Target price accuracy of sell-side analysts: evidence from India

Kadam, Suresh; Sethi, Madhvi Article Target price accuracy of sell-side analysts: evidence from India Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Kadam, Suresh; Sethi, Madhvi (2024) : Target price accuracy of sell-side analysts: evidence from India, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-17, https://doi.org/10.1080/23322039.2024.2423261 This Version is available at: https://hdl.handle.net/10419/321657 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Target price accuracy of sell-side analysts: evidence from India Suresh Kadam & Madhvi Sethi To cite this article: Suresh Kadam & Madhvi Sethi (2024) Target price accuracy of sellside analysts: evidence from India, Cogent Economics & Finance, 12:1, 2423261, DOI: 10.1080/23322039.2024.2423261 To link to this article: https://doi.org/10.1080/23322039.2024.2423261 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 20 Nov 2024. Submit your article to this journal Article views: 560 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | RESEARCH ARTICLE Target price accuracy of sell-side analysts: evidence from India Suresh Kadam a,b and Madhvi Sethi a a Symbiosis Institute of Business Management (SIBM), Symbiosis International (Deemed University) (SIU), Bengaluru, Karnataka, India; b School of Commerce & Management, D. Y. Patil International University (DYPIU), Pune, Maharashtra, India ABSTRACT Target prices forecasted by sell-side equity research analysts play a crucial role in market participants’investment decisions. We, using a large sample for Indian markets, determine during the period and end of the period 12-month ahead target price achievements, examine the effectiveness of valuation methods for determining target prices, evaluate target price accuracy using prediction error metrics, and investigate the factors influencing target price accuracy. Our findings indicate that sell-side analysts have reasonable forecasting abilities, achieving 63% of their target prices over a 12-month forecasting horizon. The level of achievement decreased with increasing optimism in predictions. Analysts generally prefer holistic and multiple-based valuation approaches to determine the target prices. The DCF methodology was less effective than the SOTP hybrid and multiple-based approaches in predicting target prices. We find that more optimistic target prices and higher beta contribute to increased prediction errors, whereas better market returns reduce errors. Analysts struggle to predict prices for loss-making enterprises, and have difficulty forecasting target prices in capital-intensive sectors. These findings contribute to the existing body of knowledge and have significant implications for stakeholders in financial markets. IMPACT STATEMENT The study examines the accuracy of analysts’target price forecasts and the factors influencing that accuracy. We find that analysts demonstrate reasonable forecasting abilities, with 63% of their target prices being accurate within a 12-month period. Our results suggest that analysts and brokerage firms should utilize more rigorous valuation models, such as the Sum of the Parts (SOTP) Hybrid, when setting target prices, whenever relevant. Investors should be aware of the limitations linked to analysts’stock price forecasts, particularly in capital-intensive industries, and should be cautious when considering overly optimistic target price recommendations, especially for small-cap or loss-making companies. Our insights into the impact of valuation methods and capital intensity on target price accuracy contribute new knowledge to the existing literature. ARTICLE HISTORY Received 16 July 2024 Revised 14 October 2024 Accepted 25 October 2024 KEYWORDS Target prices; analyst recommendation; equity research; valuation methods; analyst reports; target price accuracy SUBJECTS Finance; Business; Management and Accounting; Economics 1. Introduction Analysts play an important role in disseminating valuable information to the market participants (B. Barber et al., 2001). A typical sell-side analyst equity research report includes an earnings forecast, recommendation (for example buy, hold, sell), and target price. Previous research has found that these recommendations are important for share price discovery (Asquith et al., 2005). Given that the total market capitalization of domestic companies listed on stock exchanges worldwide is estimated at 112 trillion USD as of July 2023, 1 equity research firms spend billions of dollars annually analyzing companies and publishing research reports for investors. Over the years, academic research has been devoted to analyzing the value, impact, and accuracy of analysts’reports. One valuable measure that emerged from these reports was the target price. The target represents the potential change in security value and may have an impact on investors’investment decisions. CONTACT Madhvi Sethi [email protected] Symbiosis Institute of Business Management, Symbiosis International (Deemed University) (SIU), Electronics City, Hosur Road, Bengaluru - 560100, Karnataka, India ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2423261 https://doi.org/10.1080/23322039.2024.2423261 Analysts often provide target prices to support their recommendations (Bradshaw, 2002) and are correlated with value-relevant fundamentals, such as earnings expectations (Frankel & Lee, 1998). Target prices significantly affect market prices (Brav & Lehavy, 2003) and investors consider the target price forecast to be valuable (Asquith et al., 2005). Academic research was largely silent on target prices, a fact mainly attributed to the late coverage of target prices by major databases (Kerl, 2011). Previous studies of analyst reports have focused on the impact of recommendations and earnings forecast on stock prices and the accuracy of earnings forecasts, along with studies determining the factors for such an impact. Analyst recommendations were found to generate abnormal returns and outperform benchmarks (B. M. Barber & Loeffler, 1993; Beneish, 1991; Desai et al., 2000; Womack, 1996). Analyst coverage promotes corporate innovation (Zhang & Wang, 2023). Recommendations are expected to have a greater price impact if they are accompanied by long-term earnings growth forecasts (Jung et al., 2012; Stickel, 1995). Better forecast accuracy resulted in more profitable recommendations (Hall & Tacon, 2010). Largesample studies across the US (Asquith et al., 2005; Bradshaw, Brown, et al., 2013), German (Kerl, 2011) and Italian (Bonini et al., 2010) markets have documented various levels of predictive ability of analysts and factors impacting analyst accuracy. This study aims to expand and contribute to the existing research on the accuracy of target price achievement by sell-side equity research analysts. Therefore, our study addresses the following research questions: (i) Do sell-side analysts have superior ability to predict target prices? (ii) Effectiveness of the choice of valuation method used by analysts in predicting target prices, (iii) impact of recommendation classes on target price accuracy, and (iv) determinants of target price accuracy. Our sample consists of 22,807 analyst recommendations for the period of five years from 1 January 2016 to 31 December 2020, from 34 equity research firms for 805 companies, covering about 95% of the total market cap of all listed companies in India. All reports were individually analyzed to capture the necessary data points required for empirical analysis. Very few studies have been conducted on analysts’recommendations in the Indian market, and those that exist have used small and restrictive samples (Chatterjee et al., 2020; Patel, 2021; Sayed, 2015; Sayed & Chaklader, 2014). Our research expands the existing literature on the analysts target prices for Indian stock market. Our empirical analysis was conducted in five stages: First, we determine the target price achievements for all recommendations and further analyze each recommendation class (ie strong buy, buy, hold, sell, strong sell’) at the end of the 12-month period from the recommendation date and any time during the 12-month period. We also observe the cross-sectional achievement of the target price along the market cap of companies (grouped as large-cap, mid-cap, and small-cap) and sectors. Second, we study the valuation methods analysts use to arrive at the target price and categorize them. We study the effectiveness of various valuation methods for predicting target prices. Third, we use the accuracy metric model developed by Bonini et al. (2010), from the perspective of an investor, to test target price accuracy. Fourth, we studied the impact of recommendation class on accuracy. Fifth, we analyze the factors that determine the target price accuracy. Our findings demonstrate that the achievement of the target price surpasses the majority of previously documented results in the literature (Asquith et al., 2005;Boninietal.,2010;Bradshaw& Brown, 2006;Kerl,2011) and is in line with Bradshaw, Brown, et al. (2013) with 63% achievement during the 12-month period and 38% at the end of the 12-month period, indicating reasonable predictive abilities of analysts. Achievement decreases with optimistic predictions (‘strong buy’,‘strong sell’). Analysts prefer holistic and multiple-based valuation approaches to arrive at the target prices. The DCF methodology has limited effectiveness, whereas the sum of the parts (SOTP) hybrid and multiples-based approaches to valuation are highly effective in predicting target prices. The recommendation classes significantly impact target price accuracy. Ex-post market returns, firm beta, DCF valuation methodology, and business capital intensity contribute to prediction errors. Our findings on the effects of valuation methods and capital intensity on target price accuracy are a new addition to the literature. The rest of the paper is organized as follows: Section 2 reviews prior research; Section 3 outlines the hypotheses; Section 4 covers data collection; Section 5 details the methodology; Section 6 presents the results; and Section 7 concludes the paper. 2 S. KADAM AND M. SETHI 2. Literature review 2.1. Market impact of analyst recommendations Analysts play a vital role in disseminating information to financial markets in the form of earnings forecasts, recommendations (such as buy, hold, and sell), and target prices. Earlier research focused mainly on earnings forecasts and stock recommendations. Cowles (1933) noted that stock market forecasters fail to generate abnormal returns. However, abnormal returns were documented by Beneish (1991) and B. M. Barber and Loeffler (1993). Womack (1996) noted that post-recommendation excess returns are not mean reverting. Desai et al. (2000) also observed that stocks recommended by All Star analysts in the Wall Street Journal outperformed benchmarks controlled for size and industry. Several studies (Jegadeesh & Kim, 2006; Moshirian et al., 2009; Womack, 1996) have noted that analysts publish more buy recommendations than sell recommendations. Stickel (1995) found that downgrades have a greater negative impact than upgrades. Bradley et al. (2014) noted that the market reaction to the contrarian (recommendation in opposite direction to recent price movement) upgrades and downgrades is more than non-contrarian, concluding that contrarian upgrades are expected to have private information. Analyst recommendation changes are more likely to be influential if they are from leaders, stars, previously influential analysts, issued away from consensus, accompanied by earnings forecasts, and issued on growth, small, high institutional ownership, or high forecast dispersion firms (Loh & Stulz, 2011). Contrary to what has been observed in other studies, B. Barber et al. (2003) point out that from 2000 to 2001, stock recommendations from analysts performed worse than stocks least favored by analysts. Loh and Stulz (2011) studied whether individual recommendations are influential and documented that only 12% of the recommendation changes are influential. Michaely and Womack (1999) observed that underwriters’recommendations are biased and, in the long run, inferior to those of non-underwriters. Jegadeesh and Kim (2010) noticed that analysts herd around this consensus. Chan et al. (2018) noticed that about 56% of analysts terminated their ownership of the stock while having outstanding buy recommendations. 2.2. Target price Although target price is one of the key components of analyst research output, the focus has recently shifted toward target prices. Kerl (2011), based on the information taken from Brav and Lehavy (2003) and other studies (Asquith et al., 2005; Bradshaw, Brown, et al., 2013; Gleason et al., 2013), attributed this late interest in target prices to the fact that major databases such as First Call from Thomson Financial began coverage of target prices only at the end of 1996. Brav and Lehavy (2003) found that target prices significantly affect market prices and concluded that target price information to market participants is incrementally informative beyond earnings forecasts and stock recommendations. According to Bradshaw (2002), analysts use target price justifications in a majority of their reports. Additionally, higher target prices are linked to more favorable recommendations. Asquith et al. (2005) performed a detailed study of target prices. The authors analyzed the achievement of target prices during the 12 months post-recommendation and noted that price forecasts were achieved in 54.28% of all cases. They documented overshooting of achieved target prices by 37.27% and undershooting in the case of unachieved target prices by 15.62%. Asquith et al. (2005) noted that the target prices provide valuable information to the market. Kerl and Walter (2008) provides similar evidence for the German market. Bonini et al. (2010) developed an accuracy metric for target price accuracy and tested it in the Italian market. They noted 20.0% (end of period) and 33.12% (any time during the period) accuracy for target prices with prediction errors of up to 36% and concluded that forecasting accuracy of analysts is very limited and target prices are systematically biased. Kerl (2011) noted a target price accuracy of 56.53% during this period and found a negative correlation between accuracy and analyst optimism in the German market. Bradshaw, Brown, et al. (2013) examined overall and individual analyst accuracy and noted that, on average, 38% (end of period) and 64% (any time during the period) of analysts’target prices are met. They concluded that the frequency of accurate prediction is low, with an absolute target price forecast error of 45%, and attributed the lack of accuracy to the fact that target price forecasting was mostly an COGENT ECONOMICS & FINANCE 3 unmonitored activity. In their working paper (Bradshaw & Brown, 2006) for the sample period from 1997 to 2002, the accuracy noted was 24% (end of the period) and 45% (any time during the period). Gleason et al. (2013) studied analysts’valuation techniques for setting target prices and noted that target price accuracy improves with more rigorous valuation techniques than with simple heuristics. In a similar study on valuation techniques, Erkilet et al. (2022) note that the income and market approach leads to more accurate prices along with holistic valuation, instead of the sum of parts valuation. Bonini et al. (2022) observed that target price forecasts that differ from those of the multiple-based valuation approach are more accurate. Bradshaw et al. (2019) argued that analysts in countries with strong institutional frameworks provide more value-relevant target prices. Ant^ onio et al. (2017) also showed that accuracy increases with greater government effectiveness and greater analyst consensus in a Latin American study. Bouteska and Mili (2022) observed that analysts issue more accurate recommendations for well-governed firms and that strong corporate governance improves target price accuracy (Cheng et al., 2019). Umar et al. (2022) noted that ESG scores positively impacted target price accuracy. Umar et al. (2023) observed that financial restatements negatively impacted target price precision. Fredj and Gana (2023) observed that target price accuracy is negatively related to board independence. Institutions closely follow analyst recommendations and tend to overreact to analyst target price revisions, thereby destabilizing stock prices due to herding behavior (Gu et al., 2022). In the Indian market context, Sayed and Chaklader (2014) analyzed 1000 target prices with buy ratings and documented a target price accuracy of 57.6%. Sayed (2015) analyzed 340 research reports for Nifty 50 index firms and studied the valuation models used by analysts for ‘buy’recommendations and observed the highest target price accuracy of 70% with the DCF model and the lowest TPA of 51.1% with book value-based forecasts. Chatterjee et al. (2020) analyzed the target price accuracy for analyst calls for a specific window (30 days for technical calls and 180 days for fundamental calls) and noted that 43% of technical calls and 52% of fundamental calls met the target prices. Patel (2021) analyzed Banking Stocks listed on the National Stock Exchange and noted that promoter holdings have a significant negative association with target price accuracy. Our literature review uncovers the following gaps (a) the sector-based cross-sectional analysis of the target price achievements of the analyst recommendations is not effectively evaluated, (b) the impact of valuation methods on the accuracy of analyst stock recommendations is studied with limited classification of valuation models or for a restricted sample of large capitalization index stocks, (c) the impact of business capital intensity as measured by the capital-intensive or labor-intensive nature of the business on analyst stock recommendation accuracy is not explored and (d) the studies on analyst target price accuracy in the Indian context use a restricted sample (only buy recommendations and majorly for large listed firms), and results are thus not comparable with similar studies in other economies. Our study aims to fill in the research gaps and lays out the following objectives. 1. To study whether the sell-side analysts have superior target price predicting abilities. 2. To study the effectiveness of valuation methods analysts use in predicting target prices. 3. To study the impact of recommendation classes on target price accuracy. 4. To study the determinants of the target price accuracy. 3. Hypotheses Based on our research objectives, we test the following hypotheses. H1: Sell-side analysts have superior target price predicting abilities, and recommendation class, firm size, and sector have an impact on the target price achievements of analysts’recommendations. H2: Valuation methods used by sell-side analysts to arrive at a target price impact the target price achievements of the analyst. H3: Recommendation classes (strong buy, buy, sell, strong sell) have an impact on the target price accuracy of the analyst recommendations. H4: Valuation methods, business capital intensity (capital/labor), firm profitability, analyst optimism, firm volatility, and post-market returns have an impact on the target price accuracy of the analyst recommendations. 4 S. KADAM AND M. SETHI 4. Data We collected 28,486 sell-side analysts’recommendations from the database of ET Intelligence, the research arm of The Economic Times, India’s largest business paper, and part of The Times Group, India’s largest media group. The recommendations are for a period of five years, from 1 January 2016 to 31 December 2020. All the reports are manually analyzed to extract necessary information such as company name, date of report, recommendation, target price, and valuation methods. The reports are further filtered by removing those reports that are (a) without any specific recommendation such as buy, sell, etc.; (b) without any specific target price; (c) where there is a stock split or bonus issue within the recommendation period of 12 months from the date of publication of the report; (d) where the report is of REIT or INVIT; (e) where the valuation method is not specified; and (f) where the recommendations are before the listing of the stocks on the stock market. Finally, we analyzed 22,807 recommendations by 34 research firms for 805 companies, which covers about 95% of the total market cap of all listed companies in India. Analyst recommendations are classified in a stepwise manner. Initially, recommendations were classified according to the ranking adopted by the research firm. As each research firm has its own scale for ranking the recommendations, we reclassified recommendations on a standard five-point scale of ‘strong buy/buy/hold/sell/strong sell’. This is consistent with the classifications in the literature reviewed, and our sample can be compared cross-sectionally with other studies. The process followed for conversion of recommendations to standard five-point scale is (a) if the original scale of the research firm is a fivepoint scale having central recommendation as ‘hold’or ‘neutral’then we have converted the original recommendation directly to the standard five-point scale adopted by us; (b) if the original scale of the research firm is a three-point scale then the central recommendation is classified as ‘hold’, a buy recommendation with an implicit return above 20% is classified as ‘strong buy’and balance as ‘buy’, a sell recommendation with an implicit fall of larger than 20% are classified as ‘strong sell’and balance as ‘sell’. The breakdown of the data is presented in Table 1. Panel A presents the month-wise breakup of the recommendations for the five-year period. The recommended data were fairly distributed across years. Higher recommendations are observed in January, May, July, October, and November, consistent with the hypothesis that analysts update their recommendations on the availability of new information through quarterly results, shareholder meetings, and so on. Panel B presents the distribution of the data according to the recommendation class. It can be observed that ‘strong buy’and ‘buy’recommendations exceed ‘hold,’‘sell’and ‘strong sell’recommendations across the years. This is consistent with the hypothesis that analysts tend to provide more buy recommendations than sell recommendations (Jegadeesh & Kim, 2006; Moshirian et al., 2009; Womack, 1996). Panel C represents the distribution of data according to the 11 sector classifications. The highest recommendations were observed in the consumer discretionary (23.3%) sector, followed by financial services (14.8%). It can be seen that all sectors are well represented in the sample. The data cover 805 companies, and the highest coverage for a company (IndusInd Bank Ltd.) is 189. The average and median coverage per company are 28.30 and 13%, respectively. Of the 34 research firms, the highest recommendation from a firm (Edelweiss Securities Ltd.) was 3283. The average and median recommendations for research firms were 670 and 210, respectively. We review each report to determine the valuation method used to establish the target prices. Segregation of the valuation methods is performed, as shown in Figure 1. The first broad category is the holistic approach to the sum of the parts (SOTP) approach (Erkilet et al., 2022). Holistic valuation is further categorized into a multiples-based approach, a discounted cash flow approach, and others. Multiples have subcategories such as income, earnings, sector multiples (P/E, EV/EBITDA, Relative PE, PEG, etc.), and book value multiples (P/B). SOTP is categorized into homogeneous (using the same approach across parts) or hybrid (using different valuation approaches for different parts). Table 2 presents the various approaches used by the analysts for their recommendations. Analysts preferred a holistic approach (83%) to the sum of the parts (17%). DCF was used in only 4% of recommendations. The multiplet-based approach is the most preferred approach for analysts to arrive at target prices. Overall, 93% of the recommendations used a multiple approach (78% under holistic and 15% under homogeneous SOTP). These observations are consistent with those of previous studies. COGENT ECONOMICS & FINANCE 5 5. Methodology, tools and techniques Asquith et al. (2005) introduced the first measure of target price achievement where they devised a simple metric as the target price prediction should be considered ‘achieved’if the stock price of the analyzed company equals or exceed (falls below, in case of sell recommendations) the target price at any time during the 12-month period from the release of the recommendation. Bradshaw, Brown, et al. (2013) divided the 12 months prediction horizon into two segments, namely, during the period and the end of the period, and measured the target price achievements for the two segments separately. Bonini et al. (2010) developed a measure to test for inaccuracy from an investor perspective. They developed two metrics to define the ideal strategy that an investor can adopt during the prediction horizon and a feasible strategy by focusing only on the end of the prediction horizon along with the prediction errors. Kerl (2011) improved on Bonini et al. (2010) measures by focusing on precise accuracy and considering the absolute values for errors over and under achieving the target price. Kerl (2011) argued that accuracy may be determined by the capacity of an analyst to predict exact prices. Bradshaw, Brown, et al. (2013) added ex ante and ex post optimism measures to determine the level of optimism and accuracy. Ant^ onio et al. (2017) derived the target price accuracy of analyst consensus estimates and hypothesized a relationship between the standard deviation of consensus estimates and prediction error. Patel (2021) models for target price accuracy beyond a one-year horizon. Table 1. Details of analyst recommendations. Panel A a Month 2016 2017 2018 2019 2020 Total Jan 497 (11.3%) 381 (8.5%) 343 (7.6%) 423 (8.5%) 535 (11.9%) 2179 (9.6%) Feb 541 (12.3%) 676 (15.2%) 509 (11.3%) 405 (8.2%) 667 (14.8%) 2798 (12.3%) Mar 141 (3.2%) 124 (2.8%) 102 (2.3%) 167 (3.4%) 98 (2.2%) 632 (2.8%) Apr 327 (7.4%) 265 (5.9%) 283 (6.3%) 296 (6%) 201 (4.5%) 1372 (6%) May 585 (13.3%) 719 (16.1%) 831 (18.5%) 665 (13.4%) 400 (8.9%) 3200 (14%) Jun 200 (4.5%) 194 (4.4%) 204 (4.5%) 216 (4.4%) 639 (14.2%) 1453 (6.4%) Jul 358 (8.1%) 493 (11.1%) 481 (10.7%) 563 (11.3%) 522 (11.6%) 2417 (10.6%) Aug 509 (11.6%) 606 (13.6%) 532 (11.9%) 754 (15.2%) 554 (12.3%) 2955 (13%) Sep 217 (4.9%) 181 (4.1%) 144 (3.2%) 166 (3.3%) 200 (4.4%) 908 (4%) Oct 447 (10.2%) 226 (5.1%) 485 (10.8%) 534 (10.8%) 268 (6%) 1960 (8.6%) Nov 417 (9.5%) 444 (10%) 447 (10%) 629 (12.7%) 294 (6.5%) 2231 (9.8%) Dec 159 (3.6%) 148 (3.3%) 128 (2.9%) 144 (2.9%) 123 (2.7%) 702 (3.1%) Total 4398 (100%) 4457 (100%) 4489 (100%) 4962 (100%) 4501 (100%) 22,807 (100%) Panel B b Recommendation 2016 2017 2018 2019 2020 Total Strong buy 1469 (33.4%) 1292 (29%) 2072 (46.2%) 1885 (38%) 1540 (34.2%) 8258 (36.2%) Buy 1597 (36.3%) 1792 (40.2%) 1378 (30.7%) 1377 (27.8%) 1566 (34.8%) 7710 (33.8%) Hold 966 (22%) 1094 (24.5%) 852 (19%) 1298 (26.2%) 1046 (23.2%) 5256 (23%) Sell 280 (6.4%) 209 (4.7%) 144 (3.2%) 335 (6.8%) 269 (6%) 1237 (5.4%) Strong sell 86 (2%) 70 (1.6%) 43 (1%) 67 (1.4%) 80 (1.8%) 346 (1.5%) Total 4398 (100%) 4457 (100%) 4489 (100%) 4962 (100%) 4501 (100%) 22,807 (100%) Panel C c Sector 2016 2017 2018 2019 2020 Total Commodities 554 (12.6%) 651 (14.6%) 568 (12.7%) 636 (12.8%) 656 (14.6%) 3065 (13.4%) Consumer discretionary 1012 (23%) 1047 (23.5%) 1106 (24.6%) 1151 (23.2%) 996 (22.1%) 5312 (23.3%) Energy 167 (3.8%) 156 (3.5%) 217 (4.8%) 325 (6.5%) 328 (7.3%) 1193 (5.2%) Fast moving consumer goods 297 (6.8%) 280 (6.3%) 273 (6.1%) 337 (6.8%) 348 (7.7%) 1535 (6.7%) Financial services 595 (13.5%) 593 (13.3%) 699 (15.6%) 727 (14.7%) 771 (17.1%) 3385 (14.8%) Healthcare 422 (9.6%) 482 (10.8%) 441 (9.8%) 424 (8.5%) 359 (8%) 2128 (9.3%) Industrials 601 (13.7%) 648 (14.5%) 685 (15.3%) 694 (14%) 525 (11.7%) 3153 (13.8%) Information technology 423 (9.6%) 348 (7.8%) 280 (6.2%) 390 (7.9%) 324 (7.2%) 1765 (7.7%) Services 110 (2.5%) 84 (1.9%) 92 (2%) 132 (2.7%) 95 (2.1%) 513 (2.2%) Telecommunication 96 (2.2%) 71 (1.6%) 70 (1.6%) 68 (1.4%) 45 (1%) 350 (1.5%) Utilities 121 (2.8%) 97 (2.2%) 58 (1.3%) 78 (1.6%) 54 (1.2%) 408 (1.8%) Total 4398 (100%) 4457 (100%) 4489 (100%) 4962 (100%) 4501 (100%) 22,807 (100%) a Panel A presents the month-wise breakup of the recommendations for five years of data. b Panel B presents the breakup of the recommendations as per the recommendation class, namely, strong buy, buy, hold, sell and strong sell. c Panel C presents the breakup of recommendations per sector classification. These classifications are based on the classification of stocks provided by BSE Limited, one of the oldest and largest stock exchanges in India. 6 S. KADAM AND M. SETHI 5.1. Target price achievement To determine the target price achievement, we adopted the methodology in Bradshaw, Brown, et al. (2013). First, for each recommendation, we determine the target price achievement by computing ‘TPMetEnd’and ‘TPMetDuring’. Where, TPMetEnd ¼1 if the underlying share price reaches or exceeds (falls below in the case of sell recommendations) the target price at the end of the year from the recommendation date. TPMetDuring ¼1 if the underlying share price reaches or exceeds (falls below, in case of sell recommendations) the target price during or at the end of the year from the recommendation date. Further, the cross-section of the recommendations is analyzed across recommendation classes (Strong Buy, Buy, Hold, Sell, Strong Sell), the market capitalization of the firm (Large-cap, Mid-Cap, Small-Cap), the sector of the firm, and the year of recommendation. Figure 1. Classification of valuation methodologies. Table 2. Analyst recommendations as per valuation methodologies. Methodology Total % Holistic valuation 18,852 83% Multiples 17,854 78% Income, earnings & sector multiples 15,219 67% Book multiples 2635 12% DCF 829 4% Others 169 1% Sum of the parts valuation 3955 17% Homogeneous a 3342 15% Hybrid 613 2% Total 22,807 100% a Homogenous in our dataset using multiples approaches for different parts. COGENT ECONOMICS & FINANCE 7 Our findings for analyst optimism, firm volatility, and post-market returns are negatively related to target price accuracy, which matches the findings of Bonini et al. (2010) and Kerl (2011). Our EPS findings are consistent with those of previous studies of Bonini et al. (2010). Our findings on the effects of valuation methods and capital intensity on target price accuracy are a new addition to the literature. 7. Conclusion We analyzed a large sample of 22,807 recommendations over a five-year period from 2016 to 2020 for the Indian Stock Market, covering 805 companies. We observed higher recommendations after the availability of new information through quarterly results, shareholder meetings, and so on. These recommendations were particularly high in January, May, July, October, and November. ‘Strong Buy’and ‘Buy’ recommendations significantly exceeded sell recommendations. In our analysis, i. We observed that the target price was achieved by 38% at the end of the 12-month period and 63% at any time during the 12-month period after the recommendation. According to our literature review, 63% achievement is the highest recorded across similar studies in the US, German, and Italian markets, suggesting that analysts demonstrate reasonable predictive ability in the Indian market. Target price achievement is slightly lower for small-cap stocks, which can be attributed to their high volatility. We found the highest target price achievement for the FMCG and IT sectors and the lowest achievement for the utilities and telecommunications sectors, indicating analysts’ limited predictive abilities in capital-intensive sectors. ii. We document the limited effectiveness of the DCF methodology and the high effectiveness of the SOTP hybrid and multiple-based approach to valuation in predicting target prices. Our findings suggest that both the holistic and SOTP methods are at par. Analysts prefer holistic and multiple-based valuation approaches to arrive at the target prices. We find merit in the use of more rigorous valuation techniques, such as the SOTP Hybrid approach, to improve accuracy. iii. Target price accuracy was tested using a model developed by Bonini et al. (2010). The prediction errors for the ideal return strategy are significant and negative, indicating conservative estimates by analysts for the time horizon; here, our results differ from Bonini et al. (2010). For a feasible return strategy, the prediction errors were large, positive, and significant, indicating overshooting. Our results indicate that stock prices move in the direction of recommendation, primarily meet the target prices, Table 7. Determinants of target price accuracy. a IS_PE_M FS_PE_M Coefficient T-stat Coefficient T-stat Intercept 0.0101 1.47 0.3338 3.33 IR 0.2947 84.14 0.8930 17.45 MKT_CAP 0.0000 0.18 −0.0105 −2.59 MKTRTN_PAST 0.01091.68 −0.1934 −2.04 PB_RATIO 0.0001 0.55 −0.0052 −3.44 FIRM_BETA 0.0069 5.54 0.1364 7.52 MKTRTN_POST −0.1290 −28.88 −1.4008 −21.46 EPS −0.0000 −3.91 −0.0004 −2.80 VAL_MULT 0.0018 0.24 0.0740 0.68 VAL_DCF 0.0239 2.92 0.1911 1.60 VAL_SOTP 0.0000 0.00 0.0385 0.35 CAP_INT 0.0092 2.59 0.1412 2.73 LAB_INT 0.0009 0.27 0.1927 3.80 Adj R 2 0.284 0.044 F-statistic 821.5 95.45 Observations 22,807 22,807 Note: Significance at the 10%,5% and 1% levels are denoted by ,, and , respectively. a We have also performed a robustness check to determine the significance of the determinants of the factors affecting accuracy. The robustness check is performed by modifying the dependent variables of our determinants of target price accuracy ‘IS_PE_M’and ‘FS_PE_M’to ‘0’if the target prices are achieved and ‘1’if the target prices are not achieved under the Ideal and Feasible strategy. We find similar significance of variables and signs of the coefficients for the significant variables, which assures the robustness of our results. 14 S. KADAM AND M. SETHI and consolidate/mean revert during the 12-month horizon. We document the significant impact of recommendation classes on target price accuracy, especially, although analysts give a very low number of ‘strong sell’and ‘sell’signals, they tend to overshoot when they recommend a sell. iv. We found that analyst optimism, represented by implicit returns in target prices, negatively impacts target price accuracy. ‘Strong sell’and ‘sell’recommendations are low in number but overshoot. Ex-post market returns positively impact accuracy, owing to more buy recommendations than sell recommendations. A higher firm beta contributes to prediction errors, indicating analysts’limited ability to model volatility. The significant negative coefficients for EPS underscore analysts’difficulty in predicting prices for loss-making enterprises. The use of the DCF methodology was observed to add to the prediction errors. The capital-intensive nature of a firm adds to the prediction error, indicating the limited ability of analysts to forecast the benefits of capex and its impact on target prices. Our research indicates that analysts have reasonable predictive abilities, and over-optimism reduces the prediction accuracy. Our findings on valuation methods and capital intensity impacting target price accuracy further add to the existing literature. The study offers practical insights for both investors and policymakers. Investors should recognize the limitations of analysts’stock price forecasts, particularly in capital-intensive industries, and exercise caution when evaluating highly optimistic target price recommendations, especially for small-cap or loss-making companies. Analysts and brokerage firms are encouraged to adopt more rigorous valuation models, such as the SOTP Hybrid, wherever applicable, when setting target prices. From a policy perspective, there is a need to develop regulations and disclosure requirements to better protect investors by ensuring transparency from analysts and brokerage firms. We have analyzed sector capital intensity impact on the accuracy of target prices, future research could investigate the influence of analyst characteristics, brokerage firm attributes, and additional sectorspecific factors on the accuracy of target price predictions. Notes 1. See https://www.statista.com/statistics/274490/global-value-of-share-holdings-since-2000/. 2. SEBI has, vide its circular no. SEBI/HO/IMD/DF3/CIR/P/2017/114 dated 6th October 2017, defined large cap, midcap and small-cap companies. Author contributions Suresh Kadam: conceptualization; data curation; methodology; formal analysis; writing–original draft; writing – review & editing (equal). Madhvi Sethi: supervision; validation; writing –review & editing (equal). Disclosure statement No potential conflict of interest was reported by the author(s). Funding No funding was received. About the authors Suresh Kadam is an Assistant Professor at DY Patil International University. He completed his MBA at IIT Kanpur and has industry experience in investment banking. His research interests include financial markets, corporate finance, and FinTech. Currently, he is also a research scholar at Symbiosis International University. Dr. Madhvi Sethi is the Professor and Director at SIBM, Bengaluru. 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