Strong Buy Research Articles

The Causality between Analysts’ Recommendations and Corporate M&As

Abstract This study examines the role of analysts' recommendations in mergers and acquisitions (M&As), focusing on their impact on payment methods and acquirers' long-term performance. The findings reveal that acquirers with strong buy or buy recommendations are more likely to use 100% stock payment, consistent with the overvaluation hypothesis. Conversely, those with strong sell or sell recommendations tend to prefer cash payment. Notably, acquirers with higher recommendation scores exhibit better long-term market performance. This finding suggests that analysts' recommendations before M&A announcements do not fully incorporate the deal's potential impact on long-term value creation. Moreover, acquirers with buy recommendations experience significantly lower long-term returns, highlighting the disconnect between analysts' recommendations and long-term performance. These findings contribute to understanding the information content and limitations of analysts' recommendations in the M&A context. JEL classification numbers: G34, G24, G14. Keywords: Mergers and acquisitions, Analysts' recommendations, Payment methods, Information quality, Overvaluation hypothesis.

NEWS BASED SENTIMENT ANALYSIS USING TANH GREY WOLF OPTIMIZER (TGWO) FOR STOCK PRICE PREDICTION

Research on stock market forecasting has always been important to the financial sector. Shares price forecasting plays a significant role in raising investors' interest in an organization, which has a beneficial effect on the growth of shareholders in its stock. A substantial reward might have been available if the stock price had been correctly predicted. Due to advancements in science, technology, and the market economy, more elements today affect a company's price trend compared to the past. The traditional analytical technique cannot explain the fluctuations in stock price caused by important information hidden from view. Predicting these stock markets using confidential information can be achieved by several deep-learning time series models based on RNN and its derivatives, such as LSTM and GRU. However, their performance still needs to be improved. The use of optimization techniques like GWO, has improved the accuracy of these models, and to increase the reliability of the prediction, news sentiment has been used. The study comprised three phases. In the first phase, this study proposed a Tanh Grey Wolf Optimizer algorithm to increase the efficiency & accuracy of the model. Tanh is used to reduce the infinite search space to -1 and 1. Sigmoid and tanh are similar functions, but tanh has a more extensive range and is more symmetrical around the origin, resulting in results that are not biased. This property allows the potential for Tanh to have a superior gradient, which leads to more accuracy compared to the present basic GWO and binary GWO, which use sigmoid. In the second phase, News sentiment analysis used word embeddings to increase the predictions' reliability. In the last phase, these predictions are ensemble with news sentiment scores and classify the score into five classes, i.e., Strong Sell, Sell, Hold, Strong Buy, and Buy.

Spatial federated learning approach for the sentiment analysis of stock news stored on blockchain

Sentiment analysis can be a useful tool in predicting stock market trends, as it allows us to gauge the overall sentiment towards a particular stock or company. When combined with news stored in a blockchain, sentiment analysis can provide a more accurate and trustworthy representation of the market. The aim of this paper is to study the spatial news and external events which disrupt the stock market movement as well as news analytics techniques to understand the impact of news by spatial sentiments on stock market movement. For the prediction of stock market trading decisions, a novel ensemble technique consisting of spatial federation of deep learning algorithms, machine learning algorithms and dictionary based approach is proposed to maintain privacy and geographic diversity. These learning techniques are federated into five groups and these groups are ranked as per the prediction accuracy of these models. A bit 1 or 0 is assigned for each federation thus creating a 5 bit pattern which can be used to predict stock market trading decision as strong buy, buy, hold, strong sell, sell.

The Bank of Japan's equity purchases and stock illiquidity

Using the large-scale index-linked exchange-traded fund (ETF) purchase program of the Bank of Japan (BOJ), we examine the role of unconventional equity-based monetary policies in the market liquidity of the underlying securities. Using a large sample of Japanese stocks, we document a significant increase in stock illiquidity when a firm's ownership by the BOJ increases. Intensified ETF arbitrage activities partially mediate such effect. The increased illiquidity is concentrated among small and young firms and those whose shares are likely subject to strong buying pressure. Finally, BOJ ownership increases comovement in liquidity and reduces informational efficiency.

Factor Investing with Classification-Based Supervised Machine Learning

There are two types of supervised machine learning (SML): regression and classification. In this study, the authors propose classification-based machine learning algorithms for factor investing with artificial neural networks in which the cross section of stock returns is grouped into five categories: strong buy, buy, neutral, sell, and strong sell. Their empirical out-of-sample results demonstrate some advantages of classification-based machine learning relative to regression-based learning in which the actual stock returns denote the response variable. The classification-based models also deliver slight outperformance relative to the ordinary least squares model, although the outperformance is not statistically significant. Furthermore, the out-of-sample results show that “deep” learning with multilayers of neuron layers cannot outperform a less sophisticated “shallow” learning for both classification-based and regression-based SML algorithms. Their findings suggest that market noise, common in the financial markets, during the training process overwhelms the nonlinear association uncovered in the machine learning process; and the classification of the cross section of stock returns may have reduced some of the noise.

Whose money is smart? Individual and institutional investors’ trades based on analyst recommendations

This study explores how institutional and individual investors respond to analyst recommendations. Using a unique account-level trading dataset taken from the Shanghai Stock Exchange, we obtain direct evidence to show that (1) active institutional investors are significantly net buyers (net sellers) on “strong buy” and “buy” (“hold” and “sell”) recommendations; (2) active institutional investors condition their trades based on the buy-side pressure of analysts; (3) institutional investors earn abnormal returns by incorporating analysts’ buy-side pressure into their trading reactions to analyst recommendations; and (4) individual investors, in contrast, exhibit abnormal trade reactions opposite to those of active institutional investors. Our results are robust to alternative measures and different specifications. This study provides evidence that active institutional investors are more sophisticated processors of information and provides support for regulators’ concerns about the sub-optimal investment decisions made by individual investors who are unaware of the potential conflicts of interest analysts may face. • We explore how institutions and individuals respond to analyst recommendations. • Active institutions condition their trades based on the buy-side pressure of analysts. • Active institutions earn returns by incorporating analysts’ buy-side pressure. • Individuals exhibit abnormal trade reactions opposite to active institutions.

Do Analysts Cater to Investor Beliefs? Evidence from Market Liberalization in China

We examine whether financial analysts cater to investors’ beliefs, using the market liberalization (Stock Connect) programs in China as a shock to investor beliefs. We find that analysts become less optimistic in their recommendations following the introduction of less optimistic investors through the Stock Connect programs. In addition, catering theory predicts that when investors hold heterogeneous beliefs, analysts tend to segment the market and slant toward extreme positions in order to attract target investors. Consistent with this prediction, we find that analyst dispersion increases in the post period. Moreover, analysts with buy or strong buy (sell or underperform) recommendations of a given firm become more optimistic (pessimistic) in their research report tone. Finally, we show that in updating their earnings forecasts, analysts are more (less) responsive to earnings surprises that are consistent (inconsistent) with their stock recommendations. Overall, this paper presents evidence supporting a catering theory for analyst bias.

Origin of Outperformance for Stock Recommendations by Sell-Side Analysts

We examine the structure of portfolios built on sell-side analysts’ recommendations and show that those portfolios’ abnormal returns are explained primarily by the analysts’ stock picking ability and only partially by the effect of overweight in small-cap stocks, given that more than 80% of the studied portfolios are concentrated in the three smallest Size Deciles. We document the portfolios’ abnormal returns by examining the number of stocks in the portfolios and the weights assigned to market-cap Size Deciles and Global Industry Classification Standard (GICS) sectors and perform an attribution analysis that allows us to identify the sources of overall value-added performance. We find that the average monthly added value of 0.46 (0.34) percent obtained on Strong Buy and Buy recommendations from Stars (Non-Stars) is primarily explained by the analysts’ intra-sector stock-picking skills and that the monthly added value of 0.16 (0.18) percent obtained from Stars (Non-Stars) is related to the portfolios’ allocation among size-specific deciles.

Buying Manipulation around Seasoned Equity Offerings

We show that seasoned equity offerings (SEOs), in which secondary share-offering size greatly exceeds market share turnover, provide a unique opportunity for stock price manipulation by speculators holding restricted shares. The proposed new theory is consistent with several puzzling empirical findings, for example, the positive abnormal stock return between announcement and issuance and the long-term poorer returns of issuing firms after SEOs. The manipulation is characterized by a strong buying in the secondary market prior to the issuance, which drives up the market price and thereby the offering price of restricted shares, and a large amount of liquidation of restricted shares in the SEO. Our model predicts that favorable market reactions with speculator's manipulative intent after SEO announcement could occur, which naturally leads to a successful issuance and over-investment based long-term underperformance after SEO.

Can investors profit from security analyst recommendations?: New evidence on the value of consensus recommendations

This paper revisits the question of whether investors can benefit from consensus recommendations of stock market analysts in US equity markets. To examine the profitability net of transactions cost, we calculate several measures of transactions costs based on effective tick spreads and others. We find that transactions cost becomes noticeably lower from 2001 and the strategy of purchasing ‘strong buy’ stocks and shorting ‘strong sell’ stocks yields the abnormal returns of 4.7–5.8% per year during the period of 2001–2016, even after accounting for transactions cost. We also find that ‘strong buy (sell)’ stocks are growth (value) firms and short-term winners (losers). We discuss our empirical results in the context of market efficiency.

The Clothiers’ Century, 1450–1550

Abstract:In the hundred years from 1450 to 1550, the great success enjoyed by the English woollen industry in continental markets was a result of clothiers organising the rural cloth industry in the West Country, Suffolk/Essex, the Kentish Weald and Newbury and its surrounds, to produce cloth that London merchants required. To do this they allocated extensive capital to cloth production: buying wools, sorting and dyeing them, organising their carding and spinning, putting the yarn out for weaving, and then in some cases owning the mills that fulled the cloth and finally shearing it in-house. The leading clothiers carried wool and cloth inventories, developed strong buying networks and offered merchants credit. Clothiers' control over production declined after 1550 as the government exercised greater control over cloth quality and clothiers' freedoms, and as price competition intensified from coarser cloths and new draperies.

Can Investors Profit from Security Analyst Recommendations?

This paper revisits the question of whether investors can benefit from consensus recommendations of stock market analysts in US equity markets. To examine the profitability net of transactions cost, we calculate transactions cost based on effective tick spread. We find that transactions cost becomes noticeably lower from 2001 and the strategy of purchasing 'strong buy' stocks and shorting 'strong sell' stocks yields the abnormal returns of 4.7-5.8% per year during the period of 2001-2016, even after accounting for transactions cost. We also find that 'strong buy (sell)' stocks are growth (value) firms and short-term winners (losers). We discuss our empirical results in the context of market efficiency.

The Relative Grading Bias

This paper finds that ratings of sell-side analysts are negatively affected by the quality of the other stocks they have rated recently, even if those stocks are not in the same industry. As the influence of same-industry peers appears only after the disclosure pressure brought about by Global Settlement, but the influence of other-industry peers has always existed, this reference bias seems to have a strong behavioral component. We also document a price implication: the average difference between cumulative abnormal returns of “Strong Buy” stocks and Hold/Underperform/Sell” stocks is 4.3%; the average difference between cumulative abnormal returns of “Strong Buy” stocks of analysts with strong quality pools and “Hold/Underperform/Sell” stocks of analysts with weak quality pools is 6.1%, which is 42% greater.

Interest conflicts, stock recommendations and investor attention

This paper explores whether conflicts of interest between investors and investment banks are associated with investor attention. We find that analysts tend to issue positive recommendations about firms. However, analysts' recommendations do not align with their subsequent trades. Although conflicts of interest exist, they mainly occur on strong buy recommendations and firms that attract investor attention. Investment banks profit from strategically trading on their own recommended stocks that have high abnormal volume.

Do Stars Shine? Comparing the Performance Persistence of Star Sell-Side Analysts Listed by Institutional Investor, the Wall Street Journal, and StarMine

We investigate the profitability persistence of the investment recommendations from analysts listed in four different star rankings, Institutional Investor magazine, StarMine’s “Top Earnings Estimators” and “Top Stock Pickers”, and The Wall Street Journal, and show the predictive power of each evaluation methodology. We found that only Buy and Strong Buy recommendations from the entire group of Star analysts outperform those of the Non-Stars in the year after election, while Sell and Strong Sell recommendations performed as those of the Non-Stars. We document that the highest average monthly abnormal return of holding a long-short portfolio, 0.97 %, is obtained by following the recommendations of the group of star sell-side analysts rated by StarMine’s “Top Earnings Estimators” during the period from 2003 to 2014. Since earnings are one of the main drivers of stock prices, the results obtained are in line with the notion that focusing on superior earnings forecasts is one of the top requirements for successful stock picks.

Whose Money Is Smart? Institutional and Individual Trading Based on Analyst Recommendations

We explore how institutional and individual investors respond to analyst recommendations. Using a unique account-level trading dataset from the China, we find that 1) institutions are significantly net buyers (net sellers) on “strong buy” and “buy” (“hold” and “sell”) recommendations; 2) institutions condition their trades based on the buy-side pressure of analysts, corporate governance, and information transparency; 3) institutions earn abnormal returns through trades reacted to analyst recommendations; and 4) individuals, in contrast, trade in the direction against institutions. Our findings highlight the suboptimal investment decisions of individuals who are unaware of potential conflicts of interest that analysts may face.

Do Analysts Know But Not Tell? The Case of Going-Concern Opinions

This study explores whether sell-side analysts recognize firms’ going-concern (GC) difficulties, and whether and how they report going-concern uncertainties to investors. We show that analysts are aware of impending firm going-concern problems on the basis of their tendency to downgrade their stock recommendations to “hold” for, and increased probability of ceasing coverage of, GC firms compared with matched non-GC firms as the GC announcement date approaches. Importantly, only 11% of stock recommendations at the GC announcement date are unfavorable (as signalled by “underperform” or “sell”) in contrast to 42% of favorable recommendations (“strong buy” or “buy”). We further show that analysts react to the publication of the GC audit report mainly by stopping covering such firms.Our results suggest that analysts recognize firms’ going-concern uncertainties but communicate their negative prospects using opaque language that likely cannot be understood in particular by retail investors who constitute the main clientele of these firms. Consistent with the SEC’s concerns about the literal interpretation of analyst recommendations, we show that investors cannot rely solely on analyst recommendations in this extreme bad news domain where their information dissemination role is particularly salient.

The impact of analyst-investor disagreement on the cross-section of implied cost of capital

Implied cost of capital estimates are typically calculated using analysts’ forecasts as proxies for the market’s earnings expectations. We examine the case where deviations between investors’ expectations and analysts’ beliefs, as manifested by analysts’ recommendations, cause predictable variation in implied cost of capital. We find that stocks recommended by analysts as Buy or Strong Buy have, ceteris paribus, higher implied cost of capital than stocks recommended as Underperform or Sell, and that the effect is more clearly pronounced in stocks that have been downgraded. We attribute the effect to differential expectations between analysts and investors regarding future profitability, rather than differential expectations regarding systematic risk. We demonstrate that adjusting analysts’ earnings forecasts in line with the market’s earnings expectations largely eliminates the observed variation, indicating that such corrective mechanisms could and should be incorporated in the estimation of implied cost of capital.

Above and Beyond Recommendations: How Analysts Add Value to Their Fund Clients

This paper examines whether the sell-side research industry adds value to its mutual fund clients. I use a novel way to identify the broker-mutual fund network. I find that, within a mutual fund’s portfolio, the stocks covered by the fund’s brokers outperform the uncovered stocks by 6.3% per year, on average. This supports the view that sell-side analysts add value to their clients by helping them make better investment decisions. I further test whether the value added can be attributed to private communications between analysts and their clients. First, I find that, before an analyst releases a negative (positive) recommendation on a stock, her clients sell (buy) significantly more of the stock than do non-clients. Second, among stocks with a strong buy recommendation, those bought by clients before the recommendation announcements earn a 120-day post-recommendation abnormal return that is 1.78% higher, on average, than those sold by clients before the recommendations. These results suggest that brokers help their clients gain an information advantage over non-clients by providing private services and information. Overall, the paper helps to make sense of the existence and size of the sell-side research industry.

Hedge Funds and Analyst Conflicts of Interests

Are sell-side analysts reluctant to go against the investment views of their hedge fund clients? We show that analysts tend to upgrade stocks recently bought and downgrade stocks recently sold by hedge funds. Relative to other buy and strong buy recommendations, similar recommendations on stocks predominantly held by hedge funds parlay into poorer three-month and six-month stock returns. Hedge funds concurrently offload their stock holdings when analysts issue flattering reports. In line with an agency based explanation, our results are more pronounced for important brokerage clients such as high dollar turnover hedge funds and hedge funds who are prime brokerage clients of the analyst’s investment bank.