.2413	0.003478
Residual	4	54707.22	10941.44
Total	9	853052.4

1. Dependent Variable: MPS
2. Independent Variable: EPS, DPS and BVPS

Table 17: Coefficient

	Coefficients	Standard Error	t Stat	P-value	Result
Intercept	-264.936	689.6262	-0.38417	0.716645	Insignificant
EPS	109.4156	39.1551	2.794414	0.038245	Significant
DPS	-4.38242	8.410269	-0.52108	0.624558	Insignificant
BVPS	-3.9641	7.436695	-0.53305	0.616833	Insignificant
P/E Ratio	6.4081	1.292066	4.959537	0.00425	Significant

Table 18: Correlation Coefficient of SLICL

	MPS	EPS	DPS	BVPS0	P/E Ratio
MPS	1
EPS	0.128716	1
DPS	0.262715	0.10169	1
BVPS0	0.743281	0.557685	0.288024	1
P/E Ratio	0.577638	-0.63871	-0.03499	0.175521	1

Table 19: Multiple Regression

Modal	R	R square	Adjusted R square	Std. Error of the Estimate
	0.920374	0.847089	0.694177	176.2985

1. Dependent variables: MPS
2. Independent Variables: EPS, DPS, P/E ratio &BVPS

Table shows R² = 0.9203, this mean that the 92.03 % variation in MPS is explained by EPS, DPS, P/E ratio and BVPS and remaining 7.97 % by other factor which are not included in this model.

Table 20: ANOVA

	df	SS	MS	F	Significance F
Regression	4	688725.6	172181.4	5.539734	0.062995
Residual	4	124324.7	31081.17
Total	8	813050.2

1. Dependent Variable: MPS
2. Independent Variable: EPS, DPS and BVPS

The ANOVA table 4.20 shows significant p- value of 0.062995, which is more than α i.e. (0.05) .It shows that the overall linear regression model of MPS on EPS, DPS, P/E ratio, BVPS is insignificant

Table 21: Coefficient

	Coefficients	Standard Error	t-Stat	P-value	Result
Intercept	-959.092	1093.908	-0.87676	0.42074	Insignificant
EPS	44.243	44.17974	1.001446	0.36258	Insignificant
DPS	9.5203	11.90605	0.799619	0.46022	Insignificant
BVPS0	5.4255	12.8618	0.421837	0.69067	Insignificant
P/E Ratio	4.6147	2.476995	1.863043	0.12150	Insignificant

Table 22: Correlation Coefficient of BPCL

	MPS	EPS	DPS	BVPS	P/E Ratio
MPS	1
EPS	0.007135	1
DPS	-0.51018	0.388485	1
BVPS	-0.43721	0.399527	0.555451	1
P/E Ratio	0.728135	-0.61803	-0.72579	-0.56498	1

Table 23: Multiple Regression

Modal	R	R square	Adjusted R square	Std. Error of the Estimate
	0.942218	0.88775	0.797995	86.04273

1. Dependent variables: MPS
2. Independent Variables: EPS, DPS, P/E ratio &BVPS

Table shows R² = 0.7979, this mean that the 79.79 % variation in MPS is explained by EPS, DPS, P/E ratio and BVPS and remaining 20.21 % by another factor which are not included in this model.

Table 24: ANOVA

	df	SS	MS	F	Significance F
Regression	4	292826.8	73206.71	9.888321	0.013583
Residual	5	37016.76	7403.351
Total	9	329843.6

1. Dependent Variable: MPS
2. Independent Variable: EPS, DPS and BVPS

The ANOVA table 4.24 shows significant p- value of 0.013583, which is less than α i.e (0.05) .It shows that the overall linear regression model of MPS on EPS, DPS, P/E ratio, BVPS is significant

Table 25: Coefficient

	Coefficients	Standard Error	t-stat	P-value	Results
Intercept	-425.118	415.7521	-1.02253	0.353431	Insignificant
EPS	14.88235	3.740374	3.978841	0.010543	Significant
DPS	7.398891	9.515409	0.777569	0.471992	Insignificant
BVPS	-0.72222	1.030241	-0.70102	0.514565	Insignificant
P/E Ratio	28.12216	5.856625	4.801769	0.004876	Significant

Table 26: Correlation Coefficient of CHCL

	MPS	EPS	DPS	BVPS	P/E Ratio
MPS	1
EPS	0.660034	1
DPS	0.296421	0.048343	1
BVPS	0.616031	0.971479	0.13496	1
P/E Ratio	0.824032	0.155604	0.385038	0.115816	1

Table 27: Multiple Regression

Modal	R	R square	Adjusted R square	Std. Error of the Estimate
	0.984524	0.969287	0.944716	163.6501

1. Dependent variables: MPS
2. Independent Variables: EPS, DPS, P/E ratio &BVPS

Table shown R² =0.9447, this mean that the 94.47  % variation in MPS is explained by EPS, DPS, P/E ratio and BVPS and remaining 5.53 % by other factor which are not included  in this  model .

Table 28: ANOVA

	df		SS		MS	F	Significance F
Regression		4	4226006	1056501		39.44912	0.000566
Residual		5	133906.8		26781.37
Total		9	4359912

1. Dependent Variable: MPS
2. Independent Variable: EPS, DPS and BVPS

Table 29: Coefficient

	Coefficients	Standard Error	t-Stat	P-value	Result
Intercept	-1338.05	964.9453	-1.38666	0.224188	Insignificant
EPS	44.16549	34.64067	1.274961	0.258354	Insignificant
DPS	-1.62296	6.348252	-0.25566	0.808401	Insignificant
BVPS	1.312214	7.906726	0.165962	0.874689	Insignificant
P/E Ratio	25.001	3.04175	8.219149	0.000434	Significant

Discussion

Market value per share is one of the metrics investors use when selecting stocks. The big question about any stock is whether the shares are worth buying. The different market value ratios use different formulas to determine that. Market value per share is simply the current market price the stock sells for. During the study period of ten years we compared the six different categories of company and their fluctuation on Market price per share (MPS). The results were the market price of the share is highly dependent on the other factors like EPS, DPS, P/E ratio, BVPS alongside with market rumors and the governing rules and regulation of the secondary market (NEPSE). In the year 2013/14 investors were ready to invest Rs.2736 for the share of CHCL also the SD of CHCL is 0.6240, highest among the other five companies. The figure indicates that having the higher amount of MPS does not make company financially established and strong rather it’s the value that are willing to pay for. MPS contributes the future forecast of the share price in the secondary market. Earning price per share is the figure describing a public company’s profit per outstanding share of stock, calculated on a quarterly or annual basis. The companies for the research do not belong to the same category therefore comparing the better among them is not appropriate. However, the company having the highest figure of EPS during the study period was NABIL bank. In the year 2012/2013 the bank had the EPS of Rs.95.14. The EPS alone is not the factor to fluctuate the share price in the market, alongside with the other factor contributing in the increase and decrease in share. Between the two banks the NABAIL is considered better than SANIMA because the EPS of NABIL are higher than of SANIMA. The highest EPS of the SANIMA was 28.04 which is comparatively less than the lowest EPS of NABIL, 33.40. That makes the NABIL bank more secured and preferable to invest. Between SLICL and GLICL, SLICL is more preferable and between BPCL and CHCL, CHCL is secured to invest.  EPS indicates the company’s profitability by showing how much money a business makes for each share of its stock. The EPS figure is determined by dividing the company’s net profit by its outstanding shares of common stock. However, it is considered the higher the EPS number, the more profitable the company. A high Dividend per share (DPS) tells that a company is in good position .is earning good profit and has enough surplus cash so it can reward its shareholders. In other way, it also tells the company value its share. The regular amount if dividend either cash or bonus in an increasing form make direct impact on the MPS of the share. In the research the company having the regular and higher DPS is NABIL bank. The bank paid the DPS of Rs.65 as the highest among the sample companies. A consistent increase in DPS over time can also give investors’ confidence that the company’s management believes that its earnings growth can be sustained. Which increase the demand of the share in secondary market and make difference in MPS over the period of transaction.  P/E ratio shows current investor demand for a company share. A high P/E ratio generally indicates increased demand because investors anticipate earnings growth in the future. P/E ratio is calculated by dividing the price of a stock by its earnings, the earnings yield is calculated by dividing the earnings of a stock by a stock’s current price. It expresses earnings as a percentage of a stock’s price. The MPS of a stock tells how much people are willing to pay to own the shares, but the P/E ratio tells whether the price accurately reflects the company’s earnings potential, or its value over time. In the study GLICL &BPCL had the highest figure of P/E ratio. In this research results have significantly fulfilled the objectives of the study. In the sample companies regular and steady distribution of the DPS have made positive effects toward the MPS of share in the secondary market. Making impact on the future decision of MPS, DPS plays a vital role as one of the determining factor of share price. Which is quite similar with finding of journal article of [9]in his study of “Dividend effect on stock price –an empirical analysis of Malawi listed companies” The one of major objective of the study was to find out the relationship of determining factors of stock EPS, DPS, and BVPS on MPS. On the sample companies the impact of independent variable is increase in DPS, EPS & P/R ratio the price of stock increases and decrease in DPS, EPS& P/E ratio have decreased the price. During the period of study from 2010/2011-2020/2021 the determining factors have made both positive negative effect on the price fluctuation of the share in the NEPSE. This finding is similar with results of article of “Determinants of share price at Karachi Stock Exchange” by [10].The last objective of the research was to find out the impact of DPS, EPS, BVPS and P/E ratio on MPS. In the selected companies during the period of research the independent variable are not the reason to make the positive and negative impact solely. They make the difference in MPS when combined together. The association of EPS with DPS showed the significant inverse effect on stock price. The article of (Pradhan and Poudel) “The impact of fundamental factors on stock price in Nepalese commercial bank’’ concludes the same result.

4. CONCLUSION

The correlation coefficient analysis, relationship between MPS and other selected financial indicators (EPS, BVPS, P/E ratio and DPS) of all sampled companies are both positives and negatives. That can be concluded if independent variables (EPS, BVPS, P/E ratio DPS) increase than it causes to increases dependent variable (MPS) in case of positive correlations and also decreases MPS in case of negative correlations. Such an increasing value of MPS with EPS and DPS is healthy indicator of the financial activities of companies in the least developed countries like Nepal. It can be concluded that these sectors have good financial environment in Nepal. This Research addressed stock price determination in NEPSE. It shows how share price is affected by different variables. The study is based on six sample companies whose stocks are listed in Nepal stock exchange and traded in stock market. The above-mentioned major findings show that the market price per share has more fluctuating in trend than independent variables (EPS, DPS, P/E ratio &BVPS) which indicates MPS is more influence from others independent variables. Similarly, The MPS of most of the Companies are found to be least and insignificantly correlated with other individual financial indicators like DPS, BVPS, P/E ratio and EPS. This shows that they individually least influence share price. Therefore, there can be other factors, which influence the share price of the organization. Hence the present study confirms that the study of financial factors proves to be beneficial for the investors in Nepal Stock Exchange, as these factors possess strong explanatory power and hence, can be used to make accurate future forecasts of share price. Therefore, investors are suggested to take care of internal variables of company before investing. The risk per unit of return for investors and total risk are different in different sample companies, which have been shown by the coefficient of variation and standard deviation respectively. EPS, BPS, P/E ratio and DPS are the major influence of the stock price. Besides this, political situation, annual general meeting, assets structure, taxation policy, and capital structure of the organization also influence movement of stock price in Nepalese listed companies in NEPSE. The finding shows that MPS of one hydropower company is lowest CV which shows per unit risk of hydropower company is lower than others companies. There are more risk expose companies are insurance companies because of higher market price with higher CV of MPS.

REFERENCES

1. Allahawiah, s., & Amro, S. A. (2012). factors affecting stock market prices in Amman stock exchange. European Joural of Business and management, 4-8.
2. Almumani, A. (2014). Determinants of equity share prices of the listed banks in Amman stock exchange. Journal of business and social science , 91-104.
3. Balliey , S., & Alexander, J. (2009). Investment . New Delhi: Prentic Hall of india.
4. Bhatta, B. (2014). Combined issue Determinants of share price of Nepal commercial banks. Nepal Economic journal of Development , 3-7.
5. Chaudhary, J. (1996). Stock market volatility and the crash of 1987. Evidence from six emerging market of international money and finance, 69-81.
6. Dangol, J. (2010). Testing Random- walk behavior in nepalese stock market. PYC Nepal journal of management, 28-36.
7. Francis, J. (2002). Investment analysis and management. Newyork: MacGraw hill publication.
8. Gitman, J. L. (2009). Principles of Managerial finance 12th edition. Boston: White center for financial research.
9. Vanhorne, J., & wachowicz, J. (2000). Fundamental of financial management . New Delhi: Prentice hall of India.
10. Weston, J., & Brigham, E. (1987). Essential of managerial finance. Chicago: The Dryden press.