INTRODUCTION:

The stock market, also referred to as "the market," is the most popular financial marketplace worldwide where financial safeguards or cases on partnership income are traded. By assembling the assets from which various financial backers are allowed to start their businesses in various locations and financial backers are authorized to an optional investment opportunity, the market provides extended pull property to record organization in stock trade. The importance of the financial framework in setting aside reserves, allocating capital, implementing company management, and aiding risk management was highlighted by Levine and Zervos in 1990. Although stock exchanges in developed countries have enjoyed steady growth and stability over the long term, market declines that have shook the global financial business sectors have made stock exchanges in emerging economies the most unpredictable business sectors on the planet over the past two years (Hull, 2012).

They are also more sensitive to macroeconomic factors, such as changes in pay, inflation, financing costs, and so forth. Interest in the stock market has the potential to be both extremely rewarding and extremely dangerous. In order to increase their profits and reduce risk, possible financial backers attempt to analyse and predict the pattern of Stock Exchange costs. Financial backers take into account how specific macroeconomic circumstances, such as lending costs, rising rates, conversion standards, cash supply, and so forth, affect the presentation of their stocks while accomplishing this. According to Masuduzzaman, macroeconomic considerations play a crucial role in how a financial exchange is displayed. They can serve as a yardstick for financial backers to gauge the Stock exchange's exhibition, as well as a prime source of additional information about how the financial exchange operates. Stock markets play a significant role in a nation's business and commerce development, both of which have an impact on the economy. Long-term capital is made available to businesses through the stock exchange for investment reasons. By combining assets from different financial backers who want to invest their extra assets in optional venture routes, the market simulates the interaction between intermediation and intermediation. Before donating assets, the financial backers carefully observe the Stock exchanges' presentation by observing the composite market file. The market record provides a verifiable financial exchange execution, the yardstick to consider how specific portfolios are presented, and furthermore provides financial backers with the means of predicting future market patterns. Despite the productive market hypothesis (EMH), which holds that investors cannot obtain extraordinary benefits since all available information is fully reflected in stock market costs, many researchers agree that macroeconomic factors influence stock returns. This agreement will generally support Ross's 1976 exchange valuing hypothesis (APT), which states that stock profits depend on a variety of factors, including growth rate, organisation size, profit yield, swapping scale, total national output, consumer value record, modern creation file, joblessness rate, loan fee, genuine pay (GDP per capita pay), domestic reserve funds, stock exchange liquidity, etc. In a fast environment like the financial market, there are patterns that can either provide financial backers with favourable or negative returns. Increased market instability increases the level of risk involved and reduces stock profit potential. By gathering saving units and caring for them as deficit units that require funding to produce labour and goods, the stock market plays a fundamental role as a financial handover in the system. Thus, the market contributes to financial development by successfully allocating financial assets to lessen the risks faced by lessees and enhance benefit by raising the level of expertise of financial intermediaries. The development of financial sectors like initiatives, firms, and exchange is fundamentally highlighted by the stock exchange, which eventually promotes a healthy level of financial development in the nation. The stock market is required to advance reserve funds by providing people with financial instruments that may better satisfy their risk appetites & liquidity needs. The cost of investment funds would increase with the creation of improved reserve funds. Additionally, financial development may be encouraging for the advancement of financial exchange. Financial backers have the opportunity to raise money on the stock market at reasonable costs. By providing business sector funding rather than bank-based financing, a well-developed stock market reduces the credit risk to financial backers. This has a positive impact on financial development since a well-functioning protections market helps financial backers avoid data that is skewed. This encourages businesses to make investment decisions, which enhances the accuracy of asset categorization and so expands financial development. A very good predictor of future financial activities is the financial exchange. Given the role that the stock market plays in financial development and change, its own growth and soundness become significant. Investigating the forces that control stock exchange execution is crucial in this way. with recognizing individuals’ variables and building procedures, Stock exchange execution be able to be enhanced through subsequently setting off eco-nomic development.

LITERATURE REVIEW:

A study applied various stock indices to calculate the portfolio performance of the stocks. The present data shows that many of the stocks are not performed well. Sari, Alam and Sobarsyah (2022)¹. They have used the morkowits tests to analyse the one-way test among the 45 stocks where the all stocks are performed good in a stock market. lai, zhu, Feng and Yao (2021)². The Markowitz methods were used in this study along with two additional regression techniques, such as operating pressure and financial constraints, and it has been concluded that while regression techniques are satisfied with market returns, Markowitz methods are unsuccessful in validating the Chinese stock exchange. To support economic growth, more research might be conducted. Hasnat (2021)³ used Markowitz measures to test how well the infrastructure equity of the company performed in terms of risk and return, and it came to the conclusion that the validity could be measured by using the generalised autoregressive conditional heteroskedasticity model (GACHM), which successfully provides the better ratios. Khurram, Hamid and Javeed (2021)⁴. In this study, both open-ended and closed-ended mutual funds based on capm have been evaluated and compared using portfolio performance indicators. The Treynor ratio has a negative impact on the portfolio performance of the stock market when compared to other metrics, even though this study is successful in confirming the capm in the context of the Pakistani stock exchange. En Wu, Hoa Syu, Wei lin and Ming ho (2021)⁵. tested the effectiveness of two neutral networks using reinforcement learning and the sharpe ratio and found that using both ratios simultaneously on the neutral network produces outstanding results. It also found that while reinforcement learning is successful in reducing risk, the sharpe ratio is successful in enhancing the performance of neutral networks in the stock market. Putra and Husein (2021)⁶. The Markowitz methodologies used in this investigation led to the conclusion that LQ-45's performance had been successful in validating within the Indian stock market. While this study has fared better in the different listed stocks on the Indian stock market where a few stocks have been successful in different stocks of LQ-45. In order to choose the best stocks from the market, more study could be done. Alwi, Nurhafsari, SyataIbnas and Anugrawati (2020)⁷. With the aid of two portfolios, A and B, this study tested the performance of the Treynor ratio using Markowitz methods, and it has been found that both portfolios are successful at validating risk and returns in the context of the Indian stock market. The CAPM model, however, has outperformed both portfolios. Fahling, Ghiani and simmer (2020)⁸. This study, which relied on Markowitz methods and regression analysis, came to the conclusion that large cap companies had more success in delivering better risk and returns to the market than small cap companies, which were only successful in delivering better returns to German and US stock indices. The study also found that both stocks' returns were less risky. P. Mishra and K. Mishra (2020)⁹. This study calculated the daily returns using the event study approach and regression analysis and came to the conclusion that stocks are more volatile in the context of the Israeli stock market. During the epidemic, however, several Asian stocks had unfavourable results. The study also determined that the pandemic has a detrimental effect on the Asian stock market. Singh, Dhall, Narang and Rawat (2020)¹⁰. This study used event study methodology and regression analysis to ensure the accuracy of the returns, and it came to the conclusion that the pandemic caused a shortage of funds in the market due to the lack of manpower and other resources. It also had a negative impact on the stocks of the G-20 countries, with some of those countries experiencing greater return volatility. Further research could be done to invest in long-term investments in order to secure the future of the market. Liu and Chen (2014)¹¹. Sharpe ratio and regression analysis approaches were utilised in this study, and the results show that the hml and mom components outperformed the others and gave the portfolio comparable success possibilities. Future research should focus on establishing a relationship between these factors using risk-free returns. Zaigham, Wang and Ali (2019). Regression analysis and hypothesis testing were employed in this study, which came to the conclusion that investors are less interested in stock investments as a result of the negative effects on the relationship between the firm's investment and the stock market. Although the investors were given proper information by this study. Anitasari, Nuzula and Darmawan (2019)¹³. The sharpe ratio has been successful in providing accurate returns for the Indonesian stock exchange, according to this study, which also used regression analysis and Markowitz techniques. Additionally, they used other portfolio models and found that the robustness test is successful in giving investors more precise and reliable information. Pulungan, Wahyudi, Shuhanomo and Muharam (2019)¹⁴. This study, which made use of Markowitz methodologies and portfolio evaluation techniques, came to the conclusion that the INAFs stocks had outperformed SOEs over the time under consideration using measurement techniques in the setting of the Indonesia stock exchange market. Robiyanto, Santoso and Ernayani (2019)¹⁵. The Sahrpe ratio has been successful in confirming the performance of both funds in the context of the Indonesian stock market, however this study has performed better under the sharia mutual fund, according to Markowitz and regression methods utilised in this study. To learn more about investors' interest in Indonesian fund validation, more research might be conducted. Potrykus (2018)¹⁶. This study used the Sharpe and Calmar ratios, which are comparable in nature and yield the same results for the investment of a particular measure. It was concluded that the Sharpe ratio is appropriate for various investment decisions in order to maximise efficiency. Further study could be conducted to look at the selected stock exchanges with the lowest levels of efficiency. Robiyanto (2018)¹⁷. The study, which relied on Markowitz methodologies, came to the conclusion that the sharpe index had a significant impact on the individual stocks' ability to be validated in the setting of the Indonesian stock exchange. While the sharpe index is successful in giving the investors of the Indonesia stock exchange market precise findings. Jacob and Sinha (2018)¹⁸. This study has employed fuzzy logic and Markowitz methods to analyse data from various stock market phases and has found that stock returns can outperform, especially when they are focused on using the standardised regression coefficient to average returns and risk-adjust returns. Floros, Tabouratzi, Charamis and Zounta (2017)¹⁹. The volatility of the stocks during the period where the joiners are unable to validate the returns volatility while the leavers are given positive returns and support for volatility of stocks in the market has been examined in this study using statistical approaches. While in this study the returns and volatility of equities were examined using both joiners and leavers. Wang, Yang and Ma (2017)²⁰. While this study focused on five BRIC stocks, only one of which performed well in the context of the global stock market, it concluded that the linear conditional causality test had positively reacted to the US stock market. Further research could be conducted to examine and validate the relationship between the BRIC and US stock markets. Faruk tan (2015)²¹. Regression analysis is employed in this study to examine the volatility of equity fund returns in the stock market since Markowitz methods are not acceptable for validating returns, according to its conclusion. While the Johannesburg stock market has shown this study to perform better. Sahi and Pahuja (2015)²². Using Markowitz methodologies, this study has found that risk adjusted measures are effective at validating equities mutual funds in the context of the global stock market. While the two samples from the global stock market included in this study did better in equity funds. Further investigation might be done into the public and private sector volatility, as both have outperformed each other to the satisfaction of investors. Wang (2014)²³. This study tested the volatility of the Chinese and international stock markets using the combine generalised autoregressive conditional heteroskedasticity model (CGARCH), and it came to the conclusion that the model is only successful in validating the Chinese stock market's positive impact on the global stock market. Yang and heon lee (2013)²⁴. The vector autoregressive model is successful in validating the changes in before and after the global financial crisis, while this study showed the negative impact on the volatility in specific stocks. This study used a variety of statistical approaches on variables and came to this conclusion. Additionally, this study might pique investors' interest in making investments in the housing market during the current financial crisis. Wei (2013)²⁵. This study, which relied on statistical techniques, came to the conclusion that lead-lag had a significant impact on the stock market's ability to validate returns, assess the effectiveness of the hang seng index future and options, and determine the index's liquidity. While the performance of this study is comparable to that of the Chinese stock market. Kolbadi (2011)²⁶. Regression analysis and Markowitz methods were both used in this study, which came to the conclusion that modern and post-modern portfolio theories performed differently when it came to investments on the Tehran stock exchange market. However, this study was successful in validating statistical analysis of variance in the context of the Tehran stock exchange. Tai-leungchong, Ho sum cheng and Nga-yeewong (2010)²⁷. This study successfully validated the returns in the context of Russia, Brazil, India, and China using future and options to assess the risk and returns, concluding that the returns are more lucrative to the Russian stock market. Additionally, this study demonstrates that Brazil's results are more reliable than those of other countries. Cho (2010)²⁸. The multi-level and interactive stock market investment system (MISMIS), which was developed primarily to control the volatility of stocks in the Indian market, is more suitable for calculating the virtual trading of stocks within the Autoregressive integrated moving average (ARIMA), according to this study's analysis of various prediction techniques. The accuracy of stock trading conducted online might be investigated further. Sarno and Valente (2005)²⁹. The vector equilibrium correlation model is successful in establishing the relationships between two separate samples belonging to two different criteria, according to this study's usage of regression analysis methods. Although this investigation was unable to confirm the behaviour of the two samples. Gencay (1988)³⁰. The generalised autoregressive conditional heteroskedasticity model is suitable for validating the returns within linear and non-linear stocks, with non-linear successfully validating the returns within the context of single regression model, according to this study's use of the signal regression model. The results of additional research might be analysed using the new GARCH model.

Sample Data and Methodology:

The study is based on secondary data. The BSE-100 stocks are used in the study. The study period consists of 10 years. The performance of each stock is measured by using Sharpe ratio and Treynor ratio.

Sharpe ratio:

The risk-return performance of portfolio is indicated by the sharpe ratio. The Sharpe ratio is a formula that determines the actual return on investment after accounting for risk. As a result, the returns are flattened as if the risk were abolished, which makes it very helpful when we are comparing at least two investment alternatives.

Average strategy return - average risk free rate

Sharpe Ratio = –––––––––––––––––––––––––––––––––––––––––––

Strategy standard deviation

Treynor ratio:

We can demonstrate that there is no diversifiable risk when profits are made in the surplus of the which could have been earned on investment by using the Treynor Ratio.

The Treynor ratio displays the fund's performance after adjusting for risk. The portfolio's beta is used as the denominator in this case. Therefore, it considers the portfolio's systematic risk.

Portfolio return - risk free rate

Treynor Ratio = ––––––––––––––––––––––––––––

Portfolio Beta

Beta: With market resemblance, the beta is utilised to determine the systematic risk.

Cov (Rt, RM_t)

β = ––––––––––––––––––

Var (RM_t)

Standard deviation: The standard deviation is used to show the historical volatility.

RESULTS AND DISCUSSION:

Table 1: Data description of sharpe ratio

Sl No	Company Name	Sector	Sharpe Ratio	Rank
1	Aurobindo Pharma	Pharmaceuticals	0.760373807	1
2	Yes Bank	Bank	0.740239863	2
3	JP Associates	Construction	0.738798887	3
4	Vodafone Idea	Telecom service provider	0.724403779	4
5	Britannia Inds.	Food processing	0.684209948	5
6	Reliance Communi	Telecom	0.680634113	6
7	Tata Consumer	Fast moving consumer goods	0.678880934	7
8	Tata Steel	Steel company	0.674098148	8
9	Tata Motors-DVR	Tata motors	0.6698577	9
10	Hindalco Inds.	Aluminium and copper manufacturing company	0.668883485	10
11	Bajaj Finance	Non- banking financial	0.665907	11
12	Sun Pharma.Inds.	Pharmaceuticals	0.665907	12
13	JSW Steel	Private sector steel company	0.664275019	13
14	Maruti Suzuki	Automotive	0.662294903	14
15	Lupin	Pharmaceuticals	0.658582729	15
16	Vedanta	Vedanta	0.65230626	16
17	ICICI Bank	Bank	0.650685522	17
18	Tech Mahindra	Telecommunication	0.647156034	18
19	Punjab Natl.Bank	Bank	0.647099455	19
20	I D F C	Bank	0.646196719	20
21	Reliance Capital	Finance	0.640802548	21
22	S A I L	Central public sector	0.637425646	22
23	Wipro	Conglomerate	0.632474106	23
24	H P C L	Oil and natural gas	0.628421608	24
25	St Bk of India	Bank	0.6264114	25
26	DLF	Real state	0.624199226	26
27	Titan Company	Lifestyle	0.622928463	27
28	I O C L	Oil and gas	0.621609806	28
29	Eicher Motors	Automotive	0.621185175	29
30	Larsen and Toubro	Conglomerate	0.620633496	30
31	Ambuja Cements	Building materials	0.617260471	31
32	Jindal Steel	Steel	0.616964532	32
33	IndusInd Bank	Bank	0.616119951	33
34	B P C L	Petroleum industry	0.613255621	34
35	HCL Technologies	Information Technology	0.608605757	35
36	Suzlon Energy	Renewable energy	0.608463547	36
37	Bajaj FinServ	Finance	0.608225076	37
38	UPL	Agribusiness chemicals	0.606008686	38
39	Bank of Baroda	Bank	0.603950833	39
40	B H E L	Private sector	0.603273951	40
41	UltraTech Cem.	Building materials	0.593123437	41
42	Shree Cement	Building materials	0.592317907	42
43	Zee Entertainmen	Broadcasting services	0.589740282	43
44	United Spirits	Beverages	0.582091317	44
45	Siemens	Software development	0.578370858	45
46	Infosys	Software	0.574990538	46
47	Bharti Airtel	Telecom	0.569324444	47
48	Adani Ports	Ports and shipping	0.561950681	48
49	Cipla	Pharmaceuticals	0.557427782	49
50	Tata Motors	Automotive Manufacturing	0.55730025	50
51	Bosch	Conglomerate	0.552900323	51
52	Hero Motocorp	Automotive	0.549828039	52
53	Acc	Cement	0.549567055	53
54	Grasim Inds	Fibre chemicals agrochemicals textile insulator	0.545628723	54
55	Axis Bank	Financial services	0.543035574	55
56	Reliance Infra.	Construction	0.527198564	56
57	Reliance Power	Electric utility	0.524279939	57
58	NMDC	Mining	0.520697788	58
59	HDFC Bank	Bank	0.51783132	59
60	O N G C	Oil and gas	0.51205153	60
61	Tata Power Co.	Electric utility	0.50659973	61
62	Kotak Mah. Bank	Bank	0.49723986	62
63	M and M	Automotive	0.497021639	63
64	Dr Reddy's Labs	Pharmaceuticals	0.493617802	64
65	Asian Paints	Chemicals	0.488719334	65
66	Power Grid Corpn	Electricity grid	0.479582661	66
67	ITC	Conglomerate	0.473265144	67
68	TCS	Information technology	0.473265144	68
69	Hind. Unilever	Consumer goods	0.468523729	69
70	GAIL (India)	Energy	0.455291728	70
71	Bajaj Auto	Automotive	0.434244997	71
72	Coal India	Mining	0.422932159	72
73	Divi's Lab.	Pharmaceuticals	0.421079986	73
74	H D F C	Financial services	0.41031587	74
75	Nestle India	Food processing	0.406230842	75
76	Reliance Industr	Conglomerate	0.369160844	76
77	NTPC	Electricity	0.29133008	77

The above table displays the rankings of the top 10 companies based on Sharpe measures. According to the Sharpe ratio, Aurobindo Pharma is the best-performing company and currently holds the top spot in the market with a ratio of 0.760373807; yes bank is ranked second with a ratio of 0.74023863; and other companies include JP Associates and Vodafone Idea, which are both in the fourth and third positions, respectively. Fifth-placed Britannia Inds (0.684209948), sixth-placed Reliance Communication (0.680634113), seventh-placed Tata Consumer (0.678880934), eighth-place Tata Steel (0.674098148), ninth-place Tata Motors-DVR (0.6698577), and tenth-place Hindalco Inds (0.668883485). Here, all of the corporations are acquiring positive performance indices, demonstrating improved stock market performance.

Table 2: Data Description of Treynor ratio

Sl No	Company	Sector	Treynor Ratio	Rank
1	Axis Bank	Bank	117.4119294	1
2	United spirits	Beverages	65.46733048	2
3	Aurobindo Pharma	Pharmaceuticals	60.52821811	3
4	JP Associates	Construction	55.12256257	4
5	Britannia Inds.	Food processing	46.06588828	5
6	Eicher Motors	Automotive	44.99100521	6
7	Vodafone Idea	Telecom service provider	42.12920924	7
8	Reliance Power	Electric utility	42.04423481	8
9	Suzlon Energy	Renewable energy	41.66174811	9
10	Tech Mahindra	Telecommunication	39.76750983	10
11	Sun Pharma.Inds.	Pharmaceuticals	38.99049356	11
12	Tata Consumer	Fast moving consumer goods	38.52983099	12
13	Lupin	Pharmaceuticals	38.35489806	13
14	Wipro	Conglomerate	37.83716681	14
15	Titan Company		36.01337443	15
16	Reliance Communi	Telecom	36.7773176	16
17	Tata Motors-DVR	Automotive Manufacturing	35.55893307	17
18	HCL Technologies	Information Technology	34.80871867	18
19	Yes Bank	Financial services	34.70025325	19
20	Shree Cement	Building material	34.11279539	20
21	Reliance Infra.	Construction	31.93463944	21
22	Dr Reddy's Labs	Pharmaceuticals	30.86108976	22
23	H P C L	Oil and natural gas	28.40842295	23
24	Cipla	Pharmaceuticals	28.23596073	24
25	Tata Power Co.	Energy	28.11645095	25
26	Vedanta	Vedanta	27.17263055	26
27	Bosch	Conglomerate	26.56932968	27
28	Zee Entertainmen	Broadcasting services	26.18475152	28
29	Jindal Steel	Steel	25.99794972	29
30	Maruti Suzuki	Automotive	25.69567715	30
31	Bajaj Finserv	Financial services	24.54150318	31
32	Infosys	Software	24.53696251	32
33	Reliance Capital	Financial services	23.60657075	33
34	Bajaj Finance	Financial services	23.32230126	34
35	Divi's Lab.	Pharmaceuticals	22.50485583	35
36	Bharti Airtel	Telecom	22.48520292	36
37	Tata Motors	Automotive Manufacturing	22.2071928	37
38	B P C L	Petroleum industry	21.21605083	38
39	I O C L	Oil and gas	21.19309183	39
40	Hindalco Inds.	Manufacturing	20.88213949	40
41	I D F C	Financial services	20.6779767	41
42	IndusInd Bank	Financial services	19.93534899	42
43	TCS	Information technology	19.73870524	43
44	Tata Steel	Steel	19.57428928	44
45	S A I L	Central public sector	19.15439635	45
46	Punjab Natl.Bank	Agribusiness chemicals	19.01829717	46
47	UPL	Consumer goods	18.73172096	47
48	Hind. Unilever	Fibre chemicals agrochemicals textile insulator	18.12229227	48
49	Grasim Inds	Manufacturing	18.07073007	49
50	DLF	Ports and shipping	17.81689681	50
51	Adani Ports	Building materials	17.31284569	51
52	UltraTech Cem.	Financial services	16.44741879	52
53	St Bk of India	Private sector	16.2875112	53
54	B H E L	Automotive	15.83426075	54
55	Hero Motocorp	Food processing	15.60420173	55
56	Nestle India	Electricity grid	15.13730025	56
57	Power Grid Corpn	Steel	14.98077857	57
58	JSW Steel	Chemicals	14.77443814	58
59	Asian Paints	Information technology	14.57762152	59
60	ITC	Financial services	14.31249915	60
61	Bank of Baroda	Building materials	14.00561918	61
62	Ambuja Cements	Financial services	13.84028186	62
63	ICICI Bank	Oil and gas	13.57821091	63
64	O N G C	Conglomerate	13.18780438	64
65	Larsen and Toubro	Energy	13.01928195	65
66	GAIL (India)	Mining	12.5889406	66
67	NMDC	Building material	12.30256344	67
68	ACC	Financial services	11.79455988	68
69	HDFC Bank	Software development	11.72179424	69
70	Siemens	Mining	11.62339181	70
71	Coal India	Financial services	10.97909337	71
72	Kotak Mah. Bank	Automotive	10.78943021	72
73	M and M	Automotive	10.73327585	73
74	Bajaj Auto	Construction	9.567338626	74
75	Reliance Industr	Financial services	6.819308519	75
76	H D F C	Electricity	5.654872029	76
77	NTPC	Financial services	5.079579375	77

The above table displays the positions of the top companies based on the Treynor measures as per the Treynor ratio, with Axis Bank leading in the market with a ratio of (117.4119294) and United Spirits coming in at number two with a ratio of (65.46733048). The other companies are Aurobindo Pharma in third place with a ratio of (60.52821811), and JP Associates in fourth place with a ratio of (55.1225). With a fifth-place ranking and a ratio of (46.06588828), Eicher Motors is ranked sixth with a ratio of (44.99100521), followed by Vodafone Idea in seventh place with a ratio of (42.12920924), Reliance Power in eighth place with a ratio of (42.04423481), Suzlon Energy in ninth place with a ratio of (41.66174811), and Tech Mahindra in tenth place with a ratio of (39.76750983).

CONCLUSIONS:

A study on stock market performance utilising the Sharpe and Treynor ratio is the major focus of the study. The performance of the companies' stocks is the subject of the study. The study's analysis of the data spans a period of 24 years, from 1998 to 2022. Static techniques and metrics are employed, including market return, standard deviation, beta, Sharpe, and Treynor ratio. According to the study's metrics, Aurobindo Pharma's (0.760373807) and Axis Bank's (117.4119294) stock performances are among the best, guiding investors in choosing the best investments. In this study, the analysis was carried out methodically utilising the metrics that allowed the investors to decide where to place their investment.

REFERENCES:

1. Sari, E. (2022) Evaluation Performance of Stock Portfolio Using the Risk Adjusted Performance Methods the Case of LQ 45 Index in Indonesia Stock Exchange. https://doi.org/10.26487/hjbs.v4i2

2. Lai, F., Zhu, S., Feng, Q., and Yao, Y. (2021). Effects of Financial Market Information on Firms’ Productivity Under Operating Pressure and Financial Constraints: Evidence from the Chinese Stock Market. SAGE Open, 11(4). https://doi.org/10.1177/21582440211061377

3. Hasnat, T. (2021). Infrastructure Equity and Firm Performance in India. Millennial Asia, 12(1), 97–115. https://doi.org/10.1177/0976399620948318

4. Khurram, M. U., Hamid, K., and Javeed, S. A. (2021). Association of Mutual Fund Risk Measures and Return Parameters: A Juxtapose of Ranking for Performance in Pakistan. Journal of Asian Finance, Economics and Business, 8(2), 25–39. https://doi.org/10.13106/jafeb.2021.vol8.no2.0025

5. Wu, M. E., Syu, J. H., Lin, J. C. W., and Ho, J. M. (2021). Portfolio management system in equity market neutral using reinforcement learning. Applied Intelligence, 51(11), 8119–8131. https://doi.org/10.1007/s10489-021-02262-0

6. Ramadhan Putra, B., and Faisal Husein Mutiara Cemerlang Teknologi Sidoarjo, R. P. (2021) The Performance of the Socially Responsible Investment Index in Indonesia. Journal of Finance and Islamic, 4(2). https://doi.org/10.22515/jfib.v4i2.4901

7. Alwi, W., Nurhafsari., B., Syata, I., Ibnas, R., and Anugrawati, S. D. (2021). Portfolio Performance Evaluation for LQ 45 Index in 2016-2020 Periods Using the Treynor Method. Proceedings of the 1st International Conference on Mathematics and Mathematics Education (ICMMEd 2020), 550(Icmmed 2020), 83–88. https://doi.org/10.2991/assehr.k.210508.046

8. Fahling, E. J., Ghiani, M., and Simmert, D. (2020). Small versus Large Caps—Empirical Performance Analyses of Stock Market Indices in Germany, EU and amp; US since Global Financial Crisis. Journal of Financial Risk Management, 09(04), 434–453. https://doi.org/10.4236/jfrm.2020.94023

9. Mishra, P. K., and Mishra, S. K. (2020). Corona Pandemic and Stock Market Behaviour: Empirical Insights from Selected Asian Countries. Millennial Asia, 11(3), 341–365. https://doi.org/10.1177/0976399620952354

10. Singh, B., Dhall, R., Narang, S., and Rawat, S. (2020). The Outbreak of COVID-19 and Stock Market Responses: An Event Study and Panel Data Analysis for G-20 Countries. Global Business Review. https://doi.org/10.1177/0972150920957274

11. Liu, L., and Chen, Q. (2020). How to compare market efficiency? The Sharpe ratio based on the ARMA-GARCH forecast. Financial Innovation, 6(1). https://doi.org/10.1186/s40854-020-00200-6

12. Zaigham, G. H. K., Wang, X., and Ali, H. S. (2019). Causal Relation Between Stock Market Performance and Firm Investment in China: Mediating Role of Information Asymmetry. SAGE Open, 9(4). https://doi.org/10.1177/2158244019885146

13. Qur’anitasari, Q., Nuzula, N. F., and Darmawan, A. (2019). Critical Analysis of Sharpe, Treynor and Jensen Methods in Analyzing Stock Portfolio Performance LQ-45 Stock Studies. Asia Pacific Management and Business Application, 008(02), 89–104. https://doi.org/10.21776/ub.apmba.2019.008.02.2

14. Pulungan, D. P., Wahyudi, S., Suharnomo, S., and Muharam, H. (2019). The performance evaluation of the state-owned enterprise’s stocks in Indonesia. Investment Management and Financial Innovations, 16(2), 140–149. https://doi.org/10.21511/imfi.16(2).2019.12

15. Robiyanto, R., Santoso, M. A., and Ernayani, R. (2019). Sharia mutual funds performance in Indonesia. Business: Theory and Practice, 20, 11–18. https://doi.org/10.3846/btp.2019.02

16. Otrykus, M. (2018). Comparison of investment performance measures using the example of selected stock exchanges. financial sciences, 23(2), 30–46. https://doi.org/10.15611/fins.2018.2.03

17. Robiyanto, R. (2018). Performance Evaluation of Stock Price Indexes in the Indonesia Stock Exchange. International Research Journal of Business Studies, 10(3), 173–182. https://doi.org/10.21632/irjbs.10.3.173-182

18. Sinha 1, A. K., and Jacob 2, A. J. (2018). The Performance of Stock Portfolios formed using Fuzzy Logic. Financial Statistical Journal, 1(2). https://doi.org/10.24294/fsj.v1i2.814

19. Floros, C., Tabouratzi, E., Charamis, D., and Zounta, S. (2017). Accounting and Stock Market Performance in the US: Evidence from Joiners and Leavers. Theoretical Economics Letters, 07(04), 696–708. https://doi.org/10.4236/tel.2017.74050

20. Wang, L., Yang, Y., and Ma, Y. (2017). The Impact of US Stock Market on the Co-Movements of BRIC Stock Markets—Evidence from Linear Conditional Granger Causality. Open Journal of Statistics, 07(05), 849–858. https://doi.org/10.4236/ojs.2017.75060

21. Tan, Ö. F. (2015). Mutual Fund Performance: Evidence From South Africa. EMAJ: Emerging Markets Journal, 5(2), 49–57. https://doi.org/10.5195/emaj.2015.83

22. Sahi, A., and Pahuja, Dr. A. (2015). Performance Evaluation of Selected Open Ended Equity Funds for the Post Recession Period: An Indian Perspective. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2622404

23. Wang, L. (2014). Study on the Extreme Risk Spillover between China and World Stock Market after China’s Share Structure Reform. Journal of Financial Risk Management, 03(02), 50–56. https://doi.org/10.4236/jfrm.2014.32006

24. Yang, J. Y., and Lee, S.-H. (2013). A Study of Changes in Risk Appetite in the Stock Market and the Housing Market before and after the Global Financial Crisis in 2008 Using the vKOSPI. Modern Economy, 04(11), 712–722. https://doi.org/10.4236/me.2013.411077

25. Wei, J. (2013). The Efficiency Research on Stock Index Derivatives in a Bear Market—The Evidences from Hangseng Index Derivatives Markets. Technology and Investment, 04(02), 101–106. https://doi.org/10.4236/ti.2013.42012

26. Kolbadi, P., and Ahmadinia, H. (2011). Examining Sharp, Sortino and Sterling Ratios in Portfolio Management, Evidence from Tehran Stock Exchange. International Journal of Business and Management, 6(4). https://doi.org/10.5539/ijbm.v6n4p222

27. Chong, T. T.-L., Cheng, S. H.-S., and Wong, E. N.-Y. (2010). A Comparison of Stock Market Efficiency of the BRIC Countries. Technology and Investment, 01(04), 235–238. https://doi.org/10.4236/ti.2010.14029

28. Cho, V. (2010). MISMIS-A comprehensive decision support system for stock market investment. Knowledge-Based Systems, 23(6), 626–633. https://doi.org/10.1016/j.knosys.2010.04.009

29. Sarno, L., and Valente, G. (2005). Modelling and forecasting stock returns: Exploiting the futures market, regime shifts and international spillovers. Journal of Applied Econometrics, 20(3), 345–376. https://doi.org/10.1002/jae.787

30. Gençay, R. (1998). The predictability of security returns with simple technical trading rules. Journal of Empirical Finance, 5(4), 347–359. https://doi.org/10.1016/S0927-5398(97)00022-4

Received on 02.11.2022 Modified on 13.12.2022

Asian Journal of Management. 2023;14(1):94-100.

DOI: 10.52711/2321-5763.2023.00015