Impact of Change in Promoters’ Shareholding Pattern on the Performance of Small-Cap-Value Equity Stocks in the National Stock Exchange of India

Abstract

This paper studies the impact of a change in promoter shareholding on small-cap-value stocks. NIFTY Small Cap 250 index stocks within the top 20th percentile of the book-to-market (B/M) ratio of the same universe have been considered for this study. The paper uses regression analysis for understanding the impact of independent variables on returns. The universe is further narrowed down to stocks with a positive change in promoter shareholding, which is found to be negatively related to stock returns. In addition, although the book-to-market ratio does not play any role in the prediction of returns while within this narrowed-down universe, the size effect is present. The results are discussed with reference to some relevant past research literature, and the scope for further research is also discussed.

1. Introduction

A promoter is one who conceives the idea of setting up a business at a certain location. A promoter is also in charge of performing the necessary formalities for starting the company. A promoter may be an individual, a firm, or an association of persons or a company (Shleifer 2005). Persons who are involved in assisting the promoter in the completion of various legal formalities are professionals and not promoters. Shares these promoters hold constitute promoter shareholding.

In general, promoters are the biggest shareholders in their companies. They are also well informed about the company’s prospects (Shleifer 2005). That is why investors should monitor what promoters do with the shares they own as it will provide clues regarding what the promoters think about the company’s future (Pant and Pattanayak 2007). For example, if promoters buy a large number of shares, this may indicate that they think the stock has been undervalued. In addition, when promoters sell or buy shares, it leads to changes in the price of stocks and so this activity must be keenly observed.

Promoters have all the information about their company. If a promoter is investing, it indicates that he/she believes in the prospects and also considers that it is better to invest in the company rather than putting money elsewhere (Ganguli and Agrawal 2008). A promoter pledging his/her shares or decreasing his/her holding may signal otherwise (Raju and Sapra 2010).

Although an increased stake by a promoter is a perceived positive, investors must understand the actual reasons behind the move by the promoter. They should ask, for example, whether the company is planning to delist or whether any corporate restructuring is taking place. In the case of Essar Oil, in the year 2015, the promoters raised their stake in the company from 16% to 25% because the company was planning to delist.

An increase in promoters’ shareholding that results from a bigger investor acquiring a controlling stake in the company is also perceived as a positive sign. For example, in the case of Hexaware Technologies, the promoter stake was raised from 28% in March 2013 to 64% in March 2014, a change of 128 percent. The company Baring Private Equity, Asia, acquired a controlling stake in that company in September 2013. When a big investor buys a stake, it is considered a positive signal.

However, all the investors must also bear in mind that an increase in the promoters’ shareholding is not a guarantee that the stock price will rise. For example, in the company United Spirits, promoter shareholding went up from 25% in March 2013 to 39% in March 2014. The stock price fell by approximately 9% after that.

In addition, when a promoter sells his/her stake, it is not always a negative for the company. The reason for the step may be regulatory. For example, the market regulator SEBI has mandated that promoters cannot own greater than 75% of the stake in a PSU. This would have resulted in a reduction in the promoter shareholding in various PSUs across the country.

Although investors must take notice of any large-scale exits by promoters, this may not necessarily result in the stock performing poorly. For instance, there are nine stocks in the BSE 500 index in which the promoters reduced their holding in the year 2014, but all nine stocks gave over 100% return on investment after April 2015. For example, in the case of Kingfisher Airlines, promoter shareholding went down from 32% in March 2013 to 16% in March 2014 but the stocks delivered returns of 60% after 31 March 2014.

This study is focused on small-cap companies as the impact of an increase in promoter shareholding will be higher in small-cap stocks than in large-/mid-cap stocks (Ganguli and Agrawal 2008). A high promoter shareholding generally indicates that the company will perform well because the promoters were involved in the decision making, which will have a positive impact on the company. This, in turn, will have a positive impact on share price (Sahoo and Rajib 2010).

Value stocks have a high book-to-market value and trade at a price below what the company’s performance otherwise indicates (Piotroski 2000). Piotroski studied such stocks and identified nine parameters pertaining to liquidity, profitability, and operational efficiency that constitute the Piotroski F score, a fundamental analysis technique for identifying good buys among value stocks. In his study, he also pointed out that factors such as a change in promoter holding could be instrumental in helping predict stock performance, especially for small companies. In small cap companies, usually the influence of the promoters is much more than the other market forces. This study is based on small cap value stocks (low price to book value ratio) as these were found to be under-researched. Our study, thus, is based on equity stocks from the NIFTY Small Cap 250 index, which represents 250 companies (companies ranked 251–500) from NIFTY 500. This index measures the performance of companies that have a small market capitalization. The main objective of the paper is to examine the impact of promoter shareholding on the performance of small-cap companies listed on the National Stock Exchange of India. The study could help in the decision-making process of investors looking to invest in small-cap companies in India It could guide the potential investors as to the change in risk with an increase or a decrease in the promoter holding in a company.

In the paper, we begin with a detailed study of the papers and articles that we referred to in order to substantiate the paper. The review of the literature is followed by the research methodology, analysis and inferences from the analysis, results, and conclusion, which also incorporates the limitations and future scope of the study.

2. Review of Literature

Many researchers have tried to develop a methodology to relate the changes in share prices to the financial fundamentals of the firm. One of the first such studies was carried out by Lev and Thiagarajan (1993), where they analyzed a set of financial variables that they stated would help analysts valuate securities. They examined these claims by the incremental value relevance of the variable over earnings. Their research showed that the incremental value relevance of most of the identified fundamentals holds. A significant relation was demonstrated between earning response coefficient and future earnings growth. The quality of earnings was found to be more strongly associated with the response coefficient rather than with a time-based method. The research was guided more by analyst description rather than a statistical search procedure.

In the same context, the research performed by Harris and Marston (1994) was found to be highly relevant as it brought forth the concept of connecting beta and future growth prospects with a stock’s book-to-market value. The results suggested that after growth is controlled, beta has a substantial positive relationship with the ratio. They also concluded that when growth is mispriced, the book-to-market effect is difficult to explain. Thus, the paper indicated that growth and beta could be partly employed to explain the book-to-market ratio behavior but could not fully describe the effect.

Along similar lines, Fama and French (1995) showed how the economic foundation is an important link between average stock return and size, book-to-market equity, and the already-observed average return. The research observed that if the average return is due to the pricing rationale, there should be a similar factor of risk related to size and book to equity. In addition, low book to equity and book to market are linked to strong positive profitability. These studies have been complemented by the research carried out by Piotroski (2000), who applied a simple fundamental strategy based on accounting to the portfolio of high book-to-market stocks, particularly those categorized as value stocks. In the study, the primary methodology used was to form portfolios based on an aggregate score of firms called the F Score. Firms with high F Scores based on the aggregate signals showed strong fundamental signals. The paper concluded that investors can use relevant historical information to remove firms with not-so-good prospects from a book-to-market (high) portfolio based on the F Score.

Guay (2000) believed fundamental analysis could distinguish winners from losers. Many academic studies, including those by Fama and French and by Lakonishok, Shleifer, and Vishny, have investigated stock returns for “value” (high book-to-market) corporations and “growth” (low) enterprises. Over the previous thirty years, value companies have outperformed growth equities. The Griffin and Lemmon (2002) O-Score links the book to distress risk, market equity, and stock returns. Using Ohlson’s O-score, the return differential between low and book-to-market (high) firms was found to be twice as great as that between other firms and even the three-factor models could not explain this phenomenon. High distress risk had the biggest return reversal during earnings. Analyst-undercovered small enterprises had the highest book-to-market values. Kousenidis et al. (2000) found that book-to-market values are correlated with average stock and book returns in emerging countries. In another Romanian business enterprise research, corporate social responsibility, intellectual capital, and performance were found to be strongly linked (Popescu and Popescu 2019).

The book-to-market value of Athens Stock Exchange equities classified by size showed how accounting profitability has changed. The return on investment employed book-to-market value and size to reduce stock return risk. The results showed that book-to-market value and size captured everything the return on investment missed. Size-classified ROI decreases. The paper showed how accounting data explain stock performance in Greece and could be applied to other markets. Empirical studies showed that investing in stocks with a low price-to-value ratio yields higher returns (Kousenidis et al. 2000). Doukas et al. (2004) examined glamour and value stock return errors. The study examined value stock performance and showed that there are more forecast mistakes and downward revision related to book-to-market (high) stocks than to book-to-market (low) stocks, indicating that the expectations of growth stock investors are unrealistic. Understanding value stocks is crucial. Doukas et al. (2004) conducted a notable study on value stocks that examined the differing views of analysts on value stock performance. Analyst forecasts varied, which affected the stock price. The article examined whether opinion differences reflected cross-sectional performance and the opinion difference between growth and value stocks in US markets from 1983 to 2001.

Value stocks have more investor disagreement than growth stocks. Chan and Lakonishok (2004) reviewed, discussed, and updated empirical research on value and growth investment. The performance of value versus that of growth stock was also reviewed. The publication evaluated empirical studies based on alternative theories and presented new findings from updated, extended samples. In the late 1990s, value stocks performed well. According to standard risk assessments, value stocks were not riskier, suggesting that investment management’s behavioral consideration and agency charges caused value growth spread. Mohanram (2005) included a large sample of low book-to-market equities with substantial growth. The study took growth firms with low book-to-market ratios and combined standard basic indicators, such as earnings and cash flows, with measures such as earnings stability, capital spending, and R&D to develop the GSCORE index. The study found that enterprises with higher GSCORE give higher size-adjusted returns. The analysis showed that shorting stocks yields returns. This article contradicted the value investing method used by Piotroski (2000) that generated excess profits by disregarding a class of corporations. Aggarwal and Gupta (2009) used the method employed by Piotroski and So (2012) extensively in their research on the Indian stock market. The study showed that fundamental-analysis-based investing might distinguish winners from losers. By investing in high-F-Score companies and shorting low-F-Score companies, value investors could shift their return distributions toward the right.

Kumar and Singh (2013) found that promoters controlling 40% or more link promoter and company interests with improved performance. They also found that foreign promoters tend to boost stock price and performance.

Recent glamour and value strategy studies have focused on foundational assessments. Noma (2010) analyzed financial-statement-based value investing and found that F-Score basic signals differentiate winners from losers and hedging methods raise the mean returns by 7.8%. The study advised buying high-BM enterprises with high F Scores and low-BM firms with low F Scores. As per the study, the F Score helps predict future earnings without risk. The four major firm groups endorsed the life cycle hypothesis. The study indicated that early stage momentum losers face investor neglect and strong hostility toward the company and said that contextual FSA could anticipate future earnings readily. “Good News for Value Stocks: Further Evidence on Market Efficiency” by La Porta et al. (1997) hypothesized that investor mistakes drive value stock returns. It examined how announcements related to earnings affect stock prices.

Another study examined value and growth companies five years after portfolio construction. Announcement returns showed that value equities outperform growth stocks due to strong earnings surprises. It was also found that earnings-to-price ratios, book-to-market ratios, cash-flow-to-price ratios, and dividend yields help forecast stock returns (Piotroski and So 2012). In the study, value equities outperformed growth stocks but whether the return differential reflected risk or mispricing was debated. Mispricing explained that growth stock prices indicate optimistic expectations, while value stock prices reflect negative expectations. Asness et al. (2013) examined return premia to value and global asset class strategies. Existing theories at the time (2013 and earlier) could not explain the strong association between value and momentum across asset classes, which became harder with a momentum portfolio’s high return premium and Sharpe ratio. In the study, a simple three-factor model grouped all asset classes into 48 global test assets. The study priced global assets across markets. A study by Mundi and Gautam (2021) used Bombay Stock Exchange data to confirm the inconsistency in capital structure determinants for Indian hospitality enterprises. The study showed that: according to fixed-effects regression models, firm size and return on assets are substantially related to TDR, LTDR, and SDR; growth rate, tangibility, and volatility greatly influence TDR and LTDR; and only TDR is related to the non-debt tax shield (Mundi and Gautam 2021).

Mamashli and Osku (2016) examined how institutional ownership and other factors affect stock prices. The study hypothesized that institutional ownership affects the stock prices of the companies listed on the Tehran Stock Exchange. Multiple regressions showed that institutional ownership raises stock prices; raises institutional ownership; and lessens manager–owner disagreements, raising stock prices and maximizing shareholder wealth.

Pandey and Sahu (2019) found a positive association between manufacturing stock performance and promoter holding based on monitoring and expropriation that allows major stakeholders with enough influence to expropriate value from small stakeholders by diverting from the wealth maximization purpose to persuade minority stakeholders to sell their interests at lower prices. In Malaysian corporates, Amran and Ahmad (2014) found that higher promoter holding decreases conflict and aligns stakeholders’ interests toward wealth maximization. Kumar and Singh (2013) found that due to convergence of interest, there is a positive association between promoters’ stake and firm performance only after the promoter stake becomes 40%. Gaur et al. (2015) found that promoters’ lack of ownership causes agency difficulties and poor performance. Similarly, according to Mishra and Kapil (2017), promoter ownership improves business performance and ownership levels affect the association. Iwasaki and Mizobata (2020) found comparable results for European corporations. Unlike huge corporations, the expansion of SMEs drives socio-economic goals. Their governing structures and issues differ. A research study examined how SMEs in India perform on the basis of board quality from an agency perspective. Heteroscedasticity robust standard errors (RSEs) were used to model 68 BSE-listed SMEs from 2013–2014 to 2017–2018. The regression of SME performance versus board features showed that SMEs with highly concentrated ownership structures performed better. Leveraged enterprises performed significantly better than unlevered firms (Mehrotra et al. 2021).

Another article examined how exchange rates affect Vietnamese company stock prices asymmetrically. A partial sum decomposition process was used to break down the nominal exchange rate into currency depreciation and appreciation for monthly data from January 2001 to May 2018 on the basis of VN-Index stock prices from the Ho Chi Minh Stock Exchange (HOSE). Asymmetry was estimated for the long-run connection and short-run error correction procedure. The study found the following: exchange rate variations affect stock values differently in the short and long terms, depreciation and appreciation affect stock values differently, currency appreciation affects stock prices more than long-term depreciation, and long-term stock values are unaffected by an exchange rate without asymmetry (implying a symmetrical assumption that understates exchange rate effects on Vietnamese stock prices). This discovery affected Vietnamese regulators. As per Dang et al. (2020), Vietnamese regulators need to assess long-term and short-term exchange rate–stock price relationships to manage stock and foreign currency markets. As per another article, internal business governance and external audit prevent stock price crashes. Internal corporate governance was found to be strongly associated with future stock price crash risk in 655 non-financial listed firms in Vietnam’s Hanoi and Ho Chi Minh City stock exchanges from 2010 to 2019. Strong boards anticipated stock crashes. Audit committee failure increased crash risk. These findings suggest that corporate governance could prevent stock market crashes. Dang and Nguyen (2021) found that external audit quality increases the audit committee’s crash risk prevention. A recent study by Yeung and Lento (2018) evaluated China’s ownership structure, audit quality, board structure, and stock price crash risk. The study examined whether a Chinese corporation’s ownership, audit, and board structure affect its stock price collapse risk. After the IFRS and split-share reforms, better ownership structures and audit quality were found to be associated with lower stock market crash probabilities. Two crash risk indicators yielded consistent results that were endogeneity resistant. The study found that board structure does not affect stock price collapse risk (Yeung and Lento 2018).

Some evidence suggests that promoter holding hurts business performance. According to Jameson et al. (2014), in India, promoters with extensive control lead to weak corporate governance and business underperformance. A multi-criteria decision-making model study on the impact of CSR on 30 corporate efficiencies listed on the Bombay Stock Exchange (BSE) focused on technical efficiency and high-efficiency drivers. Data envelopment analysis and free disposal hull were used to rank the efficiency of 30 companies listed on the BSE index from 2014 to 2020. Truncated regression analysis confirmed the CSR–corporate financial performance relationship (Deb et al. 2022). Wang and Shailer (2015) found comparable results in 18 emerging markets.

Promoter holding studies have yielded inconsistent results, aside from the extreme opinions above. Pant and Pattanayak (2007) found that the convergence of interest and “monitoring influence” boosts business performance as promoter shares grow. Due to “entrenchment impact”, the promoters’ stake reduces performance. Selarka (2006) found that promoter holding initially improves firm performance. However, after promoter holding reaches 45 percent, performance drops until promoter holding reaches a value of 63 percent, when performance rises again with promoter holding.

The broad literature study showed conflicting results on promoter holding and stock performance. Numerous studies have found that promoter-held organizations perform well. However, India’s banking system restricts promoters to a 15 percent stake. Do such companies perform poorly? Results have been inconsistent. Corporate governance laws require promoters to lower their ownership and limit their stake expansion in specific areas. Such studies have not been carried out for the small-scale sector in India, where promoters normally have a large interest. Most research has focused on stock price movements owing to promoter holding announcements rather than fundamental considerations. Thus, our study examined how promoter holding affects the performance of small-cap NSE-listed companies.

3. Methods

3.1. Sample Design

For the study, the stocks in the NIFTY Small Cap 250 index as of the 30th of September each year from 2011 to 2015 were considered. The stocks were ranked in descending order on basis of their book–market (B/M) ratio, and stocks within the top 20th B/M ratio percentile were considered. The stocks with a positive change (increase) in the promoters’ shareholding among those within the top 20th B/M percentile formed an equally weighted portfolio. This portfolio was formed each year on the 30th of September of the year, and the portfolio returns were observed for two years. For example, the returns on the portfolio formed on 30 September 2011 were observed for the time periods from 1 October 2011 to 30 September 2012 and from 1 October 2011 to 30 September 2013. The returns on the portfolios formed in the later years were observed similarly. The last portfolio was formed on 30 September 2015.

In an attempt to reduce the possible distortions in the analysis due to extreme values, the universe of stocks as generated above was further limited as follows:

(1)

Stocks with one-year and two-year returns exceeding 130% were excluded from the universe.

(2)

Stocks with a change of less than 1.5% in percentage promoter holdings were excluded from the universe.

Most of the one-year and two-year returns were greater than multiples of 10, with the maximum returns capped at 130 percent, as stated above. All the B/P ratios were less than 3. Most of the changes in percentage promoter holdings were less than 5. The market capitalizations were in thousands of crores. In an attempt to reduce the disparities in scale, the variables were modified as follows:

(1)

Any change in the percentage promoter holding was multiplied by 10 (modified percentage promoter holding).

(2)

Natural logarithms of market capitalizations were considered instead of absolute numbers (termed as log market cap).

(3)

B/P ratios were multiplied by 10 (modified B/P).

Table 1. Portfolio 2011 (financial details of small-cap companies whose promoter holdings increased in a year).

Name	Market Cap (Bn INR)	B/P	Promoters’ Share 2011	Promoters’ Share 2012	Increase +1/0	Share Price 2011	Share Price 2022	Share Price 2013	One-Year Percentage Returns 2011–2012	Two-Year Percentage Returns 2011–2013
India Cements Ltd./The	26.66	1.95	25.18	25.77	1	71.45	93.2	50.5	30.44087	−29.3212
Indiabulls Real Estate Ltd.	39.70	1.73	22.96	38.47	1	73.15	66.8	55.85	−8.68079	−23.65
Rain Industries Ltd.	39.50	0.99	35.71	36.7	1	25.9	43.7	36.9	68.72587	42.47104
CG Power and Industrial Solutions Ltd.	27.40	0.97	40.92	41.69	1	50.61	43.4	30.22	−14.2462	−40.2885
JK Tyre & Industries Ltd.	22.70	0.86	46.97	47.34	1	14.53	21.74	18.31	49.62147	26.01514
Welspun Corp Ltd.	35.70	0.81	41.65	46.57	1	100.74	94.46	28.17	−6.23387	−72.0369
							Portfolio formed in 2011	Equally weighted portfolio returns	19.93789	−16.1351

Source: Bloomberg; data collected in October 2018.

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Table 2. Portfolio 2012 (financial details of small-cap companies whose promoter holdings increased in a year).

Name	Market Cap (Bn INR)	B/P	Promoters’ Share 2012	Promoters’ Share 2013	Increase +1/0	Share Price 2012	Share Price 2013	Share Price 2014	One-Year Percentage Returns 2012–2013	Two-Year Percentage Returns 2012–2014
Gammon India Ltd.	5.89	3.66	34.69	35.26	1	40.4	11.39	34.45	−71.8069	−14.7277
Firstsource Solutions Ltd.	4.11	3.45	19.86	56.86	1	11.35	17.8	41.25	56.82819	263.4361
Ansal Properties & Infrastructure Ltd.	5.54	2.99	46.46	47.06	1	31.45	16.4	31.7	−47.8537	0.794913
Electrosteel Castings Ltd.	7.01	2.64	34.8	39.64	1	20.05	13.15	20.95	−34.414	4.488778
Escorts Ltd.	7.25	2.46	27.67	41.98	1	63.95	87	149.4	36.04378	133.62
Network18 Media & Investments Ltd.	5.25	2.44	49.55	73	1	33.05	30.1	45.9	−8.92587	38.88048
Srei Infrastructure Finance Ltd.	13.1	2.34	47.07	48.77	1	27.3	18.65	46.55	−31.685	70.51282
GTL Ltd.	4.05	2.22	23.36	44.33	1	8.65	1.7	2.85	−80.3468	−67.052
Anant Raj Ltd.	17.3	2.16	61.93	62.19	1	80.2	41.85	53.2	−47.818	−33.6658
REI Agro Ltd.	11.7	2.08	42.7	52.7	1	10.45	7.5	2.62	−28.2297	−74.9282
Patel Engineering Ltd.	7.39	1.93	45.62	45.69	1	82.4	33.55	95.7	−59.284	16.14078
Usha Martin Ltd.	9.37	1.9	400.41	41.37	1	28.85	23	21.35	−20.2773	−25.9965
HCL Infosystems Ltd.	10.10	1.89	50.77	53.19	1	43.733	25.28	79.31	−42.1947	81.35047
Jindal Poly Films Ltd.	8.84	1.84	72.1	74.63	1	209.55	141.85	298.25	−32.3073	42.3288
Alok Industries Ltd.	15.50	1.8	31.78	34.16	1	12.9	8	12.35	−37.9845	−4.26357
NCC Ltd./India	14.50	1.78	19.56	20.25	1	29.97	13.42	44.05	−55.2219	46.98031
DCM Shriram Ltd.	7.59	1.72	61.14	62.28	1	51.6	58.6	233.25	13.56589	352.0349
JM Financial Ltd.	11.70	1.69	67.18	69.09	1	16.8	21.45	38.45	27.67857	128.869
Gujarat Alkalis’ & Chemicals Ltd.	8.92	1.56	36.722	40.6	1	133.25	152.55	203.15	14.48405	52.45779
							Portfolio formed in 2012	Equally weighted portfolio returns	−23.671	53.22428

Source: Bloomberg; data collected in October 2018.

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Table 3. Portfolio 2013 (financial details of small-cap companies whose promoter holdings increased in a year).

Name	Market Cap (Bn INR)	B/P	Promoters’ Share 2013	Promoters’ Share 2014	Increase +1/0	Share Price 2013	Share Price 2014	Share Price 2015	One-Year Percentage Returns 2013–2014	Two-Year Percentage Returns 2013–2015
Central Bank of India	49.1	2.25	85.31	88.63	1	51.5	60.65	85.85	17.76699	66.69903
Syndicate Bank	66.2	1.93	66.17	67.39	1	71.85	114	87.9	58.66388	22.3382
IDBI Bank Ltd.	103	1.89	71.72	76.5	1	62.25	60	80	−3.61446	28.51406
UCO Bank	42	1.83	69.26	77.2	1	63.2	80.55	45.85	27.45253	−27.4525
Hindalco Industries Ltd.	175	1.82	32.06	37	1	114	155.25	83.95	36.18421	−26.3596
Oriental Bank of Commerce	73.5	1.63	58	59.13	1	152.3	224	130.75	47.07814	−14.1497
Allahabad Bank	63.2	1.62	55.24	58.9	1	80.35	97.4	75.05	21.21966	−6.59614
Corp Bank	56.7	1.47	59.82	63.33	1	52.44	63.34	44.55	20.78566	−15.0458
Andhra Bank	53	1.42	58	60.14	1	53.45	64.55	69.2	20.76707	29.46679
Canara Bank	170	1.35	67.72	69	1	229.21	357.3	271.57	55.88325	18.48087
Indian Bank	75.7	1.33	80	81.51	1	70.85	152.15	140.8	114.7495	98.72971
Bank of India	180	1.23	64.11	66.7	1	174.5	232.6	144.65	33.29513	−17.106
Union Bank of India	120	1.22	57.89	60.13	1	114.75	200.95	173.85	75.11983	51.50327
Punjab National Bank	254	1.2	57.87	58.87	1	94.06	175.85	138.15	86.95514	46.87434
Cairn India Ltd.	520	0.93	58.77	58.85	1	324.8	304.65	152.85	−6.20382	−52.9403
							Portfolio formed in 2013		40.40685	13.53041
								Equally weighted portfolio returns

Source: Bloomberg; data collected in October 2018.

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Table 4. Portfolio 2014 (financial details of small-cap companies whose promoter holdings increased in a year).

Name	Market Cap (Bn INR)	B/P	Promoters’ Share 2014	Promoters’ Share 2015	Increase +1/0	Share Price 2014	Share Price 2015	Share Price 2016	One-Year Percentage Returns 2014–2015	Two-Year Percentage Returns 2014–2016
Indian Bank	53.4	2.37	81.51	82.1	1	152.15	133.65	223.8	−12.1591	47.09169
Canara Bank	122	2.15	69	69.91	1	344.47	271.57	310.57	−21.1629	−9.84121
Union Bank of India	86.6	2.11	60.13	60.47	1	190.7	173.85	143.2	−8.83587	−24.9082
Syndicate Bank	60	2.05	67.39	69.24	1	108.75	81.8	75.3	−24.7816	−30.7586
Punjab National Bank	269	1.31	58.87	59.86	1	175.85	132.45	143.35	−24.6801	−18.4817
Bank of Baroda	310	1.1	56.26	57.53	1	179.72	181.1	168.9	0.767861	−6.02048
Vedanta Ltd.	559	1.07	54.96	59.52	1	270.9	83.4	178.75	−69.2137	−34.0162
Bajaj Holdings & Investment Ltd.	114	0.91	40.17	41.19	1	1380.1	1591.4	1938.2	15.31048	40.4391
							Portfolio formed in 2014	Equally weighted portfolio returns	−18.0944	−4.56196

Source: Bloomberg; data collected in October 2018.

and

Table 5. Portfolio 2015 (financial details of small-cap companies whose promoter holdings increased in a year).

Name	Market Cap (Bn INR)	B/P	Promoters’ Share 2015	Promoters’ Share 2016	Increase +1/0	Share Price 2015	Share Price 2016	Share Price 2017	One-Year Percentage Returns 2015–2016	Two-Year Percentage Returns 2015–2017
Bank of India	126	2.44	64.43	68.01	1	134.35	116.4	137.75	−13.3606	2.530703
Union Bank of India	99.5	1.89	60.47	63.44	1	173.85	143.2	126.7	−17.6301	−27.1211
Hindalco Industries Ltd.	266	1.52	36.99	40.04	1	70.9	154.1	241.6	117.3484	240.7616
Punjab National Bank	261	1.46	59.86	62.08	1	132.45	143.35	129.6	8.229521	−2.15176
Reliance Power Ltd.	158	1.23	74.98	75	1	43.5	48.65	40.3	11.83908	−7.35632
DLF Ltd.	282	0.97	74.91	74.95	1	134.2	152.85	167.45	13.89717	24.77645
Central Bank of India	160	0.91	81.46	86.4	1	83.75	95.8	74.2	14.38806	−11.403
Bajaj Holdings & Investment Ltd.	144	0.83	41.19	45.87	1	1591.4	1938.2	2884.35	21.79213	81.24607
State Bank of India	1990	0.81	58.6	61.32	1	235.15	255.3	251.3	8.568999	6.867957
Torrent Power Ltd.	77.3	0.8	53.44	53.57	1	180.1	180.3	211.85	0.111049	17.62909
CESC Ltd.	80	0.75	49.5	49.92	1	402.13	484.46	773	20.47348	92.22639
							Portfolio formed in 2015	Equally weighted portfolio returns	16.87792	38.00056

Source: Bloomberg; data collected in October 2018.

show the portfolio returns of the equally weighted portfolios which were formed for the years 2011, 2012, 2013, 2014 and 2015, respectively.

3.2. Benchmarking Returns

The returns obtained in various years are benchmarked against the small-cap index in order to examine the performance of portfolios containing small-cap stocks with increased promoter interest. The same can be seen in

Table 6. Cumulative returns from the portfolios in Table 1, Table 2, Table 3, Table 4 and Table 5.

	Equally Weighted Portfolio One-Year Return Percentage	Equally Weighted Portfolio Two-Year Return Percentage	Benchmark One-Year Return (in Percent)	Benchmark Two-Year Return (in Percent)
Portfolio formed in 2011	19.93789	−16.1351	7.21	−8.14247
Portfolio formed in 2012	−23.671	53.22428	−14.32	27.12342
Portfolio formed in 2013	40.40685	13.53041	48.37	81.05591
Portfolio formed in 2014	−18.0944	−4.56196	22.03	42.71409
Portfolio formed in 2015	16.87792	38.00056	16.95	41.08848

The returns have been benchmarked against NIFTY Small Cap 100.

.

4. Analysis

All the regressions were run in GRETL econometric software.

• One-Year Returns:

A simple OLS regression of one-year percentage returns as the dependent variable and modified percentage promoter holding as the independent variable was run on the final universe of stocks. The coefficient of the independent variable and the constant turned out to be statistically insignificant. This means that the independent variable that changes in promoter holdings has no bearing on the dependent variable, i.e., the one-year percentage returns. The results are given in

Table 7. Model 1: OLS.

	Coefficient	Std. Error	t-Ratio	p-Value
Constant	8.5672	10.8712	0.7881	0.4366
Change in Promoter Holding	−0.232930	1.26015	−0.1848	0.8546
Mean Dependent Variance	7.063612	S.D.-dependent variance	43.97143
Sum Squared Residual	61768.73	S.E. of regression	44.63787
R-Squared	0.001662	Adjusted R-squared	−0.030542
F(1, 31)	0.034167	p-Value (F)	0.854556
Log-Likelihood	−171.1466	Akaike criterion	346.2932
Schwarz Criterion	349.2862	Hannan–Quinn	347.3003

.

• Inference from the Regression:

A multiple regression with one-year percentage returns as the dependent variable and modified percentage promoter holding, log market cap, and modified B/P as the independent variables was run. The constant and the coefficients of all independent variables turned out to be statistically insignificant. Hence, it was inferred that a modified promoter holding, the log of market capitalization, and modified B/P did not influence the change in one-year percentage returns. The results are given in

Table 8. Model 2.

	Coefficient	Std. Error	t-Ratio	p-Value
Constant	−217.278	190.757	−1.139	0.264
Log of Mkt Cap	9.15825	7.29311	1.256	0.2192
Change in Promoter Holding	0.0830186	0.127754	0.6498	0.5209
Modified B/P	−0.314444	1.37643	−0.2284	0.8209
Mean Dependent Variance	7.063612	S.D.-dependent variance	43.97143
Sum Squared Residual	56443.27	S.E. of regression	44.11711
R-Squared	0.087735	Adjusted R-squared	−0.006637
F(3, 29)	0.777866	p-Value (F)	0.515909
Log-Likelihood	−169.6590	Akaike criterion	347.3179
Schwarz Criterion	353.3039	Hannan–Quinn	349.332

Excluding the constant, the p-value was the highest for variable 10 (BP1).

.

• Two-Year Returns

• Inference from the Regression:

A simple OLS regression of two-year percentage returns as the dependent variable and modified percentage promoter holding as the independent variable was run on the final universe of stocks as can be inferred from

Table 9. Model 1.

	Coefficient	Std. Error	t-Ratio	p-Value
Constant	27.8893	13.6802	2.039	0.051
Change in Promoter Holding	−3.10212	2.12277	−1.461	0.1551
Mean Dependent Variance	11.48347	S.D.-dependent variance	51.31466
Sum Squared Residual	68198.73	S.E. of regression	49.3525
R-Squared	0.10691	Adjusted R-squared	0.075014
F(1, 28)	2.135555	p-Value (F)	0.155051
Log-Likelihood	−158.5029	Akaike criterion	321.0058
Schwarz Criterion	323.8082	Hannan–Quinn	321.9023

. The constant, the log of market capitalization, the change in promoter holding, and B/P were all found to be insignificant (at a 5% level of significance) as all the p-values failed to fall under the cut-off of 0.05. This was supported by a small value of R-squared, of 0.087. The overall regression was also found to be insignificant. Hence, it can be inferred that none of the three independent variables affected the one-year returns.

• Inference from the Regression:

A multiple regression with two-year percentage returns as the dependent variable and modified percentage promoter holding, log market cap, and modified B/P as the independent variables was run.

Table 10. Model 2.

	Coefficient	Std. Error	t-Ratio	p-Value
Constant	358.226	233.512	1.534	0.1371
Log of Mkt Cap	−12.7601	7.78413	−1.639	0.1132
B/P	−0.591242	2.72092	−0.2173	0.8297
Change in Promoter Holding	−0.433931	0.225097	−1.928	0.0649
Mean Dependent Variance	11.48347	S.D.-dependent variance	51.31466
Sum Squared Residual	61,166.98	S.E. of regression	48.50336
R-Squared	0.198993	Adjusted R-squared	0.106569
F(3, 26)	2.286873	p-Value (F)	0.102203
Log-Likelihood	−156.8706	Akaike criterion	321.7413
Schwarz Criterion	327.3461	Hannan–Quinn	323.5343

Excluding the constant, the p-value was the highest for variable 7 (BP1).

shows that the constant was statistically insignificant (at a 10% level of significance), while the coefficients of modified percentage promoter holding and the log of market capitalization were not found to be significant (at 5% levels of significance). The regression was also not significant, and the R-squared, as well as the adjusted R-squared, were found to be low. The results are given below. The F value was also low, at 2.13. The overall regression was found to be insignificant as the p-value was 0.15. The standard errors of the variables were found to be lesser in magnitude than the coefficients of the constant and the variables, indicating the independence of the variables. This means that multicollinearity did not exist among the independent variables.

• Inference from the Regression:

Another multiple regression with two-year percentage returns as the dependent variable and modified percentage promoter holding and log market cap as the independent variables was run. As can be observed from

Table 11. Model 3.

	Coefficient	Std. Error	t-Ratio	p-Value
Constant	318.676	137.256	2.322	0.028
Change in Promoter Holding	−0.438793	0.221482	−1.981	0.0578
Log of Mkt Cap	−11.5468	5.33381	−2.165	0.0394
Mean Dependent Variance	11.48347	S.D.-dependent variance	51.31466
Sum Squared Residual	61,326.67	S.E. of regression	47.65877
R-Squared	0.196902	Adjusted R-squared	0.137413
F(2, 27)	3.488792	p-Value (F)	0.044907
Log-Likelihood	−156.9097	Akaike criterion	319.8195
Schwarz Criterion	324.0231	Hannan–Quinn	321.1643

, the constant and the coefficient of the log market cap were found to be insignificant, at a 5% level of significance. The coefficient of modified percentage promoter holding was significant, at a 10% level of significance. Again, as in the previous regressions, the R-squared, adjusted R-squared, and F values were found to be small. This was supported by a p-value that was more than 0.05, making the regression insignificant and the effect of the independent variable on the dependent variable low. Only about 10% of the variance in the dependent variable could be explained by the independent variables, making the error variance large and rendering the regression insignificant.

One can make the following observations from the second multiple regressions for the two-year returns. The change in promoter shareholding and the log of market capitalization are negatively related to the two-year stock returns. However, both the independent variables and the constant are statistically significant. The log market cap coefficient is negative, implying a size effect within the specified universe of stocks. The standard errors of the variables are lesser in magnitude than the coefficients of the constant and the variables, indicating the independence of the variables. This means that multicollinearity does not exist among the independent variables.

5. Results

• One-Year Returns:

When the one-year returns were regressed against the modified change in the promoter shareholding variable, the constant and the coefficient of the independent variable were statistically insignificant, at 5% and 10% levels of significance, respectively. When a multiple regression was run with one-year returns as the dependent variable and modified change in promoter shareholding, modified book to price, and log market capitalization as the independent variables, the constant and the coefficients of all the independent variables turned out to be statistically insignificant.

• Two-Year Returns:

When the two-year returns were regressed against the modified change in the promoter shareholding variable, the constant and the coefficient of the independent variable were statistically insignificant, at a 5% level of significance. The coefficient of the independent variables was insignificant, at 5% and 10% levels of significance. That the constant is positive and significant, at an almost 5% level of significance, implies that the constant is possibly substituting for some omitted variables. This is confirmed when we run a multiple regression with two-year returns as the independent variable and modified change in promoter shareholding, modified book to price, and log market capitalization as the independent variables. The constant was statistically insignificant, at a 10% level of significance, while the coefficient of the modified log market cap was insignificant, at 5% and 10% levels of significance. The coefficient of the book-to-market variable was statistically insignificant, at 5% and 10% levels of significance. The coefficients of modified change in promoter shareholding and the log market cap were both negative, and the change in promoter holding was significant, at a 10% level of significance.

So, another multiple regression was run with two-year returns as the dependent variable and modified change in promoter shareholding and log market capitalization as the independent variables. The constant and the coefficient of the log market cap were significant, at a 5% level of significance. The coefficient of modified change in promoter shareholding was significant, at a 10% level of significance. Note that here too, the coefficients of modified change in promoter shareholding and log market cap were both negative.

We could infer the following from the results of the regression analysis.

• There is no significant relationship between one-year returns and an increase in promoter shareholding.
• The change in promoter shareholding is negatively related to the two-year stock returns.
• In the regressions where two-year returns is a dependent variable, the log market cap coefficient is negative, implying a size effect within the specified universe of stocks.
• For two-year returns, the log of market capitalization and modified change in promoter holding both affect the returns. However, the goodness-of-fit measure, i.e., R-squared, is quite low. This means that the model is not a good fit and one may be missing out on many other important variables in the study.

6. Conclusions

6.1. Discussion and Practical Implications

The studies that have been conducted on the implications of change in promoter shareholding so far have not looked at the specific universe that this study focuses on, namely the small-cap value universe. This study comes up with some interesting results for this particular universe.

In our study, within the specified universe, while one-year returns had no relationship with a change in promoter shareholding, two-year returns had a negative relationship with a change in promoter shareholding. This inference may be consistent with the results of some of the research literature discussed earlier in this paper.

• Our results may be consistent with the results of the study by Kumar and Singh (2013), who argue that a minimum threshold promoter holding in a firm is needed for a positive relationship with the firm’s performance, which, in turn, could impact stock price performance. Thus, small-cap-value investors may be advised to avoid stocks where the promoter holding is significantly low.
• Our results could be consistent with the results of the study by Selarka (2006), who observes that firm performance increases with an increase in the promoter shareholding but peaks at a particular point and then dips.
• Another work with which the results of this paper could be consistent is that by Jameson et al. (2014), who state that firms in which promoters have a significant controlling power exhibit poor corporate governance and firm performance. Thus, small-cap-value investors may avoid stocks where promoter control is unusually high.

6.2. Limitations and the Future Scope of Study

An earlier section in this paper led us to one of the limitations of this paper. The relationship between stock returns and a change in promoter shareholding for the small-cap-value universe (or any universe) could depend on the absolute value of promoter shareholding in the firm. The variable absolute value of promoter shareholding has not been studied in this paper. Future studies on this topic could include this variable to obtained deeper insights.

This study only looks at the positive changes in the promoter shareholding within the specified universe but does not look at reductions in the promoter shareholding. Future studies could look at this aspect.

In addition, while this paper confines itself to the small-cap-value universe in the Indian market, future studies could look at different universes across varied geographies.