The impact of analyst sentiment on UK stock recommendations and target prices

Abstract

The aim of this paper is to investigate the relationship between narrative sentiment in analysts' company reports and their recommendation and target price outputs. We study an industry-balanced sample of 275 UK quoted company sell-side analyst reports over the period 2006–2010 using a content analysis methodology to measure net sentiment for a range of themes. We then model analysts' outputs against themed sentiment scores to analyse the impact of the Global Financial Crisis. We find that themed sentiments impact upon analysts' outputs, but their magnitude and direction vary over the pre-crisis, crisis and post-crisis periods. In particular, before the crisis we find a strong negative relationship between the macroeconomic and regulatory environment and report outputs, though this effect diminishes somewhat with the onset of the crisis, to be restored thereafter. Growth sentiment exerts a weak positive impact before the crisis which disappears thereafter. Financial performance sentiment becomes a significant positive driver of outputs following the crisis. There is evidently a “back to basics” approach following the crisis which restores financial fundamentals to the heart of stock analysis. Our findings provide some insight into the thought processes of analysts by identifying the dynamic relation between analysts' outputs and themed sentiments.

Keywords:

• analyst recommendations
• target price
• sentiment
• content analysis
• financial crisis

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. We classify the reports with a positive recommendation such as buy, overweight, outperform, and add under the buy category, reports with a neutral recommendation such as hold, equalweight, marketperform, and neutral under the hold category, and reports with a negative recommendation such as sell, underperform, underweight, and reduce under the sell category.

2. To check whether analysts forecasted the crisis at the start of 2007, we eliminate reports from 2007 but observe no discernible difference in the recommendation structure.

3. These changes are computed in relation to the previous recommendation given by the same analyst (brokerage house) for a given company.

4. These changes are computed in relation to the previous target price produced by a given analyst (brokerage house) for that company. Please note that sample size reduced in this table due to the previous target prices being omitted in eight reports.

5. In our study, the maximum number of words in a phrase is three, as in ‘like for like'.

6. For example, in a given analyst report, if there are 10 sentences that contain keywords relating to financial performance and if these sentences have a total of 80 words then NW = 80. We then divide this by the total number of words in a report; if a report has 1000 words then we compute a thematic variable score of 0.08.

7. The highest VIF is for SFinPerf with a value of 1.96.

8. We compute the Wald statistic to test whether the sum of the base (pre-crisis) period and crisis interaction coefficients is significant for the crisis period, and whether the sum of the base (pre-crisis) period and post-crisis interaction coefficients is significant for the post-crisis period. The null hypothesis is that the sum of the coefficients is equal to zero.