Financial analysts play an intermediary role in financial markets, resulting in two steps for information to be fully absorbed into the stock price: analysts reaction to information, and investors reaction to analysts recommendations. Thus any observed inefficiency in stock pricing could result from two possibilities: analysts failed to fully incorporate the market information into their stock analysis, or the information released in the analysts report is not fully believed by investors. <p>The documented optimism of financial analysts may suggest the possibility of the later case. To test the accuracy of analysts from another perspective, we follow a market microstructure model and use intraday market data to estimate the probability of an information event, the probability of good or bad news, and the rates that different traders arrive at the market. <p>By comparing those estimates based on days with and without recommendation changes, we find inconsistent results with regard to a difference in the probability of an information event. For some stocks, we do observe an increase in the likelihood of news on days when analysts change their recommendations, but this is not the case for most stocks. However, even though they are inaccurate most of the time, uninformed investors usually believe financial analysts. Furthermore, it seems that uninformed investors disbelieve analyst recommendation changes at those instances when analysts are most accurate. <p> Because of this, we hypothesise that market makers might suspect that orders in the opposite direction of an analysts recommendation change are more likely to come from informed traders. This is consistent with the intuition that most traders are uninformed and will simply follow the advice of a perceived expert, and therefore those that dont follow that advice may be more likely to have special information of their own. We check whether there are any differences in the probability of information-based trading (PIN) and for the conditional probability of information-based trading conditioned on sell (PIN|sell) and buy (PIN|buy) between days with and without recommendation changes. We did not find any significant difference, indicating that although we may observe a higher arrival rate of informed traders on recommendation change days, the probabilities of information-based trading do not change substantially. More informed traders seem to come to the market merely because the higher arrival rate of uninformed traders on recommendation days gives them a good opportunity to camouflage their behaviour. And the specialists likely would not have to change their behaviour on those days by increasing or shifting bid-ask spreads since the increased costs from the higher volume of informed trading are balanced by increased profits from the higher volume of uninformed trading. <p>Furthermore, regression of the probabilities of informed trading (conditional or unconditional) on firm size, trading volume, and volatility of daily return shows nothing significant, so we werent able to identify influential factors that affect informed trading or explain differences in informed trading between firms.

Financial analysts play an intermediary role in financial markets, resulting in two steps for information to be fully absorbed into the stock price: analysts reaction to information, and investors reaction to analysts recommendations. Thus any observed inefficiency in stock pricing could result from two possibilities: analysts failed to fully incorporate the market information into their stock analysis, or the information released in the analysts report is not fully believed by investors. <p>The documented optimism of financial analysts may suggest the possibility of the later case. To test the accuracy of analysts from another perspective, we follow a market microstructure model and use intraday market data to estimate the probability of an information event, the probability of good or bad news, and the rates that different traders arrive at the market. <p>By comparing those estimates based on days with and without recommendation changes, we find inconsistent results with regard to a difference in the probability of an information event. For some stocks, we do observe an increase in the likelihood of news on days when analysts change their recommendations, but this is not the case for most stocks. However, even though they are inaccurate most of the time, uninformed investors usually believe financial analysts. Furthermore, it seems that uninformed investors disbelieve analyst recommendation changes at those instances when analysts are most accurate. <p> Because of this, we hypothesise that market makers might suspect that orders in the opposite direction of an analysts recommendation change are more likely to come from informed traders. This is consistent with the intuition that most traders are uninformed and will simply follow the advice of a perceived expert, and therefore those that dont follow that advice may be more likely to have special information of their own. We check whether there are any differences in the probability of information-based trading (PIN) and for the conditional probability of information-based trading conditioned on sell (PIN|sell) and buy (PIN|buy) between days with and without recommendation changes. We did not find any significant difference, indicating that although we may observe a higher arrival rate of informed traders on recommendation change days, the probabilities of information-based trading do not change substantially. More informed traders seem to come to the market merely because the higher arrival rate of uninformed traders on recommendation days gives them a good opportunity to camouflage their behaviour. And the specialists likely would not have to change their behaviour on those days by increasing or shifting bid-ask spreads since the increased costs from the higher volume of informed trading are balanced by increased profits from the higher volume of uninformed trading. <p>Furthermore, regression of the probabilities of informed trading (conditional or unconditional) on firm size, trading volume, and volatility of daily return shows nothing significant, so we werent able to identify influential factors that affect informed trading or explain differences in informed trading between firms.

Financial analysts are considered inefficient when they do not fully incorporate relevant information into their forecasts. In this dissertation, I investigate differences in the observable efficiency of analysts' earnings forecasts between firms that consistently meet or exceed analysts' earnings expectations and those that do not. I then analyze the extent to which the market incorporates this (in)efficiency into its earnings expectations. Consistent with my hypotheses, I find that analysts are relatively less efficient with respect to prior returns for firms that do not consistently meet expectations than for firms that do follow such a strategy, especially when prior returns convey bad news. However, forecast errors for firms that consistently meet expectations do not appear to be serially correlated to a greater extent than those for firms that do not consistently meet expectations. It is not clear whether the market considers such inefficiency when setting its own expectations. While the evidence suggests they may do so in the context of a shorter historical pattern of realized forecast errors, other evidence suggests they may not distinguish between predictable and surprise components of forecast error when the historical forecast error pattern is more established.

Bachelor-Arbeit Univ. St. Gallen, 2005.

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Despite extensive research on price and earnings estimations, there are still puzzling results that have not been resolved. One of the puzzles in price estimation is that multiples using earnings forecasts outperform multiples using the residual income model (Liu, Nissim and Thomas, 2002). This puzzle undermines the validity of theory-based valuation models, which are originated from valuation theory and have been developed over the century. The first two projects of this thesis address this puzzle and explain mathematically how the pricing error of a multiple is determined by the correlation coefficient between price and a value driver. The projects then demonstrate that the puzzle in Liu, Nissim and Thomas (2002) is caused by the bad selection of residual income models and, in fact, the majority of residual income models (i.e. well-chosen residual income models) actually outperform multiples using earnings forecasts in pricing error. When models are examined in terms of future return generation, residual income models again outperform multiples using earnings forecasts, providing evidence that theory-based valuation models are superior to rule-of- thumb based multiples in price and intrinsic value estimations. The third project addresses an issue in earnings estimation by cross-sectional models. Recently, Hou, van Dijk and Zhang (2012) and Li and Mohanram (2014) introduce cross-sectional models in earnings estimation and argue that their cross-sectional models produce better earnings forecasts than analyst forecasts. However, their models suffer from one fundamental problem of cross-sectional models: the loss of firm-specific information in earnings estimation (Kothari, 2001). In other words, cross-sectional models apply the same coefficients (i.e. the same earnings persistence and future prospects) to all firms to estimate their earnings forecasts. The third project of this thesis addresses this issue by proposing a new model, a conditional cross-sectional model, which allows the coefficient on earnings to vary across firms. By allowing firms to use different earnings coefficients (i.e. different earnings persistence and future prospects), the project shows that a conditional cross-sectional model improves a cross-sectional model in all dimensions: a) bias, accuracy and earnings response coefficient; b) unscaled and scaled earnings estimations; and c) across all forecast horizons. The thesis contributes to the price and earnings estimations literature. First, the thesis addresses the decade-old puzzle in price estimation and rectifies the previous misunderstanding of valuation model performance. By demonstrating the superiority of theory-based valuation models over rule-of-thumb based multiples, the thesis encourages further development of theory-based valuation models. Second, in earnings estimation, the thesis provides future researchers a new model, which overcomes the fundamental problem of cross-sectional models in earnings estimation while keeping their advantages. In sum, the thesis improves the knowledge and practices of price and earnings estimations.

The effectiveness of the PEG ratio as a valuation tool has been a topical debate between market commentators ever since being popularised by Lynch (1989). This study examines the appropriateness of the fair value criteria of 1.0 (PEGL) in comparison with a time-series based share specific benchmarking model (PEGT). Furthermore, influencing factors of analyst forecasting accuracy, namely: the number of analyst contributions, forecast dispersion and forecast horizon, were tested and compared using sub-set portfolios for each category with the objective of identifying a possible optimal PEG trading rule strategy. The outcome showed a consistent outperformance of PEGT portfolios compared to PEGL portfolios and the market benchmark. Unexpected results were obtained for the impact of analyst forecasts on the performance of the PEG ratio with additional literature review providing possible reasons that analyst optimism may have a more influencing impact on the PEG ratio than forecasting accuracy. Finally, an optimised PEG trading rule strategy delivered annual abnormal returns of 5.4% (CAGR: 19.7%) for a PEGL portfolio, versus that of 13.7% (CAGR: 28.5%) for a PEGT portfolio. The ensuing methodology appeared to single out small cap firms with above market growth prospects. Copyright / Dissertation (MBA)--University of Pretoria, 2010. / Gordon Institute of Business Science (GIBS) / unrestricted

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Bachelor Thesis, Program of Master of Business and Economics, 15 hp School of Business and Economics – Linnaeus University in Växjö 2FE30E:3 Spring, 2017 Authors: Sofie Areskoug and Niklas Karlén Supervisor: Damai Nasution Examiner: Natalia Semenova Keywords: Financial Analyst, Gender, Determinants of forecast accuracy, Sweden Background: The search of finding analysts who make the best forecasts has been an ongoing process since the 1930's. Determinants that can help predict the forecast accuracy of the analysts are in the interest of both investors and brokerage houses. Newer research in this area has taken gender of the analyst into consideration. Women are widely under-represented in the analyst occupation and there is evidence that investors are apprehensive toward women in the financial sector. Purpose: The aim of this thesis is to examine determinants of forecast accuracy regarding analysts covering Swedish companies. The authors have confidence in the research to benefit investors in their decisions on the Swedish stock market. In addition, the authors aim to shed light on the unequal gender representation of female analysts. Method: This thesis has examined 519 individual scores of forecast accuracy from 284 financial analysts covering stocks on the Swedish Index OMXS30. The forecasts are from the years 2016 and 2017. This study has a quantitative strategy and the data have been tested by an OLS estimates regression. Results: The empirical evidence shows that being a female analyst have a statistically significant positive effect on forecast accuracy. Female analysts covering Swedish stocks seem to outperform their male colleagues. Furthermore, insignificant results were found for firm complexity, industry complexity, brokerage house and analyst experience.

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