Min Chen

Zhaobo Zhu
email: zb.zhu@szu.edu.cn

Peiwen Han

Bo Chen

Jia Liu
email: jia.liu@port.ac.uk.

Min Chen Zhaobo

Zhu Peiwen

Han Bo Chen

Economic policy uncertainty and analyst behaviours: Evidence from the United Kingdom

Keywords: Economic policy uncertainty, Analyst earnings forecast accuracy, Forecast dispersion, Analyst coverage, Analyst stock recommendations

This paper documents that both domestic and cross-country economic policy uncertainty have significant impacts on the behaviours of domestic analysts in the United Kingdom.

Specifically, domestic economic policy uncertainty has significant negative impacts on analyst earnings forecast accuracy, dispersion, and both analyst recommendation upgrades and downgrades, whereas it has no significant impact on analyst coverage in the United Kingdom. An industry analysis shows that the effects of policy uncertainties on analyst behaviours vary across industries. Moreover, European and global economic policy uncertainty have similar cross-country impacts as U.K. policy uncertainty on analyst behaviours in the United Kingdom, whereas U.S. policy uncertainty exhibits different impacts. This study presents novel and comprehensive evidence of the impacts of policy uncertainty on an important information intermediary that has significant influences on capital market efficiency, providing practical implications for investors, analysts, corporate managers, and policy makers.

Introduction

As information intermediaries, sell-side financial analysts play an important role in interpreting and transmitting valuable firm-level information on investment and financing decisions to external investors, thereby influencing the aggregate capital market. Research has documented various factors influencing financial analysts' performance, including heightened uncertainties. For example, [START_REF] Hope | The association between macroeconomic uncertainty and analysts' forecast accuracy[END_REF] find that inflation and foreign exchange volatility compromise analysts' forecast accuracy in an international setting. [START_REF] Baloria | Policy uncertainty and analyst performance[END_REF] find that U.S. presidential election cycles negatively affect analyst earnings forecast accuracy but positively influence the forecast dispersion in the United States.

However, there has been a paucity of research investigating the systematic effects of macro uncertainty due to economic policies on the behaviours of financial analysts outside of the United States or the effects of cross-country macroeconomic policy uncertainty on the behaviours of domestic analysts. Because of the cross-country differences in capital market development and institutional quality (e.g., [START_REF] Stulz | Culture, openness, and finance[END_REF], comprehensive tests in different settings are necessary to reach a universal argument that policy uncertainty has significant impacts on analysts. Moreover, motivated by existing studies on the cross-country spillover effect (e.g. [START_REF] Rapach | International stock return predictability: What is the role of the United States?[END_REF], along with the rapid global economic integration in recent decades, macro policy uncertainties in dominant countries and zones is expected to have a cross-country spillover effect on the capital market. Therefore, we address these issues by examining whether and how macro-level policy uncertainties in the United Kingdom, United States, Europe, and around the world affect the behaviours of sophisticated professional financial analysts in the U.K. setting. We adopt the latest economic policy uncertainty (EPU) index, developed by [START_REF] Baker | Measuring economic policy uncertainty[END_REF], to measure the U.K. economic policy uncertainty. The index incorporates an extensive range of uncertainty factors related to economic policies. 1Compared to traditional measures of macro uncertainties such as inflation, exchange volatility, or presidential election cycles, the EPU index measures uncertainty in dimensions of the economy, markets, policies, and regulations, allowing for cross-sectional variations at various frequencies of annual, monthly, and daily indices [START_REF] Brogaard | The asset-pricing implications of government economic policy uncertainty[END_REF].

Our investigations generate a set of interesting findings. First, the U.K. economic policy uncertainty has a negative impact on analysts' earnings forecast accuracy, consistent with the U.S. findings in [START_REF] Baloria | Policy uncertainty and analyst performance[END_REF]. Second, the U.K. economic policy uncertainty generates a negative impact on analysts' forecast dispersion. This indicates greater herding during times of policy uncertainty, consistent with [START_REF] Lin | The impact of aggregate uncertainty on herding in analysts' stock recommendations[END_REF], who finds that analysts' tendency to herd increases with aggregate uncertainty. Third, the numbers of stock recommendation upgrades and downgrades are negatively related to the U.K. economic policy uncertainty, suggesting that although analysts are less likely to upgrade firms at times of macro uncertainty because of concerns over unfavourable prospects, they are reluctant to downgrade firms during times of macro uncertainty. This is consistent with [START_REF] Hugon | Analysts, macroeconomic news, and the benefit of active in-house economists[END_REF], who observe that analysts underreact to negative macroeconomic news. Furthermore, following [START_REF] Gulen | Policy uncertainty and corporate investment[END_REF] and [START_REF] Baloria | Policy uncertainty and analyst performance[END_REF], we conduct an industry-level analysis to test whether firms in certain industries are more sensitive to policy uncertainty than firms in other industries. Naturally, if firms in some industries are more sensitive to policy uncertainty, then it is more challenging for analysts to forecast their results. We find that the impacts of U.K. economic policy uncertainty on analyst behaviours vary across industries.

Further, we analyse whether and how economic policy uncertainty in the United States, Europe, and across the globe affect analysts' behaviours in the United Kingdom.

Our investigation reveals that European and the global economic uncertainties have similar impacts on analysts' behaviours in the United Kingdom, exerting negative influence on forecast accuracy, dispersion, recommendation upgrades and downgrades. However, U.S. policy uncertainty demonstrates a negative impact on forecast accuracy and recommendation upgrades but a positive impact on analyst coverage. Overall, we find cross-country effects of policy uncertainties on analysts' behaviours, indicating crosscountry spillovers in terms of macro uncertainty. This paper contributes to the literature in several important ways. First, it is the only investigation of the impact of macroeconomic policy uncertainty on analysts' behaviours in the United Kingdom, providing novel evidence beyond the U.S. setting. The U.K. market has some features in common with the U.S. market, upon which most research to date has concentrated, but the markets also have a number of differences. For example, they differ in terms of investor protection and corporate disclosures [START_REF] Collins | Management discussion and analysis: an evaluation of practice in UK and US companies[END_REF]). 2 Moreover, analysts' behaviours also exhibit significant dissimilarities. Compared to U.S. analysts, U.K. analysts demonstrate lower forecast error [START_REF] Cho | Properties of market expectations of accounting earnings by financial analysts: UK versus US[END_REF] and consider communications with management more important [START_REF] Chang | An international comparison of investor uses of financial statements[END_REF]. In addition, U.S. analysts are subject to certain regulations, including the Regulation Fair Disclosure, which requires firms' management to grant all investors equal access to material information. Our investigation is both timely and informative, given that the U.K. economy is the fifth largest in the world, and the U.K. stock market is a leading player in the currently volatile global financial markets due to recent international crises.

Second, this study provides a comprehensive view of how the economic policy uncertainty impacts analysts' behaviours. Unlike existing studies, which focus on analyst forecast accuracy and dispersion, 3 we systematically examine the five main components of analysts' behaviours: forecast accuracy, forecast dispersion, coverage, and stock upgrade and downgrade recommendations. The literature has documented inconsistencies between analyst earnings forecasts and stock recommendations, although they are analysts' two main research outputs. Specifically, earnings forecasts and stock recommendations predict firms' prospects differently. 4 Furthermore, forecast accuracy is not always positively 2 The United States and United Kingdom share many similarities, such as the common law-based legal systems, artistic cultures, political ethoses, and market-based financial systems (La [START_REF] Porta | The economic consequences of legal origins[END_REF]. However, they are dissimilar in many other respects. U.S. investors have greater protections than their U.K. counterparts, with the SEC enforcing sometimes-draconian sanctions against wrongdoers (e.g., the Sarbanes Oxley Act). On the other hand, compared to U.S. companies, U.K. companies provide more informative reports, greater disclosures of risk and uncertainty, and more forward-looking information [START_REF] Collins | Management discussion and analysis: an evaluation of practice in UK and US companies[END_REF]. 3 Extant studies have focused on the impact of macro uncertainty on analysts' forecasting performance, mainly on their earnings forecast accuracy and earnings forecast dispersion (e.g., [START_REF] Amiram | How are analysts' forecasts affected by high uncertainty?[END_REF]Stubben, 2018, Baloria and[START_REF] Baloria | Policy uncertainty and analyst performance[END_REF][START_REF] Hope | The association between macroeconomic uncertainty and analysts' forecast accuracy[END_REF] but a few studies examine the impact on analyst stock recommendations (e.g., [START_REF] Lin | The impact of aggregate uncertainty on herding in analysts' stock recommendations[END_REF]. 4 A buy-and-hold investment strategy based on firm value estimated using analyst earnings forecasts and residual income models outperforms an investment strategy based on analyst stock recommendations [START_REF] Barniv | International evidence on analyst stock recommendations, valuations, and returns[END_REF][START_REF] Bradshaw | How do analysts use their earnings forecasts in generating stock recommendations?[END_REF]. related to recommendation profitability. 5 Our findings show that although the macroeconomic uncertainty negatively affects U.K. financial analysts, as the decrease in forecast accuracy shows, they tend to herd when issuing forecasts and act conservatively when revising stock recommendations in times of greater macroeconomic uncertainties.

Third, we provide evidence of the cross-country spillover effects in terms of policy uncertainty by demonstrating that the effects of European and global macroeconomic policy uncertainties on U.K. financial analysts are similar to the effect of U.K. macroeconomic policy uncertainty. This illustrates that along with the benefits of globalization, countries will have to bear more risks and uncertainties. Further, such uncertainties may appear in many aspects (e.g., financial analysts), even those not directly associated with international trade.

The rest of the paper is organized as follows: Section 2 discusses the hypotheses, Section 3 discusses the research design, and Section 4 discusses the empirical results and robustness test. A summary and conclusion are provided in Section 5.

Brief Literature Review and Hypothesis Development

Uncertainty refers to the situation when investors do not clearly know the probability measure governing future stock prices. People are adverse to uncertainty [START_REF] Ahn | Estimating ambiguity aversion in a portfolio choice experiment[END_REF][START_REF] Bossaerts | Ambiguity in asset markets: Theory and experiment[END_REF]. Whether economic policy uncertainty has a significant impact on analysts' behaviours is a topic 5 For buy recommendations, this positive relation holds only for analysts with no conflict of interest from investment banking activities. For hold recommendations, this conclusion holds only for conflicted analysts [START_REF] Ertimur | Measure for measure: The relation between forecast accuracy and recommendation profitability of analysts[END_REF]. This inconsistency arises partly from the negative influence of investment banking in the United States [START_REF] Barniv | Do analysts practice what they preach and should investors listen? Effects of recent regulations[END_REF][START_REF] Chen | NASD Rule 2711 and changes in analysts' independence in making stock recommendations[END_REF].

engaging considerable academic interest. When policy uncertainty is high, the forecasting environment is more challenging, with greater fluctuations in firms' operating activities and increased asymmetry of information between firms and analysts. For example, firms reduce investments and IPOs when uncertainty increases around the time of gubernatorial elections in the United States and when national elections take place across the globe (Colak, Durnev, and Qian, 2016;[START_REF] Jens | Political uncertainty and investment: Causal evidence from US gubernatorial elections[END_REF][START_REF] Julio | Political uncertainty and corporate investment cycles[END_REF]. Such changes complicate the analysts' tasks when they are seeking to predict firms' earnings forecasts.

Extant research has documented that analyst earnings forecasts are less accurate during bad times [START_REF] Hope | The association between macroeconomic uncertainty and analysts' forecast accuracy[END_REF][START_REF] Jacob | Discussion of do security analysts improve their performance with experience?[END_REF] and during presidential elections in the United States [START_REF] Baloria | Policy uncertainty and analyst performance[END_REF]. Based on the foregoing discussions, we hypothesized the following.

Hypothesis 1: Macroeconomic uncertainty is negatively related to earnings forecast accuracy.

The second measure of analyst behaviours is their earnings forecast dispersion, which reflects the standard deviation of earnings forecasts all analysts have issued for the same firm in the same period. Research has demonstrated that analysts exhibit herding behaviour; that is, they refer to peer analyst earnings forecasts and then issue similar forecasts, ignoring their own research [START_REF] Clement | Financial analyst characteristics and herding behavior in forecasting[END_REF][START_REF] Jegadeesh | Do analysts herd? An analysis of recommendations and market reactions[END_REF][START_REF] Lamont | Macroeconomic forecasts and microeconomic forecasters[END_REF], seeking either to share blame or match the forecast quality of their counterparts in the industry. Analysts herd for many reasons, for example, belief in better information, receipt of greater compensation, or enhancement of reputation (e.g., Scharfsten and Stein, 1990;[START_REF] Trueman | Analyst forecasts and herding behavior[END_REF]. Further, [START_REF] Lin | The impact of aggregate uncertainty on herding in analysts' stock recommendations[END_REF] reports that analysts' tendency to herd increases with the uncertainty of future aggregate equity returns. We argue that analysts behave similarly and herd when uncertainty results because of heightened concerns over economic policies. Specifically, such uncertainty increases uncertainties in relation to firms' operations, financing, cash holdings, IPOs, and mergers and acquisitions [START_REF] Bonaime | Does policy uncertainty affect mergers and acquisitions?[END_REF]Colak, et al., 2016;[START_REF] Jens | Political uncertainty and investment: Causal evidence from US gubernatorial elections[END_REF][START_REF] Julio | Political uncertainty and corporate investment cycles[END_REF], which, in turn, increases the complexity of forecast issuance. Heightened uncertainty can also increase analysts' job insecurity in times of recession, motivating them to join the herd to escape individual blame. Because adaptation to a group norm means analysts tend to issue similar forecasts, we expect less forecast dispersion considering that macroeconomic uncertainty intensifies analysts' herding instincts. Therefore, our corresponding hypothesis is the following.

Hypothesis 2: Macroeconomic uncertainty is negatively related to earnings forecast dispersion.

The third measure of analyst behaviour is analyst coverage, which is the number of analysts following a firm. Prior research has documented uncertainty drives investor demand for analyst research in valuation and investment decisions. For example, [START_REF] Barniv | Does information uncertainty affect investors' responses to analysts' forecast revisions? An investigation of accounting restatements[END_REF] find that investors in restatement firms have a greater demand for accurate analyst forecasts than investors in non-restatement firms. [START_REF] Amiram | How are analysts' forecasts affected by high uncertainty?[END_REF] report that during periods of high market, industry, and corporate uncertainty, 6 analyst forecasts are less accurate but timelier yet still trigger significant market reactions. This indicates that investors demand timely information, regardless of its lower accuracy. [START_REF] Amiram | How are analysts' forecasts affected by high uncertainty?[END_REF] 6 Market uncertainty is measured as the standard deviation of the value-weighted daily market return over the 30 days prior to analyst forecast. Industry uncertainty is measured as the standard deviation of the valueweighted daily industry return (in excess of the daily value-weighted market return) over the 30 days prior to the forecast. Firm uncertainty is measured as the standard deviation of firm's stock return (in excess of the daily value-weighted industry return) over the 30 days prior to the forecast. also observe that it is more challenging for analysts to deal with heightened market uncertainty than with industrial and corporate uncertainty, as declines in both timeliness and forecast accuracy demonstrate. In addition to investors, a firm's management are also aware of the greater information asymmetry prevailing during times of uncertainty and demand information from more channels. [START_REF] Kirk | Research for sale: Determinants and consequences of paid-for analyst research[END_REF] consider firms that face greater uncertainty are those that have a higher standard deviation of stock returns, that are in the high-technology industries, and that are more R&D intensive. He finds that these firms are more likely to hire fee-based analyst research firms to increase analyst coverage. The increase in analyst coverage reduces the information asymmetry between the investors and firms, facilitates firms' access to the capital market, and reduces firms' costs of capital [START_REF] Bushee | Investor relations, firm visibility, and investor following[END_REF][START_REF] Easley | Information and the cost of capital[END_REF].

In a similar way to the uncertainty experienced at the firm and industry levels, macroeconomic uncertainty increases information asymmetry that, in turn, affects firms' liquidity and costs of capital. Both the investors and managers demand increased analyst coverage during times of uncertainty. Based on the foregoing discussions, we hypothesize the following.

Hypothesis 3: Macro-economic uncertainty is positively related to analyst coverage, depending on the trade-off between incentives and the costs of coverage.

The fourth and fifth measures of analyst behaviour are analyst recommendation upgrades and recommendation downgrades, which are more likely to generate additional trading than analysts' earnings forecasts [START_REF] Irvine | Analysts' forecasts and brokerage-firm trading[END_REF]. Analysts' recommendations usually contain market-and industry-level information about future returns and earnings (e.g., [START_REF] Bradshaw | How do analysts use their earnings forecasts in generating stock recommendations?[END_REF][START_REF] Howe | The predictive content of aggregate analyst recommendations[END_REF]. Research has established that macro uncertainty has an influence on analysts' recommendations. [START_REF] Bradshaw | How do analysts use their earnings forecasts in generating stock recommendations?[END_REF] demonstrates a positive relationship between analysts' stock recommendation revisions and changes in long-term growth. Because the prediction of long-term growth is negatively correlated with macro uncertainty [START_REF] Kneller | Business cycle volatility, uncertainty and long-run growth[END_REF], a negative and a positive association should exist between macro policy uncertainty and upgrade and downgrade recommendations, respectively.

However, in addition to stock valuations based on analysts' primary research, analysts issue or revise stock recommendations based on many other considerations, including private communications with firms' managers and the generation of underwriting business, which determines their financial compensation [START_REF] Brown | Inside the "black box" of sell-side financial analysts[END_REF][START_REF] Soltes | Private interaction between firm management and sell-side analysts[END_REF].

Thus, analysts are reluctant to downgrade stocks [START_REF] Westphal | Sociopolitical dynamics in relations between top managers and security analysts: Favor rendering, reciprocity, and analyst stock recommendations[END_REF], even at times of macro uncertainty. Taking these factors into account, during a period of macroeconomic uncertainty, analysts are unlikely to revise their recommendations upward because they are not positive about firms' long-term growth prospects. However, they are also unlikely to revise their recommendations downward because of their close connections with the firms' managers. Our corresponding hypothesis therefore is as follows.

Hypothesis 4: Macro-economic uncertainty is negatively related to recommendation upgrades and downgrades.

Data and Methodology

Research Design

When examining the relationship between analyst performance and macro uncertainty, we follow standard methods reported in the literature and control for firm-level characteristics and macro factors [START_REF] Baloria | Policy uncertainty and analyst performance[END_REF]Hope and Kang 2015). We use the following model:

Analyst performancei,t = α Macro_uncertaintyi,t + β1 ROAi,t +β2 Leveragei,t+ β3Smoothi,t + β4 Sizei,t + β5 Sentimenti,t + β6 MKT Volatilityi,t + Industry fixed effect + Ɛi,t, (1)
where Macro_uncertainty is measured with U.K. EPU. We employ the weighted average of the original indices developed by [START_REF] Baker | Measuring economic policy uncertainty[END_REF] to calculate U.K. EPU. 7 We assign two thirds of the weight to the month prior to the last month (November) in a given year t because analysts will need time to observe the effect of the macro uncertainty on firms or to incorporate the uncertainty information into their earnings forecasts and stock recommendations. We assign one third of the weight to the second to last month (October) in a given year t because macro uncertainty may have lingering effects on firms and analysts.

UKEPUt,m = )EPU t,(m-1) +( 13

)EPU t,(m-2) 1,000 (2)
Analyst performance is measured with earnings forecast accuracy, earnings forecast dispersion, analyst coverage, stock recommendation upgrades, and stock recommendation downgrades. Following prior literature on analysts, forecast accuracy (accuracy) is calculated as -1 times the absolute difference between the mean analyst earnings forecast per share over a year and firms' actual earnings per share scaled by the price per share.8 Forecast dispersion (dispersion) is the standard deviation of all analyst earnings forecasts issued for a firm over a year. Analyst coverage (logfollow) is calculated as the natural logarithm of the sum of one plus the number of analysts following a firm.

Stock recommendation upgrades (lognumrecup) is calculated as the natural logarithm of the sum of one plus the number of recommendations that have been revised upward, whereas recommendation downgrades (lognumrecdown) is calculated as the natural logarithm of the sum of one plus the number of recommendations that have been revised

downward. The calculation of these variables is demonstrated below:

Analyst earnings forecast accuracyi,t = (-1) * | mean EPS i , t -actual EPS i , t price per share i , t | (3)
We control firm level characteristics to isolate the effects of macro uncertainty on analyst performance because prior research has documented a correlation between analyst behaviour and firm characteristics. For example, [START_REF] Barth | Analyst coverage and intangible assets[END_REF] demonstrate that analyst coverage is positively associated with firm size, growth, equity issuance, and perceived mispricing. Thus, we control for firm size, return on assets, leverage, and income smoothing. Return on assets (ROA) is calculated as the income before extraordinary items divided by the average total assets. Leverage (Leverage) is calculated as total long-term debt divided by the average total assets. Firm size (Size) is calculated as the natural logarithm of the total assets. Income smoothing (Smooth) is calculated as the standard deviation of earnings divided by the standard deviations of cash flows from operations, where earnings and cash flows are divided by the lagged total assets [START_REF] Baik | Managerial ability and income smoothing[END_REF].

We also control for other macro factors that potentially influence analyst behaviours to isolate the effects of macro uncertainty on analyst behaviour. We include investor sentiment (Sentiment) as a macro-level control variable because [START_REF] Walther | Do investor expectations affect sell-side analysts' forecast bias and forecast accuracy?[END_REF] find that this is significantly related to forecast accuracy. Following [START_REF] Lemmon | Consumer confidence and asset prices: Some empirical evidence[END_REF] and [START_REF] Schmeling | Investor sentiment and stock returns: Some international evidence[END_REF], we use the standard deviation of the consumer confidence index over the year as a proxy for investor sentiment. Similarly, [START_REF] Amiram | How are analysts' forecasts affected by high uncertainty?[END_REF] find that market volatility significantly affects forecast accuracy.

Therefore, we control for market volatility (MKT Volatility) as measured by the standard deviation of the Financial Times Stock Exchange 100 index (the FTSE index) over the year.

In addition, we control for industry fixed effects in the regressions. The Appendix A presents the variable descriptions in detail. All dependent variables and control variables are winsorized at the 1% and 99% levels to minimize the effect of outliers.

Data and Sample

We obtain analyst data from I/B/E/S and firms' financial data from Compustat Global. We start with all U.K. firms with 1-year-ahead analyst earnings forecasts available on I/B/E/S and delete any observation with missing data on the control variables. The final sample covers 1998-2016 and comprises 1,893 firms with 12,717 firm-year observations. Table 1 describes the sample selection. analysts coverage, indicating that when there is economic uncertainty, firms' forecasting is more complex and is therefore followed by more analysts. The index is also positively correlated with firm size, the market sentiment index, and stock market volatility. This suggests that larger firms are more likely to be influenced by uncertainty in economic policy because analysts pay closer attention to their forecasts during economic fluctuations, so that their consequential prognostications influence, in their turn, both investors and the stock market. Hypothesis 3, we investigate this hypothesis further in the industry analyses section.

Empirical Results and Discussions

U.K. Policy Uncertainty and Analyst Behaviours

Columns 4 and 5 report significantly negative coefficients on the log of the number of recommendation upgrades and the log of the number of recommendation downgrades.

This supports our Hypothesis 4 that analysts are reluctant to revise their recommendations during times of macroeconomic uncertainty because they are concerned with firms'

prospects and damaging their connection to firms' managers. This is consistent with prior findings that when issuing their research products, analysts consider multiple sources of information, such as their own research, firms' financial statements, private information obtained from firms' managers, and brokerage firms' underwriting business [START_REF] Brown | Inside the "black box" of sell-side financial analysts[END_REF][START_REF] Soltes | Private interaction between firm management and sell-side analysts[END_REF][START_REF] Westphal | Sociopolitical dynamics in relations between top managers and security analysts: Favor rendering, reciprocity, and analyst stock recommendations[END_REF].

The above results show that in the United Kingdom, macroeconomic uncertainty increases the complexity of analysts' tasks, which, in turn, intensifies analysts' herding behaviour. Although analysts are not positive enough to revise stock recommendations upward, analysts also are unwilling to revise stock recommendations downward, probably due to concerns about damaging their relationships with firm management. This is in contrast to the findings for the United States, as documented in [START_REF] Baloria | Policy uncertainty and analyst performance[END_REF], that analyst earnings forecasts are less accurate and more dispersed when the macroeconomy is more uncertain. We also provide novel evidence of the impacts of macroeconomic uncertainty on analyst coverage and stock recommendations, which have not been studied in the U.S. setting.

Industry-level Effect

Prior research has asserted that firms in some industries are more sensitive to policy uncertainty than firms in other sectors [START_REF] Gulen | Policy uncertainty and corporate investment[END_REF]. If firms operating in industries related to the government are more sensitive to policy uncertainty, analysts find it more challenging to deal with information concerning them. [START_REF] Boutchkova | Precarious politics and return volatility[END_REF] demonstrate that the impact of political uncertainty on firms' decisions depends on three industry characteristics: contract enforcement, labour intensity, and international trade exposure, a conclusion that is also supported by [START_REF] Baloria | Policy uncertainty and analyst performance[END_REF].

Further, [START_REF] Amiram | How are analysts' forecasts affected by high uncertainty?[END_REF] demonstrate that analysts achieve greater forecast accuracy when faced with heightened industry uncertainty than they do when confronted by market-and firm-level uncertainty. This suggests that analysts are able to make more accurate forecasts in some industries than in others because of their experiences [START_REF] Bradley | Are all analysts created equal? Industry expertise and monitoring effectiveness of financial analysts[END_REF]. [START_REF] Kadan | Analysts' industry expertise[END_REF] provide further evidence of analysts' industry expertise on the basis of the recommendations they make in specific industries. Taken together, policy uncertainty would have greater impacts on industries that are more sensitive to policy uncertainty.

To identify the impact of economic policy uncertainty on analyst behaviours, we conduct an industry-level analysis. We use the NAICS industry system and focus on Table 4 reports the results across these industries. Panel A reports the results of analyst earnings forecast accuracy. These findings show that the coefficient of policy uncertainty is significantly negative in the mining sector. Untabulated analyses of the regression in more detailed NAICS industry classifications within the mining sector demonstrates that analyst forecast accuracy is significantly negative in the oil and gas industries. This is consistent with prior research on the significant relationships between oil and gas industry uncertainty and macroeconomic uncertainty [START_REF] Barsky | Oil and the macroeconomy since the 1970s[END_REF][START_REF] Hamilton | Oil and the macroeconomy since World War II[END_REF]. In addition, our results show that analyst earnings forecasts are less accurate during downturns of the macroeconomy, predominantly in the oil and gas industries.

Panel B reports the results of the analyst earnings forecast dispersion. Our results indicate that a significant negative relationship between policy uncertainty and analyst forecast dispersion is present in the construction sector. Untabulated analyses reveal that the significant negative relationship in the residential construction industry dominates the effect in the construction sector. Analyst forecast dispersion is also significantly negatively related to policy uncertainty in the information sector, especially in the industry sector of internet publishing, broadcasting, and web search portals (untabulated). This could be because the generally greater demand to gather more information during periods of uncertainty has resulted in better prospects for firms in the internet, broadcasting, and web search industries [START_REF] Bontempi | Uncertainty, perception and the internet[END_REF].

Analyst forecast dispersion is significantly positive in the transportation and warehousing sector. Interestingly, forecast dispersion is significantly positive in the air transportation industry but negative in industries providing supporting activities for air transportation and water transportation (untabulated). The air transportation industry has experienced many storms such as the Great Depression and the attacks on September 11, 2001, and had to reduce capacity to adjust to increased costs given the decline in demand during times of uncertainty [START_REF] Franke | What comes next after recession?-Airline industry scenarios and potential end games[END_REF][START_REF] Pearce | The state of air transport markets and the airline industry after the great recession[END_REF]. The reduction in demand and changes in capacity complicate forecasting tasks and exacerbate analysts' opinions on firms' earnings, which will result in greater forecast dispersion.

Panel C reports the results for analyst coverage in the 12 broad sectors. We find converse impacts of policy uncertainty on analyst coverage across industries. The coefficients of policy uncertainty are significantly positive in the manufacturing sector, whereas they are significantly negative in the retail trade and the arts, entertainment, and recreation sectors. Such an effect indicates investors have a greater demand for analyst research on the manufacturing sector due to the production of life necessities and the production of equipment to potentially restart businesses.

In contrast, investors demand less information on the retail trade, and the arts, entertainment, and recreation sectors probably because of the declines in business these industries experience during times of uncertainty. The offsetting effects in these industries explain the insignificant coefficient of analyst coverage in the aggregate sample. Overall, these results imply a significant industry effect on the relationship between EPU and analyst coverage.

Panels D and E report the results for analyst recommendation revisions. Panel D

shows that the EPU coefficient is significantly negative for upward recommendation revisions in several industries, namely, manufacturing, transportation, communication, electric, gas and sanitary services, wholesale trade, and retail trade and services. This suggests that analysts are reluctant to upgrade recommendations for firms in these industries when policy uncertainty is high.

Panel E reports that in the case of downward recommendation revisions, the EPU coefficient is significantly negative in manufacturing, transportation, communication, electric, gas and sanitary services, and retail trade. This suggests that analysts are reluctant to downgrade recommendations for firms in these industries when policy uncertainty is high. It is interesting that analysts are hesitant to revise their recommendations either upward or downward for similar industries. Our results, in general, suggest that analysts tend to be reluctant to revise their recommendations upward or downward for most industries when policy uncertainty is high.

In sum, policy uncertainty has different impacts on analyst behaviours across industries. Most notably, policy uncertainty has opposing impacts on analyst coverage across industries, leading to an insignificant impact for the overall sample, as shown in Table 3.

Cross-Country Impacts of Policy Uncertainty on Analyst Behaviours

With increasing globalization, economies around the world have become dynamically interrelated. [START_REF] Rapach | International stock return predictability: What is the role of the United States?[END_REF] find that lagged U.S. market returns can significantly predict returns in many non-U.S. industrialized countries. Brogaard, Dai, Ngo, and Zhang (2020) contend that political uncertainty as measured by the U.S. election cycle has a significant impact on asset prices in other countries. The United Kingdom is the fifth largest economy in the world during our study period and thus plays an important role in the world economy, because its own economy is globally well integrated. In this subsection, we investigate whether, and in what ways, policy uncertainty in other regions significantly influences analyst behaviours in the United Kingdom. In particular, we examine the individual impacts of the U.S., European, and global policy uncertainties on analyst behaviours.

Table 5 reports the results of the impact of policy uncertainty in the United States on analyst behaviours in the United Kingdom. First, the U.S. EPU coefficient is significant and negative in the case of analyst earnings forecast accuracy, suggesting that policy uncertainty in the United States has a significant and negative impact on analyst forecast accuracy in the United Kingdom. Second, the U.S. EPU coefficient is insignificant in the case of the analyst earnings forecast dispersion, demonstrating that policy uncertainty in the United States has no impact on analyst forecast dispersion in the United Kingdom.

Third, the coefficient of analyst coverage is significantly positive, indicating that more analysts in the United Kingdom tend to follow firms when policy uncertainty in the United

States is high.

Fourth, the U.S. EPU coefficient is insignificant and negative in the case of recommendation upgrade revisions, suggesting that analysts are reluctant to upgrade recommendations for U.K. firms when policy uncertainty in the United States is high.

However, the U.S. EPU coefficient is insignificant in the case of recommendation downgrade revisions, suggesting that policy uncertainty in the United States has no significant impact on analyst recommendation downgrade revisions. This reveals that despite the close connections between the United States and United Kingdom, the market and information environments are still different. Investors and analysts refer to the specific macro policies in each country in their decision-making processes. Overall, these results demonstrate that policy uncertainty in the United States has a differing impact on analyst dispersion, coverage, and recommendation downgrade revisions in the United Kingdom. Third, consistent with the insignificant effect of U.K. EPU over analyst coverage, the European EPU has an insignificant impact on analyst coverage. Fourth, in alignment with our findings for U.K. EPU, the European EPU coefficients are significantly negative in the case of both recommendation upgrade and downgrade revisions, suggesting that analysts are reluctant to revise their recommendations upward or downward in the United Kingdom when policy uncertainty in Europe is high. Overall, these results show that policy uncertainty in Europe has a significant impact on some aspects of analyst behaviours in the United Kingdom.

Table 7 reports the results for the impact of global EPU on analyst behaviours in the United Kingdom. First, global policy uncertainty has a significant negative impact on analyst forecast accuracy. Second, consistent with our finding on European EPU, we observe that policy uncertainty around the world has a significant negative impact on the analyst earnings forecast dispersion in the United Kingdom. Third, global policy uncertainty has no significant impact on analyst coverage in the United Kingdom.

Fourth, in line with our findings for recommendation upgrade and downgrade revisions in the United Kingdom, the negative global EPU coefficients suggests that analysts are reluctant to revise their recommendations upward or downward in the United Kingdom when global policy uncertainty is high. Overall, these results demonstrate that policy uncertainty around the world has a significant impact on some aspects of analyst behaviours in the United Kingdom.

In sum, we find that U.S., European, and global policy uncertainties has significant negative impacts on analyst earnings forecast accuracy in the United Kingdom. In addition,

European and global policy uncertainties have similar effects to U.K. policy uncertainty on analyst behaviours in the United Kingdom. However, U.S. policy uncertainty has differing impacts on analyst dispersion, coverage, and recommendations in the United Kingdom.

Overall, our results demonstrate that policy uncertainty has cross-country effects on analyst behaviours in other countries.

Robust Test

We examine the validity of the effect of EPU on analyst behaviours by including additional macro-level factors in the models. Specifically, we include the geopolitical risk and an indicator of macroeconomic activity in United Kingdom. [START_REF] Carney | Uncertainty, the economy and policy[END_REF] considers the geopolitical risk as influential as the economic and policy uncertainty because of the significant adverse economic effects. The European Central Bank and the International Monetary Fund highlight geopolitical uncertainties as a salient risk to the economies. 9 We measure geopolitical risk with the geopolitical risk index developed by [START_REF] Caldara | Measuring Geopolitical Risk[END_REF]. This index is the results of automated newspaper text-search of words associated with geopolitical risk, nuclear tensions, war threats, terrorist threats, and actual adverse geopolitical events.

We the macroeconomic activity with annual GDP growth because GDP is a key summary statistic of the economy (e.g., [START_REF] Mcculla | Measuring the Economy: A primer on GDP and the National Income and Product Accounts[END_REF] 10 . [START_REF] Henderson | Measuring economic growth from outer space[END_REF] consider GDP as the most important variable in analyses of economic growth. Financial analysts are likely to factor the geopolitical risk and GDP growth in their earnings forecasts and stock recommendations. Including the geopolitical risk and the GDP growth in the models will isolate the effects of EPU on analyst behaviours, and thus can test the robustness of EPU's effects.

We examine whether the effects of EPU on analyst behaviours are still significant after controlling for these two variables. Columns 1, 3, 5 and 7 in Table 8 tabulate the results after we only include geopolitical risk index in the main analyses. Columns 2, 4, 6 and 8 in Table 8 tabulate the results after both geopolitical risk index and the GDP growth rate are controlled. The independent variable, U.K. EPU, continues to be significantly negatively related with analyst forecast accuracy, analyst coverage, analyst recommendation upgrades and downgrades. The original findings continue to hold.

Conclusions

This paper investigates the impact of policy uncertainty on analyst behaviours in the United Kingdom. We find that greater U.K. EPU leads to less accurate earnings forecasts but lower forecast dispersion among analysts in the United Kingdom. Analysts tend to be reluctant to revise their recommendations upward or downward when policy uncertainty is high, suggesting that they consider not only firms' prospects but also their relationships with firms' managers during uncertain times. However, policy uncertainty has no significant impact on analyst coverage when we control for firm-specific variables. The industry analysis reveals that policy uncertainty has different impacts on analyst behaviours across industries. Notably, it produces converse influences on analyst coverage in different industries.

Finally, we provide strong evidence of cross-country effects of policy uncertainty by demonstrating that policy uncertainties in the United States, Europe, and around the world have significant impacts on analyst behaviours in the United Kingdom, arguably because the latter plays a significant role in the international market and because the U.K.

economy is well integrated into world trade and the global economy.

Our paper has implications for financial analysts, policy makers, firm managers and investors. Financial analysts should be aware that not just domestic uncertainty but also foreign uncertainty will complicate their tasks, and they are encouraged to explore tools or do research to predict and incorporate such uncertainties better. For example, they could seek access to an economist or award-winning macroeconomists. On the other hand, when proposing certain policies, policy makers should consider the potential impact of information dissemination on the capital market.

Firm managers should be aware of the impacts of uncertainties on analysts' research and provide voluntary disclosures on firms' practices to cope with uncertainty.

This will assist both analysts and investors with investment decisions. Investors should be investing more cautiously during uncertain times and refer to more trustworthy sources for investing advices, for example, earnings forecasts and stock recommendations from award-winning analysts. Our findings signpost a direction for future research to investigate how global policy uncertainty affects analyst behaviours around the world.

  industries with more than 100 observations, which are (a) mining; (b) utilities; (c) construction; (d) manufacturing; (e) wholesale trade; (f) retail trade; (g) transportation and warehousing; (h) information; (i) professional, scientific, and technical services; (j) administrative support and waste management and remediation; (k) arts, entertainment, and recreation; and (l) accommodation and food services.

Table 2

 2

	presents the descriptive statistics and correlations for all variables. Panel A
	reports that the mean (median) analyst earnings forecasts accuracy is 0.0835 (-0.0122), the
	mean (median) dispersion of earnings forecasts is 0.1024 (0.0284), and the mean (median)

log of analyst following for a firm in a single year is 1.7450 (1.6094), equivalent to a mean of 4.72 (3.99) for the analyst following. The mean log of the number of analyst upward recommendation revisions is 0.1028, equivalent to 0.11 upward recommendation revisions.

The mean log of the number of analyst downward recommendation revisions is 0.1118, equivalent to 0.12 downward recommendation revisions. The medians of the log of downward recommendation revisions and upward revisions are both 0.0000, equivalent to 0 downward and upward recommendation revisions. In addition, firms in the United

Table 3

 3

reports the results of the relationships between U.K. policy uncertainty and proxies for analyst behaviours after controlling for other macro-level variables and firm-level variables. Column 1 shows that analyst forecast accuracy is negatively related to U.K. policy uncertainty after controlling for the variables, consistent with Hypothesis 1, which indicates that analyst earnings forecasts are less accurate during times of macroeconomic uncertainty. Similar findings have been made in the United States.

[START_REF] Baloria | Policy uncertainty and analyst performance[END_REF]

find less accurate analyst forecasts around times of presidential elections.

Column 2 shows that analyst forecast dispersion is negatively related to U.K. policy uncertainty after controlling for the variables, consistent with Hypothesis 2, demonstrating that analysts herd more during times of macroeconomic uncertainty. This finding is in line with the conclusion of

[START_REF] Lin | The impact of aggregate uncertainty on herding in analysts' stock recommendations[END_REF]

that analysts' tendency to herd increases with the uncertainty of future aggregate equity returns. In contrast,

[START_REF] Baloria | Policy uncertainty and analyst performance[END_REF]

find more dispersed analyst forecasts around presidential elections in the United States. This indicates the potential for different analyst behaviours under the impact of the macro environments in the United Kingdom and United States. Interestingly, Column 3 shows that analyst forecast coverage is insignificantly related to U.K. policy uncertainty after controlling for the variables. Although this cross-sectional sample does not support our

Table 6

 6 reports the results of the impact of European EPU on analyst behaviours in the United Kingdom. The United Kingdom is a major economy in the European zone; therefore, we expect policy uncertainty in Europe to have a more significant impact on analyst behaviours in the United Kingdom than U.S. uncertainty. Consistent with our predictions, policy uncertainty in Europe has a significant impact on analyst forecast accuracy for the United Kingdom. Second, consistent with our findings for the U.K. EPU, policy uncertainty in Europe increases analyst earnings forecast dispersion in the United Kingdom.

Table 1 :

 1 Sample Selection

	All Compustat firms with firm financial data in UK between 1998 and 2018	21,936
	Less: Observations with missing I/B/E/S analyst earnings forecasts or	8,779
	recommendations
	Less: Observations with missing data to compute U.K. EPU	440
	Final sample for main analysis	12,717

Table 3 :

 3 U.K. Economic policy uncertainty and analyst behaviorsThis table presents the average estimated coefficients from regressions of measures of analyst behaviors on U.S. EPU. All variables are defined in Appendix A. *, **, and *** indicate significance at the 10%, 5%, and 1% levels (two-tailed), respectively. T-statistics are in parentheses.

		(1)	(2)	(3)	(4)	(5)
		Accuracy	Dispersion	Coverage	Upgrade	Downgrade
	Intercept	-0.067**	0.022	0.026	-0.112***	-0.132***
		(-2.64)	(1.10)	(1.43)	(-11.80)	(-13.30)
	UK EPU	-0.022*	-0.018*	-0.000	-0.030***	-0.021***
		(-1.73)	(-1.85)	(-0.04)	(-5.90)	(-4.00)
	ROA	0.344***	-0.231***	-0.073***	-0.033***	-0.049***
		(11.57)	(-9.57)	(-3.54)	(-2.83)	(-4.09)
	Leverage	0.042	-0.033*	-0.012	-0.007	-0.022**
		(1.88)	(-1.85)	(-0.85)	(-0.98)	(-2.72)
	Smooth	-0.003*	0.001	0.001	0.000	-0.000
		(-1.66)	(0.95)	(0.71)	(0.01)	(-0.16)
	Size	0.010***	0.014***	0.336***	0.045***	0.051***
		(2.90)	(5.52)	(143.40)	(36.60)	(39.30)
	Sentiment	-0.029	0.038***	0.015	0.013*	0.007
		(-1.55)	(2.64)	(1.09)	(1.77)	(0.98)
	MKT Volatility	-0.010**	0.010	-0.010	0.010	0.010
		(-2.23)	(1.43)	(-1.59)	(1.39)	(1.38)
	Industry Fixed Effects	Yes	Yes	Yes	Yes	Yes
	N	11,157	10,899	12,243	11,613	11,613
	R 2	0.0201	0.0112	0.6644	0.1141	0.1256

Table 4 :

 4 U.K. economic policy uncertainty and analyst behaviors: an industry analysisThis table presents the average estimated coefficients from regressions of measures of analyst behaviors on economic policy uncertainty (EPU) index in the

	United Kingdom in 12 industries. These industries are (1) Mining, (2) Utilities, (3) Construction, (4) Manufacturing, (5) Wholesale Trade, (6) Retail Trade, (7)
	Transportation and Warehousing, (8) Information, (9) Professional, scientific and technical services, (10) Administrative and support and waste management
	and remediation services, (11) Arts, entertainment, and recreation, and (12) Accommodation and food services. All variables are defined in Appendix A. *, **,
	and *** indicate significance at the 10%, 5%, and 1% levels (two-tailed), respectively. T-statistics are in parentheses.

Table 5 :

 5 U.S. economic policy uncertainty and analyst behaviorsThis table presents the average estimated coefficients from regressions of measures of analyst behaviors on U.S. EPU. All variables are defined in Appendix A. *, **, and *** indicate significance at the 10%, 5%, and 1% levels (two-tailed), respectively. T-statistics are in parentheses.

		(1)	(2)	(3)	(4)	(5)
		Accuracy	Dispersion	Coverage	Upgrade	Downgrade
	Intercept	-0.035	0.011	-0.058***	-0.120***	-0.157***
		(-1.15)	(0.47)	(-2.63)	(-10.40)	(-13.00)
	US EPU	-0.042**	-0.004	0.072***	-0.013*	0.008
		(-2.44)	(-0.29)	(5.67)	(-1.91)	(1.05)
	ROA	0.342***	-0.230***	-0.067***	-0.032***	-0.047***
		(11.50)	(-9.53)	(-3.26)	(-2.76)	(-3.91)
	Leverage	0.039*	-0.031*	-0.006	-0.007	-0.019**
		(1.73)	(-1.76)	(-0.34)	(-0.89)	(-2.42)
	Smooth	-0.003*	0.001	0.001	-0.000	-0.000
		(-1.71)	(-0.92)	(-0.76)	(-0.10)	(-0.20)
	Size	0.009***	0.014***	0.336***	0.045***	0.050***
		(2.91)	(5.40)	(143.40)	(36.20)	(38.80)
	Sentiment	-0.029	0.032**	0.004	0.004	-0.001
		(-1.62)	(2.28)	(0.26)	(0.56)	(-0.20)
	MKT Volatility	-0.010**	0.010	-0.010*	0.010	0.010
		(-2.36)	(1.20)	(-1.94)	(0.74)	(0.79)
	Industry Fixed Effects	Yes	Yes	Yes	Yes	Yes
	N	11,157	10,899	12,243	11,613	11,613
	R 2	0.0204	0.0109	0.6653	0.1117	0.1245

Table 6 :

 6 European economic policy uncertainty and analyst behaviorsThis table presents the average estimated coefficients from regressions of measures of analyst behaviors on European EPU. All variables are defined in Appendix A. *, **, and *** indicate significance at the 10%, 5%, and 1% levels (two-tailed), respectively. T-statistics are in parentheses.

		(1)	(2)	(3)	(4)	(5)
		Accuracy	Dispersion	Coverage	Upgrade	Downgrad e
	Intercept	-0.046	0.033	0.038*	-0.099***	-0.131***
		(-1.63)	(1.48)	(1.85)	(-9.34)	(-11.90)
	European EPU	-0.026**	-0.017**	-0.008	-0.026***	-0.012***
		(-2.35)	(-2.02)	(-1.00)	(-5.96)	(-2.67)
	ROA	0.341***	-0.233***	-0.074***	-0.035***	-0.050***
		(11.50)	(-9.62)	(-3.59)	(-3.06)	(-4.14)
	Leverage	0.039*	-0.040*	-0.015	-0.010	-0.022***
		(1.73)	(-1.94)	(-0.94)	(-1.25)	(-2.77)
	Smooth	-0.003*	0.001	0.001	-0.000	-0.000
		(-1.69)	(0.92)	(0.71)	(-0.08)	(-0.21)
	Size	0.010***	0.014***	0.337***	0.046***	0.051***
		(3.01)	(5.58)	(143.0)	(36.70)	(39.00)
	Sentiment	-0.033*	0.034**	0.016	0.005	0.001
		(-1.81)	(2.42)	(1.19)	(0.76)	(0.18)
	MKT Volatility	-0.010**	0.010	-0.010*	0.010	0.010
		(-2.58)	(1.11)	(-1.65)	(0.43)	(0.77)
	Industry Fixed Effects	Yes	Yes	Yes	Yes	Yes
	N	11,157	10,899	12,243	11,163	11,613
	R 2	0.0204	0.0113	0.6645	0.114	0.125

Table 8 :

 8 Robust testThis table presents the average estimated coefficients from regressions of measures of analyst behaviors on U.K. EPU after geopolitical risk (GPR) and annual GDP growth (GDP growth) are controlled. All other variables are defined in Appendix A. *, **, and *** indicate significance at the 10%, 5%, and 1% levels (two-tailed), respectively. T-statistics are in parentheses.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
		Accuracy Accuracy Dispersion Dispersion Coverage Coverage	Upgrade	Upgrade Downgrade Downgrade
	Intercept	-0.060**	-0.048	0.025	0.041	0.044**	0.062*** -0.119*** -0.100*** -0.132***	-0.125***
		(-2.27)	(-1.51)	(1.22)	(1.63)	(2.32)	(2.72)	(-12.1)	(-8.25)	(-12.8)	(-9.81)
	UKEPU	-0.021*	-0.022*	-0.018*	-0.019*	0.001	-0.001	-0.030*** -0.032*** -0.021***	-0.022***
		(-1.68)	(-1.75)	(-1.82)	(-1.93)	(0.11)	(-0.05)	(-6.04)	(-6.30)	(-3.99)	(-4.07)
	GPR	-0.102	-0.082	-0.046	-0.023	-0.249*** -0.221*** 0.102*** 0.128***	0.001	0.009
		(-0.95)	(-0.75)	(-0.59)	(-0.28)	(-3.32)	(-2.85)	(2.67)	(3.22)	(0.001)	(0.22)
	GDP Growth		-0.003		-0.004		-0.005		-0.005**		-0.002
			(-0.67)		(-1.11)		(-1.42)		(-2.56)		(-0.91)
	ROA	0.342*** 0.343*** -0.232*** -0.231*** -0.077*** -0.076*** -0.031*** -0.030*** -0.049***	-0.049***
		(11.5)	(11.5)	(-9.58)	(-9.55)	(-3.71)	(-3.67)	(-2.66)	(-2.58)	(-4.08)	(-4.05)
	Leverage	0.042*	0.042*	-0.033*	-0.032*	-0.015	-0.014	-0.007	-0.006	-0.022**	-0.021***
		(1.86)	(1.88)	(-1.86)	(-1.82)	(-0.93)	(-0.88)	(-0.94)	(-0.83)	(-2.72)	(-2.67)
	Smooth	-0.003*	-0.003*	0.001	0.001	0.001	0.001	0.001	0.001	-0.001	-0.001
		(-1.67)	(-1.65)	(0.95)	(0.97)	(0.71)	(0.74)	(0.01)	(0.07)	(-0.16)	(-0.13)
	Size	0.010*** 0.009***	0.014***	0.014***	0.337***	0.337***	0.045*** 0.045***	0.051***	0.051***
		(2.96)	(2.95)	(5.55)	(5.52)	(143.4)	(143.7)	(36.4)	(36.3)	(39.2)	(39.1)
	Sentiment	-0.032*	-0.034*	0.036**	0.034**	0.006	0.003	0.017**	0.014*	0.007	0.006
		(-1.70)	(-1.77)	(2.46)	(2.29)	(0.42)	(0.23)	(2.29)	(1.93)	(0.96)	(0.83)
	MKT Volatility	-0.001**	-0.001**	0.001	0.001	-0.001	-0.001	0.001	-0.001	0.001	0.001
		(-2.00)	(-2.09)	(1.52)	(0.91)	(-0.90)	(-1.41)	(0.80)	(-0.34)	(1.35)	(0.85)
	Industry Fixed Effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	N	11,157	11,157	10,899	10,899	12,243	12,243	11,613	11,613	11,613	11,613
	R 2	0.0202	0.0203	0.0112	0.0114	0.6647	0.6648	0.1146	0.1152	0.1256	0.1257

It is a measure of policy-related economy uncertainty, comprising newspaper coverage, temporary federal tax code provisions, and reported disagreements among economic forecasters. Half of the weightings this index uses are from the news-based policy uncertainty index (which is based on the frequency of uncertaintyrelated words in 10 leading U.K. newspapers), and the following three measures each contribute one sixth of the weightings: the temporary federal tax code index; forecast disagreement of the Consumer Price Index; and forecast disagreement of federal, state, and local purchases.

We derive similar results from using different weights of the policy uncertainty index in the formation period.

We use the consensus analyst EPS forecast, the actual firm EPS, and the price per share in the last month to calculate the analyst forecast accuracy, following prior literature on analysts.

The International Monetary Fund addresses geopolitical uncertainties in the 2017 World Economic Outlook report. The report is available at https://www.imf.org/en/Publications/WEO/Issues/2017/09/19/worldeconomic-outlook-october-2017. The European Central Bank discusses the importance of geopolitical task in the Economic Bulletin in 2017. It is available at https://www.ecb.europa.eu/pub/pdf/ecbu/eb201704.en.pdf.

 10 We obtain the annual GDP growth from the website of the Organisation for Economic Co-operation and Development (OECD).

Appendix A: Variable definitions

Accuracy

Earnings forecast accuracy, which is calculated as -1 times the absolute value of the difference between the mean analyst earnings forecast per share over a year and firms' actual earnings per share divided by the price per share. Dispersion Earnings forecast dispersion, which is calculated as the standard deviation of all individual analyst earnings forecasts issued for a firm over a year. Individual analyst earnings forecasts are obtained from I/B/E/S detail file.

Logfollow

Analyst coverage (number of analysts following a firm in a year), which is measured as the natural logarithm of the sum of one plus the number of analysts following a firm.

Lognumrecup

Number of recommendations revised upward (upgrade), which is measured as the natural logarithm of the sum of one plus the number of recommendations that have been revised upward. Lognumrecdown Number of recommendations revised downward (downgrade), which is measured as the natural logarithm of the sum of one plus the number of recommendations that have been revised downward.

EPU

The weighted-average value of monthly EPU indices of the most recent two months. The EPU index is the economic policy uncertainty index constructed by [START_REF] Baker | Measuring economic policy uncertainty[END_REF].

Sentiment

Investor sentiment, which is measured as the standard deviation of the consumer confidence index over the year. The consumer confidence index is obtained from the OECD.

MKT Volatility

Stock market volatility, which is measured as the standard deviation of the FTSE index over the year. ROA Return on assets, which is calculated as the income before extraordinary items divided by average total assets. Leverage Firm leverage, which is calculated as total long-term debt divided by average total assets.

Smooth

Income smoothing, which is measured as the standard deviation of earnings divided by the standard deviations of cash flows from operations, where earnings and cash flows are scaled by lagged total assets (Baik et al., 2017). Size Firm size, which is calculated as the natural logarithm of total assets.


Xing Liu
email: xing.liu@univ-lorraine.fr

Cédric Laurent

Qiaoyue Du

Laurie Targa

Ghislaine Cauchois

Yun Chen

Xiong Wang

Natalia De Isla
email: natalia.de-isla@univ-lorraine.fr

Mesenchymal stem cell interacted with PLCL braided scaffold coated with poly-l -lysine/hyaluronic acid for ligament tissue engineering

Keywords: Poly (L-lactide-co-caprolactone), braided scaffold, mesenchymal stem cell, polyelectrolyte multilayer films, ligament tissue engineering

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Ligament injuries are among the highest incidence of musculoskeletal disorders, [1][START_REF] Barber | Braided nanofibrous scaffold for tendon and ligament tissue engineering[END_REF][START_REF] Ge | Biomaterials and scaffolds for ligament tissue engineering[END_REF][START_REF] Leong | Current tissue engineering strategies in anterior cruciate ligament reconstruction[END_REF] especially for the people who commonly do sports, rigorous physical activities, suffer acute trauma, or for aged people. The main strategy for repairing injured ligament consists in a surgical intervention called ligamentoplasty. Grafts [START_REF] Lavoie | Patient satisfaction needs as related to knee stability and objective findings after ACL reconstruction using the LARS artificial ligament[END_REF][START_REF] Samuelsson | Treatment of Anterior Cruciate Ligament Injuries With Special Reference to Graft Type and Surgical Technique: An Assessment of Randomized Controlled Trials[END_REF][START_REF] Laurencin | Ligament tissue engineering: An evolutionary materials science approach[END_REF] used in such interventions include autografts, allografts, and artificial substitutes. These therapies are subject to constant improvements, [START_REF] Lavoie | Patient satisfaction needs as related to knee stability and objective findings after ACL reconstruction using the LARS artificial ligament[END_REF][START_REF] Yagi | Double-bundle ACL reconstruction can improve rotational stability[END_REF] resulting in the increase of joint stability, recovered mechanical properties, and patients' overall satisfaction. However, such surgeries are still associated with drawbacks [START_REF] Kaeding | Allograft versus autograft anterior cruciate ligament reconstruction: predictors of failure from a MOON prospective longitudinal cohort[END_REF][START_REF] Carlisle | Technical considerations in revision anterior cruciate ligament surgery[END_REF] including donor site morbidity, immune rejection, or limited graft sources. With the rise of tissue engineering, ligament tissue engineering could constitute a novel, attractive and alternative approach to recover the function of injured ligament. 1,[START_REF] Ge | Biomaterials and scaffolds for ligament tissue engineering[END_REF][START_REF] Laurencin | Ligament tissue engineering: An evolutionary materials science approach[END_REF] Scaffolds with the architectures of sponges, knitted fibers, meshes, hydrogels, wired yarns, and braided fibers have already been reported [START_REF] Ge | Biomaterials and scaffolds for ligament tissue engineering[END_REF][START_REF] Guarino | Bioactive scaffolds for bone and ligament tissue[END_REF][START_REF] Yang | The design of scaffolds for use in tissue engineering. Part I. Traditional factors[END_REF] in ligament tissue engineering. Despite their benefits, they also suffer from some drawbacks, [START_REF] Ge | Biomaterials and scaffolds for ligament tissue engineering[END_REF][START_REF] Yang | The design of scaffolds for use in tissue engineering. Part I. Traditional factors[END_REF][START_REF] Cooper | Fiber-based tissue-engineered scaffold for ligament replacement: design considerations and in vitro evaluation[END_REF] such as insufficient mechanical properties, brittle behavior, or lack of functional groups for molecular signaling. In our previous studies, [START_REF] Laurent | Towards a tissue-engineered ligament: Design and preliminary evaluation of a dedicated multi-chamber tension-torsion bioreactor[END_REF][START_REF] Laurent | Morphological characterization of a novel scaffold for anterior cruciate ligament tissue engineering[END_REF] PLCL has been used to develop a novel multilayer braided scaffold. It presents a porous structure for cell or tissue ingrowth, nutrient exchange, and post-graft degradation. Thanks to the adjustable geometrical parameters of this architecture (e.g. number of layers, fiber diameter and braiding angle), the mechanical strength could also be adjusted to fulfill requirements of different ligaments. PLCL is a synthetic biodegradable co-polymer with high elasticity consisting of polylactide (PLLA) and polycaprolactone (PCL). It has already been pointed out [START_REF] Lee | Regeneration of Achilles' tendon: the role of dynamic stimulation for enhanced cell proliferation and mechanical properties[END_REF] to support tendon cell proliferation, and has been widely applied in tissue engineering because of its good biocompatibility and mechanical properties.

Seeding cells [START_REF] Ge | Selection of cell source for ligament tissue engineering[END_REF][START_REF] Chen | Human bone marrow stromal cell and ligament fibroblast responses on RGD-modified silk fibers[END_REF][START_REF] Cooper | Evaluation of the anterior cruciate ligament, medial collateral ligament, achilles tendon and patellar tendon as cell sources for tissue-engineered ligament[END_REF] could also contribute to the functionality of tissue engineered constructs by encouraging its colonization and extra cellular matrix (ECM) synthesis.

Ligament fibroblasts [START_REF] Cooper | Evaluation of the anterior cruciate ligament, medial collateral ligament, achilles tendon and patellar tendon as cell sources for tissue-engineered ligament[END_REF] may appear as natural candidate reparative cells for ligament tissue engineering, but there are limited by cell availability, quantity of donors and also differentiation capacity. Because of self-renew ability, differentiation potential, and bioactive molecule secretion activity, Mesenchymal Stem Cells (MSCs) have become a popular cell source applied in tissue engineering. [START_REF] Ge | Selection of cell source for ligament tissue engineering[END_REF][START_REF] Van Eijk | Tissue Engineering of Ligaments: A Comparison of Bone Marrow Stromal Cells, Anterior Cruciate Ligament, and Skin Fibroblasts as Cell Source[END_REF] In addition, MSCs have already been reported [START_REF] Ge | Selection of cell source for ligament tissue engineering[END_REF][START_REF] Van Eijk | Tissue Engineering of Ligaments: A Comparison of Bone Marrow Stromal Cells, Anterior Cruciate Ligament, and Skin Fibroblasts as Cell Source[END_REF] to proliferate better and produce more collagen than fibroblasts. Wharton's Jelly Mesenchymal Stem Cells (WJ-MSC) and Bone Marrow Mesenchymal Stem Cells (BM-MSC) were thus selected as cell sources in our present study.

In the last decades, Layer-By-Layer (LBL) technology has been widely used to modify the surface of biomaterials in order to improve the integration or add specialized properties to scaffolds. [START_REF] Tang | Biomedical applications of layer-by-layer assembly: from biomimetics to tissue engineering[END_REF][START_REF] Costa | Polyelectrolyte multilayered assemblies in biomedical technologies[END_REF] Examples of LBL biological applications include mediation of cellular functions to promote cell adhesion, proliferation, differentiation; biosensors; drugs or bioactive molecular delivery system, and biomimetics. The cellular adhesion and homogeneous cell distribution on the scaffold is a primary element in tissue engineering, and many studies [START_REF] Laurencin | Ligament tissue engineering: An evolutionary materials science approach[END_REF][START_REF] Sahoo | A bFGF-releasing silk/PLGA-based biohybrid scaffold for ligament/tendon tissue engineering using mesenchymal progenitor cells[END_REF] have emphasized the importance of growth factors to promote MSCs attachment, proliferation, and matrix synthesis in the process of ligament/tendon repair. In the light of the advantages of LBL technology for cell adhesion, and the demand of growth factor delivery system for tissue engineering, LBL modification was introduced in our study in order to promote a ligamentous tissue. Poly-L-lysine (PLL) and Hyaluronic Acid (HA) were selected [START_REF] Yamanlar | Surface functionalization of hyaluronic acid hydrogels by polyelectrolyte multilayer films[END_REF][START_REF] Hahn | Characterization of biocompatible polyelectrolyte complex multilayer of hyaluronic acid and poly-L-lysine[END_REF] as polycation and polyanion for PEM. HA, as a component of extracellular matrix, plays an important role in improving cell growth, differentiation and migration, but it has been observed to be non-adhesive to cells. [START_REF] Yamanlar | Surface functionalization of hyaluronic acid hydrogels by polyelectrolyte multilayer films[END_REF][START_REF] Pavesio | Anti-adhesive surfaces through hyaluronan coatings[END_REF] On the contrary, PLL has been shown [START_REF] Huang | Improved section adhesion for immunocytochemistry using high molecular weight polymers of L-lysine as a slide coating[END_REF][START_REF] Zhang | Graphene oxide/poly-l-lysine assembled layer for adhesion and electrochemical impedance detection of leukemia K562 cancercells[END_REF] to promote cell adhesion and it has widely been used for biomaterial coating and drug delivery. The pair of PLL/HA PEM was expected to be an effective system to improve MSCs behavior on scaffolds.

Prior to any incorporation of growth factors into a LBL-treated scaffold, it is crucial to study the effect of PLL/HA LBL modification on the behaviors of WJ-MSC and BM-MSC on PLCL braided scaffold. Therefore, in the present contribution the effect of LBL technology on MSCs proliferation, distribution, morphology, collagen synthesis, tenascin-C expression and migration were evaluated, as well the consequence of this treatment on scaffold topology, and mechanical properties.

METHODS AND MATERIALS Scaffold fabrication

PLCL (85/15) braided scaffold was fabricated according to previously described methods. [START_REF] Laurent | Towards a tissue-engineered ligament: Design and preliminary evaluation of a dedicated multi-chamber tension-torsion bioreactor[END_REF][START_REF] Laurent | Morphological characterization of a novel scaffold for anterior cruciate ligament tissue engineering[END_REF] Briefly, it consists of a multilayer braided structure, each layer being made up of 16 fibers of poly(lactide-co-ε-caprolactone) (PLCL, Purac Biomaterials) with a lactic acid/ε-caprolactone proportion of 85/15. The polymer was firstly processed into 170 µm fibers using a homemade extrusion machine at 180°C. Fibers were collected on bobbins, and 16 bobbins were installed on a maypole braiding machine (Composite & Wire Machinery, United States) to form braided scaffolds. Six layers were superposed in total, for a resulting scaffold of 96 fibers measuring around 4mm in diameter.

Scaffold modification with LBL technology

PLL (Sigma) and HA (Acros) were dissolved in the NaCl-Tris buffer (pH6.3) at a concentration of 1 mg/mL respectively. Scaffolds were cut into 1 cm-long parts. Firstly, scaffolds were immersed into PLL solutions for 7 min, then gently rinsed in NaCl-Tris buffer for 1 min. They were subsequently introduced into HA solution for 7 min, and then rinsed. Scaffold-blank (SB) refers to the scaffold without LBL modification; scaffold-PLL (SP) refers to scaffold modified only by PLL solution, and scaffold-PLL-HA-PLL(S1L)refers to one-layer modification. This configuration (pH6.3 and only one-layer modification) was selected based on preliminary studies briefly reported in supplementary material.

Scaffold characterization

Characterization of scaffold by Fourier Transform Infrared Spectroscopy (FTIR)

PLCL fibers were vacuum-dried at 60°C for 24 h, and cut into fine powders for the measurements by FTIR. The dry powders were mixed with Potassium bromide (KBr), and spectra were recorded on an FTIR spectrometer (Nicolet, USA) over the wavenumber range from 4000 to 600 cm -1 .

Detecting of PLL/HA coating by Atomic Force Microscopy (AFM) and Confocal

Laser Scanning Microscopy (CLSM)

The surface of modified fibers was characterized by AFM (Oxford instruments). PLCL fibers were cut into 1 cm and maintained on a glass slide. Surface topology was observed and recorded in contact mode.

PLL-FITC (Sigma) was used to replace PLL in PLL/HA system. Fibers were immersed in PLL-FITC/HA solution using the procedure described above, then detected by CLSM (Nikon).

Scaffold morphology by Scanning Electronic Microscopy (SEM)

Scaffolds were frozen with liquid nitrogen for 10 min, dried with vacuum oven for 7 h, then coated with gold and observed by SEM (TESCAN, Czech).

Mechanical properties of fibers

The mechanical properties of PLCL fibers were assessed using tensile tests performed with a Zwick Roell 2.5 device (Zwick, Germany), at a tensile speed of 0.1mm.s -1 . Six 10 cm-long fibers were tested in parallel for SB, SP and S1L (n=4). The following loading cycle was prescribed 1)1% strain then unloading 2) 3% strain then unloading 3) increasing strain up to failure. Young's modulus was measured for each sample from the slope of the stress-strain curve just after the first unloading cycle (at 1% of strain).

Mesenchymal Stem Cell isolation and expansion

Human bone marrow and human umbilical cords were supplied by CHRU Hospital and Maternity Hospital (Nancy, France) with the informed consent of donors. Firstly, 25 mL α-MEM (Lonza) medium containing 10% fetal calf serum (FCS, Dominique Dutscher), 2 mM L-glutamine (Sigma), 100 U/mL penicillin /streptomycin (Gibco) and 1 μg/mL amphoterin B (Gibco) was added to 20 mL bone marrow, and centrifuged 300g for 5 min. The supernatant was removed, and cells were seeded on tissue culture plates at the density of 5×10 4 cells/cm 2 , and then incubated in 37°C, 5% CO2, 90% humidity until the cells reach 70% -80% confluence.

Human umbilical cord was washed with 70% ethanol and cut into 3-4 cm long samples in the HBSS buffer. The cord was opened to peel off two arteries and one vein; the jelly was torn off from cordon and cut into fine pieces in HBSS buffer. After 7 days, jelly was removed, and the culture medium was changed twice a week until the cells reach 70% -80% confluence. All MSCs used for assays were in passage 3 (P3). Before seeding cells on scaffolds, the quality of MSCs was controlled by testing biological properties as CFU test, senescence test and phenotype characterization. (data shown in supplementary material)

MSC culture on scaffold

Firstly, scaffolds were immersed in ethanol for 30 min, then sterilized with UV for 30 min. MSCs were distributed on scaffold (3 × 10 5 cells, 50 μL/scaffold). MSC-scaffolds were kept in incubator for 30 min, then supplied with 0.95 mL α-MEM complete medium including 100 μg/mL ascorbic acid (Sigma) and cultured for two weeks.

Evaluation of cell biocompatibility on the scaffolds

Cell metabolic activity of MSCs on scaffold

Alamar Blue (AB) tests were performed on day 1, 3, 5, and 7. 1 mL 10% AB work solution (Thermo Fish scientific) was added to each scaffold, and incubated for 4 h.

The absorbance was measured at 570 nm and 600 nm (n=3). Negative control was AB working. The percentage of Alamar Blue Reagent reduction was calculated according to manufacturer's instructions.

Cell morphology and distribution on scaffold

Scanning Electronic Microscopy (SEM)

MSC-Scaffolds were firstly fixed with 2.5% glutaraldehyde at 4°C overnight on day 14, then washed with PBS three times. MSC-scaffolds were frozen with liquid nitrogen for 10 min, dried with vacuum oven for 7 h, and then images were taken by SEM.

Confocal microscopy

Firstly, MSC-scaffolds were fixed with 1% paraformaldehyde for 10 min at room temperature, then washed with PBS three times. Permibilization treatment was performed by adding 1 mL 0. Quantification of cell nuclear elongation was performed by Image J software version 1.51k. The length was defined as the longest distance through nucleus and the width was defined as the distance perpendicular to the length. Cell nuclear aspect ratio was calculated by dividing the length to the width. The area of cell nucleus (n ≥ 100 from more than three images) was measured by Image J 1.51k.

Extracellular matrix synthesized by MSCs on scaffold

Cell Chemotactic assay

PLCL films were fabricated by hot-processing method, and modified with PLL/HA PEM as PLCL braided scaffold treatment. Boyden chamber assay was performed for migration test by using a transwell (Corning® FluoroBlok™) with 0.8 μm pore fluorescence blocking PET track-etched membrane insert according to manufacturer's instructions. The downside of insert was coated with 100 μL collagen for 30 min.

MSCs were starved 24 h before cell migration assay. SB, SP, and S1L film with 500 μL α-MEM complete medium were introduced to each reservoir. 1.5×10 5 cell suspension in α-MEM without FCS (200 μL) was added into each insert. After 24 h, cells were stained with calcein AM (Thermo Fish scientific) (1/1000) for 30 min, then washed with PBS three times. Cell distribution was observed by fluorescence microscopy and fluorescence intensity was measured by fluorescence spectrophotometer.

RESULTS

Physiochemical properties of PLCL braided scaffold with/without PLL/HA modification

Fourier-transform infrared spectroscopy of PLL/HA LBL modification

The structure of PLCL fiber with/without PLL/HA modification are shown in Fig. 1 by FTIR. The spectra at 2990 cm -1 (PLLA), 2945 cm -1 (PLLA) and 2860 (PCL) cm -1 demonstrated [START_REF] Kalpan | Fast degradable poly (L-lactide-co-e-caprolactone) microspheres for tissue engineering: synthesis, characterization, and degradation behavior[END_REF] the presence of alkyl group of copolymer PLCL. Bands at 1130 cm -1 and 1065 cm -1 corresponded to ester backbone of PLA in scaffold SP and S1L respectively. [START_REF] Xu | Preparation of chitosan/PLA blend micro/nanofibers by electrospinning[END_REF] The wavelength of PLL in SP was present as anion group [START_REF] Jeong | In vivo biocompatibilty and degradation behavior of elastic poly(l-lactide-co-ε-caprolactone) scaffolds[END_REF] at 1600 cm -1 .

The wavelength of HA in PLL/HA-PLL was present as carboxylate anion group 31 at 1616 cm -1 and 1413 cm -1 . The wavelength of amine groups of PLL [START_REF] Jeong | In vivo biocompatibilty and degradation behavior of elastic poly(l-lactide-co-ε-caprolactone) scaffolds[END_REF] was shown at 1630 cm -1 in PLL/HA-PLL.

Topology of PLL/HA LBL modification.

PLL-FITC was observed to cover the fiber homogeneously by CLSM (Fig. 2a), and from SP to S1L the fluorescence intensity was increased. As shown in Fig. 2b, the surface of fiber was globally flat and smooth. Irregular polygonal structures on the surface of SP were deduced to correspond to PLL molecules, while the "particles" structures on S1L were attributed to HA molecules by AFM.

Morphology of scaffold

SEM images of PLCL braided scaffold in Fig. 3a indicated a porous and braided structure, and this multilayer structure was not influenced by PLL/HA modification.

For SB (scaffold without modification), the fibers kept the same texture from original fabrication, while for SP and S1L the fibers presented a relatively smooth surface after modification. The architecture of this multi-layer braided scaffold is illustrated in Fig. 3b: the scaffold was composed with six layers shown in six different colors ( 16braided fibers each layer).

Mechanical properties

Typical stress-strain curves as well as Young's modulus evolution are exhibited in Fig. 4.

Young's modulus of SB,SP and S1L were respectively 1616 ± 643 MPa, 1608 ± 156 MPa and 1758 ± 470 MPa. No significant changes in Young's modulus were therefore observed. The three types of fibers broke for a strain around 15-25% with no significant effect of surface modification.

Biocompatibility of MSC-scaffold in vitro MSC metabolic activity on scaffold

Cell metabolic activity was detected by AB and is presented in Fig. 5. The metabolic activity of WJ-MSC on SB, SP and S1L increased of 24.2%, 19.64%, and 12.82% respectively from day 1 to day 7. For BM-MSC, the activity increased of 21%, 15.77% and 15.95% respectively on SB, SP and S1L from day 1 to day 7. Although cell metabolic activities were improved both for WJ-MSC and BM-MSC from day 1 to day 7, the cell metabolic activities decreased as the surface treatment went from SB to SP and S1L, especially for WJ-MSC.

MSC morphology and location on the scaffold

The morphology of MSCs on the scaffold by SEM (Fig. 6) showed that cells gathered to form cell sheets between fibers, and also indicated ECM synthesis. Fluorescent images (Fig. 7) displayed a spindle and elongated shape of MSCs along the fibers. MSCs tended to grow along the fiber direction in a homogeneous manner, migrated and proliferated across fibers to form cell bridges. Quantification of morphological changes of nucleus induced by scaffolds is illustrated in Fig. 8. Mean nuclear area of WJ-MSC (224.7 µm²) was smaller (p < 0.05) than BM-MSC (246.9 µm²) on tissue culture polystyrene (TCPS). For WJ-MSC, the mean nuclear area did not change between cells on scaffold and cells on TCPS. In contrast, for BM-MSC, the mean nuclear area decreased for cells on SB and S1L compared to cell nucleus on TCPS. The nuclear aspect ratio (length/width) of cells significantly increased (p < 0.05) comparing to cell nucleus on TCPS for both WJ-MSC and BM-MSC.

Extracellular matrix expression by MSC on the scaffold

Results concerning collagen and tenascin-C expression are shown in Fig. 9. Green points on the scaffold correspond to collagen type I, collagen type III and tenascin-C synthesized by WJ-MSC and BM-MSC respectively on day 14. It was observed that both types of collagens and tenascin-C were substantially secreted, independently of the surface modification.

Chemotactic properties of scaffold on MSC

Cell migration was performed to evaluate the effect of scaffold and scaffold modification on MSCs chemotaxis. As observed on fluorescent images showed in Fig. 10, the number of migrated BM-MSC tended to be higher than WJ-MSC induced by SB, SP and S1L, and this was confirmed by fluorescent intensity. It also demonstrated that MSCs chemotaxis was most activated under the stimulation of S1L for WJ-MSC and BM-MSC by fluorescent spectrophotometer.

DISCUSSION

PLCL [START_REF] Jeong | In vivo biocompatibilty and degradation behavior of elastic poly(l-lactide-co-ε-caprolactone) scaffolds[END_REF] is an elastomeric synthetic copolymer combining biocompatible, degradable PLLA and PCL, resulting in desirable mechanical properties. It is of great interest in tissue engineering, [START_REF] Laurent | Towards a tissue-engineered ligament: Design and preliminary evaluation of a dedicated multi-chamber tension-torsion bioreactor[END_REF][START_REF] Vaquette | Aligned poly(L-lactic-co-e-caprolactone) electrospun microfibers and knitted structure: A novel composite scaffold for ligament tissue engineering[END_REF] because of its biocompatibility, biodegradability, and easy-processing. Vuornos et al [START_REF] Vuornos | Human adipose stem cells differentiated on braided polylactide scaffolds is a potential approach for tendon tissue engineering[END_REF] compared a foamed PLCL(70/30) structure with a braided poly(L/D)lactide (PLA, 96L/4D) scaffold for tendon tissue engineering, and PLCL was judged to be excessively elastic to form a braided structure in their study. On the contrary, our group developed a controllable method to fabricate a uniform, porous, braided structure based on elastic PLCL (85/15). Noteworthy, the number of fiber layers could be adjusted to fulfill the mechanical requirements of different ligaments. Moreover, PLL/HA layer-by-layer system was reported to be one of the classical PEM for cell attachment, proliferation and ECM synthesis in many works. [START_REF] Yamanlar | Surface functionalization of hyaluronic acid hydrogels by polyelectrolyte multilayer films[END_REF][START_REF] Hahn | Characterization of biocompatible polyelectrolyte complex multilayer of hyaluronic acid and poly-L-lysine[END_REF] In our study, we introduced PLL/HA PEM on a braided PLCL scaffold for the first time and confirmed by FTIR that PLL and HA were deposited on the surface of PLCL fibers [START_REF] Kalpan | Fast degradable poly (L-lactide-co-e-caprolactone) microspheres for tissue engineering: synthesis, characterization, and degradation behavior[END_REF][START_REF] Xu | Preparation of chitosan/PLA blend micro/nanofibers by electrospinning[END_REF][START_REF] Amorim | Hyaluronic acid/poly-L-lysine bilayered silica nanoparticles enhance the osteogenic differentiation of human mesenchymal stem cells[END_REF] . After the replacement of PLL by PLL-FITC, the fluorescence intensity was increased from SP to S1L by CLSM. A polygonal and particle-like structure of PLL and HA were additionally detected on scaffold by AFM.

The interaction between cells and biomaterials is influenced [START_REF] Lutolf | Synthetic biomaterials as instructive extracellular microenvironments for morphogenesis in tissue engineering[END_REF] by physicochemical properties of scaffold (surface topography, surface toughness, wettability, stiffness, and crystallinity), and also cell sources. PLL/HA modification could therefore influence the scaffold surface microenvironment [START_REF] Burke | pH-Responsive Properties of Multilayered Poly(l-lysine)/Hyaluronic Acid Surfaces[END_REF] as local mechanical, wettability, toughness and so on. In a non-reported preliminary study, we firstly compared the deposition order of PLL and HA by testing cell metabolic activity, and the results demonstrated that PLL constitutes a cell-adhesive layer while HA is cell-repellent layer. PLL/HA polyelectrolyte films have been previously reported [START_REF] Burke | pH-Responsive Properties of Multilayered Poly(l-lysine)/Hyaluronic Acid Surfaces[END_REF] to be a pH-responsive PEM, and both pH6.0-6.5 and pH7.4 have been applied for PLL/HA bilayer deposition prior to the present study [START_REF] Yamanlar | Surface functionalization of hyaluronic acid hydrogels by polyelectrolyte multilayer films[END_REF][START_REF] Burke | pH-Responsive Properties of Multilayered Poly(l-lysine)/Hyaluronic Acid Surfaces[END_REF] . Based on these published data, the effects of pH (pH6.3 and pH7.4) on PEM deposition was assessed and different PLL/HA bilayers numbers were compared (reported in supplementary material) in order to optimize LBL modification on scaffold for both WJ-MSC and BM-MSC. As a result, for both MSCs, cell proliferation did not increase over one layer modification. Moreover, pH 6.3 for PEM deposition greatly encouraged MSCs metabolic activity, especially for WJ-MSC (reported in supplementary material). Consequently, in the present contribution, SB, SP and S1L made in pH6.3 were selected.

Cell attachment, proliferation, ECM synthesis and migration are a series of prerequisite activities for tissue regeneration. It is also modulated by bioactive molecular such as growth factors. In the present study, MSCs attached well on scaffolds as shown by the formation of cell sheets. There was no significant difference of cell attachment between non-modified and modified scaffolds. Cell cytoskeleton organization has been previously reported [START_REF] Treiser | Cytoskeleton-based forecasting of stem cell lineage fates[END_REF] to relate to cell fate on scaffold, to be beneficial to predict cell commitment, and it also indicates cell senescence. Senescent mesenchymal stem cells were characterized with enlarged, irregular morphology, following with the decrease of phenotype and differentiation abilities [START_REF] Wagner | Replicative Senescence of Mesenchymal Stem Cells: A Continuous and Organized Process[END_REF] . Fluorescent images reported in the present study presented a spindle-shape cell cytoskeleton covering the fibers. The nuclear size and shape were also related to the physiological process [START_REF] Chen | Cell shape dependent regulation of nuclear morphology[END_REF] as cell senescence, differentiation, and diseases. The quantitative data of cell nuclear morphology indicated an elongated and relatively spindle-like nuclear shape with decreased nuclear area while increased aspect ratio for WJ-MSC and BM-MSC on scaffold than on TCPS. Cells grew along the longitudinal direction of the fibers which could be an adaptable, resistant cell-scaffold system to response to mechanical stimulation. The results of Alamar Blue assay indicated that cell proliferated well on scaffold, and the metabolic activity did not show significant change after PLL/HA scaffold modification for WJ-MSC or for BM-MSC. Collagen constitutes the main component of native ligament tissue [START_REF] Altman | Cell differentiation by mechanical stress[END_REF] , and its high expression is a basic requirement for the formation of ligament-like extracellular matrix. In addition, Tenascin-C has been shown to be a promising specific indicator [START_REF] Altman | Silk matrix for tissue engineered anterior cruciate ligaments[END_REF] of ligament ECM, and it has been reported [START_REF] Jarvinen | Mechanical loading regulates the expression of tenascin-C in the myotendinous junction and tendon but does not induce de novo synthesis in the skeletal muscle[END_REF] to be highly expressed in regenerative process of musculoskeletal tissue, and to provide elasticity to tissues in reaction to heavy tensile loading. In the present study, Collagen I, collagen III and tenascin-C were secreted by BM-MSC and WJ-MSC on the scaffold within 14 days, which could have been encouraged by ascorbic acid in the medium. This pointed out that BM-MSC-scaffold and WJ-MSC-scaffold had potential in the future to form ligamentous tissue. Additionally, other specific indicators as tenomodulin and scleraxis could have been characterized, especially during a long-term culture including mechanical or biological stimulation.

Cell migration is a crucial step in the tissue healing process since cells firstly migrate to the injury site or target area. Consequently, migration was assessed in present study and our results showed that chemotaxis of WJ-MSC and BM-MSC was activated by SB, SP and S1L. This migration was even more pronounced for one layer scaffold, compared to SB and SP. This may show a possibility to recruit MSC for in vivo implantation. These cell-scaffold constructs showed satisfying biocompatibility, and the effect of growth factors incorporation into this PEM layer will be assessed in the forthcoming developments.

CONCLUSION

A PLCL braided scaffold with PLL/HA modification was designed in our study. The mechanical, morphological and biological results concerning the activity of WJ-MSC and BM-MSC and the ECM synthesis showed that it may constitute a promising scaffold for ligament tissue engineering. The incorporation of LBL increased cell chemotaxis, which opens interesting perspectives for in vivo application. In the future, the developed LBL technology will be used to incorporate growth factors on PLCL braided scaffold in order to encourage tissue growth and the regeneration of a functional ligamentous tissue.

  5% triton-PBS to each well for 20 min and washed with PBS three times. 250 µL Alexa 488 Phalloidin (Life technologies) solution was supplied to each MSC-scaffold for 45 min, then washed with PBS three times. 200 µL DAPI (1/2000) (Sigma) was added to each scaffold for 15 min, and washed with PBS three times. Images were taken by confocal microscopy (Leica SP5-CFS).

After 2

 2 weeks, MSC-scaffolds were firstly fixed with 1% paraformaldehyde for 10 min, then washed with PBS three times. 5% PBS-BSA blocking buffer was added to scaffolds for 30 min. The first antibody anti-collagen type I (CALBIOCHEM), collagen type III (Sigma) and anti-tenascin-C (Abcam) were added (250 µL) to scaffolds for 1 h, then washed with PBS three times. The second antibody anti-Rabbit IgG-Alexa488 (1/50) (Life technologies) and anti-mouse IgG-Alexa488 were distributed (250 µL) to scaffold for 45 min, then washed with PBS three times. 200 µL DAPI (1/2000) was added to each scaffold for 15 min. MSC-Scaffolds were washed with PBS three times, and observed by fluorescence microscopy. Negative controls were performed as MSC with only second antibody, and positive controls were tested as fibroblast with anti-collagen I , collagen III and tenascin-c and second antibody ( data shown in supplementary material).

REFERENCE 1 .

 1 Khan W. Ligament tissue engineering. Regen Strateg Treat Knee Jt Disabil.

Fig1.

  Fig1. FTIR of PLL/HA modified on PLCL scaffold (SB:PLCL scaffold blank, SP:

Fig3.

  Fig3. Morphology of PLCL scaffold and PLCL scaffold modified with PLL/HA by

Fig4.

  Fig4. Mechanical test of PLCL fibers (SB:PLCL fiber blank, SP: PLCL fiber-PLL, S1L:

Fig5.

  Fig5. Migration of WJ-MSC and BM-MSC induced by PLCL scaffold, scaffold-PLL

Fig6.

  Fig6. Reduction of AB of WJ-MSC and BM-MSC on PLCL scaffold (SB:PLCL

Fig7.

  Fig7. Morphology and distribution of WJ-MSC and BM-MSC on scaffold by SEM.

Fig8.

  Fig8. Morphology and distribution of WJ-MSC and BM-MSC on scaffold by

Fig9.

  Fig9. Collagen I and collagen III expression by WJ-MSC and BM-MSC on PLCL