The Effects of News Media Coverage on Investing

Transcription

1 Sophistication, News and Individual Investor Trading TOMAS THÖRNQVIST JOB MARKET PAPER ABSTRACT This paper investigates the effect of individual investor sophistication on the way they respond to new information in their stock trading decisions. I employ a 9-year panel of all the Swedish households together with a new dataset containing all the articles published in all the Swedish periodicals during the same time period. I show that while sophisticated investors trade more frequently unconditionally, unsophisticated investors trade more frequently in response to news. I provide evidence suggesting that sophisticated investors are more likely to use complex information that requires higher levels of reading comprehension, which leads to better stock-picking performance. Finally, I find that sophistication reduces transaction sizes and that while, on average, males trade more often and have larger individual transactions, sophistication reduces that difference between genders. JEL classifications: G02, G11 G12, D10, D14 This paper was written as part of my PhD thesis at the Finance Department at the Stockholm School of Economics. I owe special thanks to my supervisor Paolo Sodini for his ongoing support and valuable comments. This paper has benefited from the insightful input of Paolo Sodini, Bo Becker, Fransesco Sangiorgi, Laurent Bach, Per Strömberg, Magnus Dahlquist, Sebastien Betermier, Mariana Khapko, and Valeri Sokolovski. I would also like to thank seminar participants at the Finance Department at the Stockholm School of Economics as well as participants at the National Finance Workshop (2015). Financial support from the Tom Hedelius Foundation is gratefully acknowledged. 1

2 I. Introduction The excessive trading of households has been described as one of the great puzzles in financial economics (Grinblatt and Keloharju, 2001; Hong et al., 2014). It has been shown that excessive trading reduces the return of retail investors by as much as 3.8 percentage points (Barber et al., 2009). Existing literature finds convincing empirical evidence linking excessive trading to demographic characteristics, behavioral biases, and news media consumption 1. In this paper, using a detailed 9-year panel of the entire Swedish population together with a new dataset containing the unabbreviated textual content of every article published in Swedish periodicals during the same period, I ask how demographic characteristics and behavioral biases interplay with the effects of news media coverage. In their seminal paper Barber and Odean (2001) show that men trade as much as 45% more than women. Grinblatt et al. (2012) and Calvet et al. (2009a) show that investor sophistication is an important determinant of investor trading and rebalancing behavior. Sophisticated investors are less likely to be subject to the disposition effect, more aggressive about tax-loss sales, rebalance more frequently, and generally exhibit better trading performance. While, Engelberg and Parsons (2011) provide a causal link between news media coverage and individual investor trading volumes. In this paper I argue that in order to better understand household trading one needs to look at these aspects in combination. In particular, I investigate the effect of sophistication on how individual investors incorporate news in their trading decisions. The analysis is based on two large panels. First, a panel containing detailed and unaggregated demographic, portfolio, and transaction data for the entire Swedish population (a country with a population of roughly 9 million) during the 9-year period While the dataset is rich in demographic details, transactions are observed with daily frequency only for sales, while purchases are observed yearly. Using the sales data I calculate various measures of trading behavior, restricting my analysis to active traders I classify as sophisticated those investors that have attained the equivalent of a bachelor s degree. Second, the proxy for new information is based on a new dataset containing metadata and the unabbreviated textual content of every article published in all the Swedish periodicals. In total the dataset contains roughly 8 million articles. Using standard textual analysis methods, for each article in the dataset I determine if the topic of the article is one of the 412 companies traded on the Stockholm Stock 1 See for example Barber and Odean (2001); Grinblatt and Keloharju (2001); Engelberg and Parsons (2011). 2

3 Exchange, and if so, whether the article portrays the company in a positive or negative light. Since the analysis is based on sales I follow Tetlock (2007) and restrict my analysis to negative articles. This paper makes four contributions to the literature. First, I show that sophisticated investors are less likely to trade in response to new information. I flag transactions that occur within 5 days of a news article being published and estimate the effect of news on the sell-or-hold decision of each individual investor in the sample. Using the same measure of trading propensity I find that sophisticated investors tend to trade more often than unsophisticated investors unconditionally. This suggests that the trading motives of sophisticated investors are different from unsophisticated investors in that they tend to trade more for reasons such as portfolio rebalancing and less in response to new information. Second, I show that unsophisticated investors trade using relatively larger individual transactions. For each transaction in the sample I determine the fraction of stocks sold relative to the investor s total holding in the specific stock and estimate the effect of sophistication on relative transaction sizes. Hence, even though sophisticated investors trade more frequently each individual transaction is smaller. This is consistent with previous literature that finds that turnover, the compound effect of trading frequency and transaction sizes, is larger for unsophisticated investors (Anderson, 2013). Third, I provide evidence suggesting that sophisticated investors are more likely to use complex information that requires higher levels of reading comprehension, and that this leads to better stock-picking performance. Using LIX (Björnsson, 1968), the Swedish equivalent of standard readability measures such as Gunning-fog and Flesch-Kincaid (Flesch, 1948; Gunning, 1952; Kincaid et al., 1975), I divide my sample of articles into two groups, complex and non-complex, based on how difficult they are to comprehend. I find that while sophisticated investors, compared to unsophisticated investors, are less likely to trade following non-complex articles, they are more likely to trade following complex articles. Furthermore, comparing stock prices at the date of the transaction to the price of the same stock 12 months later I find that the probability to sell a stock that subsequently decreases in price is higher if the transaction occurred in response to a complex article, and particularly so for sophisticated investors. Fourth, I document a gender gap in individual investor reactions to news. I find that males trade more often and use larger individual transactions both in response to news as well as unconditionally. While the gender gap is present among both the unsophisticated and the sophisticated investors, sophistication greatly reduces the magnitude of this gap. While the gender gap is present and 3

4 sizeable in the propensity to trade as well as in relative transaction sizes, I find no systematic evidence for a gender gap with regards to stock-picking performance. In interpreting my findings I relate to the theoretical model of overconfidence in Odean (1998). Modelling overconfidence as a tendency to overvalue the relevance and precision of new information Odean (1998) predicts that overconfidence increases trading volumes and decreases returns. Furthermore, Odean (1998) predicts that markets underreact to abstract, statistical, and highly relevant information and overreact to salient, anecdotal, and less relevant information. I also relate to the empirical findings that investor sophistication (1) reduces behavioral biases and investor mistakes (Feng and Seasholes, 2005; Calvet et al., 2007, 2009a), and (2) is strongly linked to reading comprehension (Flesch, 1948; Gunning, 1952; Kincaid et al., 1975). I argue that with investor sophistication reducing behavioral biases such as overconfidence we would expect household trading behavior to be affected in two ways: (1) sophisticated investors, more likely to discount the relevance of new information, will be less likely to act on new information; (2) conditional on acting on new information, sophisticated investors are more cautious about the precision of the new information, and hence react less strongly. Since unsophisticated investors exhibit lower levels of reading comprehension we would expect unsophisticated investors to be less likely to act on new information that is complex in nature. Finally, the better stock-picking performance of sophisticated investors can be understood as a result of them being more selective in the news they choose to act on, as well as them being able to comprehend more complex information. The organization of the paper is as follows. Section (II) contains a discussion on related research. In Section (III) I describe the extensive dataset. Section (IV) contains details on the empirical methodology employed. In Section (V) I report and discuss the results. Section (VI) contains robustness checks and alternative interpretations. Section (VII) concludes. II. Related Research What do we know about individual investor trading and news? The normative advice of classical portfolio choice models (see, Markowitz (1952) or Merton (1969), for example) is that investors should respond to new information that results in a change of the estimates of expected risky asset excess returns and their covariance structure. However, recognizing the news is only a noisy signal of these uncertain parameters investors should rebalance and reallocate their 4

5 portfolio more cautiously (Pastor and Veronesi (2009)) and trade on new information only when it is profitable (Brunnermeier (2001)). When it comes to observed behavior, the household finance literature shows that while many individual investors who participate in the stock market reallocate their financial wealth rather infrequently (Agnew et al. (2003), Alvarez et al. (2012)), some individual investors trade to adjust their portfolio very often (Odean (1999), Barber and Odean (2000), Barber and Odean (2001)). The average household participating in the stock market trades excessively and underperforms (Linnainmaa (2011)). From the growing literature on news and financial markets, we also know that there is a a causal link between media coverage and subsequent abnormally high trading volumes among retail investors in the stock of the company under scrutiny (Engelberg and Parsons (2011)). In this section I discuss the literature on household trading, news and financial markets, and finally the growing literature on textual analysis in finance. A. Household Trading Analyzing trading patterns of investors with discount brokerage accounts, Odean (1999) concludes that these retail investors are trading excessively to the effect of reducing their returns and highlights overconfidence as a possible reason for the excessive trading. Barber and Odean (2000) quantify the penalty for overconfidence-motivated trading by showing that households that trade more frequently experience lower returns (11.4 percent) compared to the average investor (17.4 percent). In their seminal paper Barber and Odean (2001) demonstrate that excessive trading is most pronounced for a particular group of retail investors, namely men. Male trading volumes are on average 45% higher than those of females leading to an on average reduction in returns of 2.65 percentage points (1.72 percentage points for females). They link this to findings in the psychology literature where men have been shown to exhibit higher levels of overconfidence in their abilities. Using trading records from a Swedish discount broker Anderson (2013) finds that sophisticated households are less likely to exhibit overconfidence. While research based on brokerage accounts finds evidence of excessive trading, data on 401k accounts demonstrates inertia in asset allocations (Madrian and Shea (2001)). Agnew et al. (2003) show that 401k investors engage in only a limited trading activity in order to rebalance their portfolios. To reconcile these seemingly conflicting reports and obtain a better understanding of the motives behind household trading, researchers have turned to analyze large comprehensive datasets. Using household data from Sweden (Calvet et al. (2007), 5

6 Calvet et al. (2009a), Calvet et al. (2009b), Calvet and Sodini (2014)), Finland (Grinblatt and Keloharju (2000), Grinblatt and Keloharju (2001), Grinblatt and Keloharju (2009), Grinblatt et al. (2012)), and Taiwan (Barber et al. (2009)), household finance literature has been able to paint a more complete picture of investor trading behavior. Grinblatt and Keloharju (2000, 2009), and Barber et al. (2009) show that the average household trades excessively. Using comprehensive Finnish data, Grinblatt and Keloharju (2001) identify several biases in household trading behavior. They find that investors are subject to the disposition effect, engage in tax-loss selling and are affected by past returns. Grinblatt and Keloharju (2009) suggest that both overconfidence and the desire to gamble are the driving forces behind these results. However, there is substantial heterogeneity in both trading activity and performance. Calvet et al. (2009a) show that Swedish households engage in active rebalancing of their portfolios. They confirm the disposition effect, and show that wealthier households exhibit weaker tendency to sell winners. Feng and Seasholes (2005) show that while investor sophistication does not completely eliminate biases it reduces them significantly. Their findings are supported by Grinblatt et al. (2012) where the authors conclude that high IQ individuals are less likely to be subject to the disposition effect and more likely to exhibit better performance in their trading. The relation between stocks that are able to attract investor attention and investor buying behavior is highlighted in Barber and Odean (2008). Stocks that are in the news are more likely to be noticed and using historical news data from the Dow Jones News Service Barber and Odean (2008) infer that indeed investors are more likely to buy these stocks. However, their empirical investigation on news-induced trading lacks statistical power to allow for quantitative conclusions. B. News and Financial Markets News has been shown to affect prices in financial markets. One of the first papers to study the effect of the media on financial markets is Tetlock (2007) which shows that pessimism espoused in a popular Wall Street Journal column predicts downward pressure on market prices. Tetlock (2007) concludes that his results suggest that media drives investor sentiment and non-informational trading. Fang and Peress (2009) emphasize the role of media in dissemination of information. Even if media for the largest part does not produce original news, it helps to channel and spread information. Fang and Peress (2009) show that companies that experience little media coverage tend to outperform companies 6

7 with high media coverage. One of the explanations for this finding is the media coverage draws investor attention and increases recognition. For investors to act on information they first need to acquire it. Da et al. (2011) use aggregate search frequency in Google to test the theory of Barber and Odean (2008) that predicts attention-induced buying pressure for stocks that have been noticed by retail investors. They show that an increased number of Google searches for a specific company predicts subsequent higher stock prices in the company s stock. Apart from its role in delivering information to a wider audience, media might play a role in reducing information asymmetry between informed and uninformed investors. Tetlock (2010) finds support for this prediction in patterns of postnews returns and trading volume. News, on average, reduces return reversal and more so when it is accompanied by high trading volume. This is consistent with the prediction of models of asymmetric information and seems to suggest that release of news coincides with information more often than it coincides with liquidity shocks. The effect is not homogenous. The evidence points to news stories being more important for small and illiquid firms and the effect is stronger for news containing more content (as measured by earnings-related words). Engelberg and Parsons (2011), using geographical segmentation for identification, confirm the effect of media on individual investors by providing a causal link between media coverage and subsequent increased trading volumes. Media coverage affects trading of retail investors, increasing daily trading volume from 8% to nearly 50% depending on the specification. The effect is significant for both buying and selling activity. Using a different identification strategy, namely strikes, Peress (2014), confirms a causal impact of the media on trading volume. On days when the media was on strike trading volume fell by 12% on average. The evidence points to the media s influence on individual investors and its role in the dissemination of information. C. Textual Analysis in Financial Research Finally, this paper is related to the growing area of financial research that employs advanced textual analysis. Introduced by the pioneering work of Frazier et al. (1984); Antweiler and Frank (2004); Das and Chen (2007); Tetlock (2007); Li (2008) textual analysis in financial research has become an important area. The advent of the internet has made vast amounts of textual data available to researchers and recent technological advances has made the computing power required to process large amounts of textual data available to researchers without access to super computers. In addition to being the first paper to identify an effect of news on financial 7

8 markets Tetlock (2007) was also one of the first papers to utilize textual analysis in financial research. He uses textual analysis to quantify the level of pessimism espoused in a popular Wall Street Journal column and shows that increased pessimism predicts downward pressure on market prices. In this paper I use many of the same methods used in this pioneering work. In a series of papers Loughran and McDonald (2011, 2014, 2015) make extensive use of textual analysis in their financial research. Loughran and McDonald (2014) introduces the concept of readability as a factor in how investors respond to textual content. Loughran and McDonald (2015) shows that the Harvard IV-4 wordlist that has been used extensively in the literature for the purpose of sentiment analysis is ill-suited for financial research and propose a new word list where words such as liability are not classified as negative. Finally, several papers have turned their attention to social media and its effects on aggregate returns and trading volumes. For example, Bollen et al. (2011), use textual analysis to quantify the current mood of a large number of twitter users and find that this predicts future aggregate market returns. III. Data This paper is the first to use an entirely new dataset containing metadata and the unabbreviated textual content of all the articles published in all the Swedish periodicals. This dataset is used together with another data set that contains highly disaggregated data on the entire Swedish population. The data on households was collected for taxation purposes and is hence of very high quality. Statistics Sweden, a government agency, has a mandate to collect extensive data on all individuals that either live in Sweden, are Swedish citizens, or own assets in Sweden. The dataset contains detailed demographic, portfolio, and transaction data for the 9-year period on all Swedish individuals (a country of roughly 9 million people). The demographic data is available on a yearly basis and contains variables such as age, education, location of residence, family ties, and also other information such as salary income and real estate wealth. Portfolio holdings are reported on a yearly basis. For each individual all financial assets held outside retirement accounts are reported on a per security basis. The lack of data on retirement accounts and the significance thereof is discussed below. For each individual and security I observe the number of securities held as well as the security s ISIN identifier which allows us to easily match the data with other sources. Finally there is data on all the securities 8

9 sold by an individual during the year. For each individual and transaction I observe the ISIN identifier of the stock sold, the number of stocks sold as well as the total transaction value. Notably the date of the transaction is not directly observed but can be inferred. The method used to infer transaction dates for sales is discussed in detail below. Throughout the paper I will consider stocks traded on the Stockholm Stock Exchange (SSE). For the period under consideration 472 stocks in 406 companies were traded on the SSE for either the entire period or part of the period. Since some companies have multiple classes of stock the number of companies is smaller. For each of these stocks I have access to daily detailed market data with closing prices as well as the highest and lowest prices paid for each stock traded. The stocks are identified with the ISIN identifier also used in the household data which facilitates effortless merging of the two datasets. The dataset on periodicals contains all the articles published in all the printed periodicals. For the sample period under consideration this represents roughly 7.3 million articles. Notably the dataset does not contain articles published on the internet, however I believe this to be of limited concern since during this period the internet versions of these publications were largely subsets of the printed versions. The dataset contains the full unabbreviated textual content of all of these articles together with meta data describing in which periodical the article was published and the date of publication. The process with which I convert these articles into data suitable for econometric analysis is described below. A. Pension System The Swedish pension system consists of five separate parts. These parts can be classified into three groups depending on whether the funds come from the government through taxes, from the employer or from the individual directly. The public pension system differs depending on whether the retiree was born before or after The system for people born before 1938 consists only of defined benefits, whereas the system for people born during or after 1938 consists of both defined benefits and defined contribution components. With respect to the latter system, 16% of earnings go to the defined benefit plan, whereas 2.5% go to the defined contribution plan. The defined contribution plan, PPM, allows the individual to decide where he or she wants to invest his or her pension money from a menu of funds with different risk and return characteristics. Employer-provided pensions are widespread in Sweden, with approximately 90% of employees receiving some sort of pension benefits as part of their employ- 9

10 ment package, according to the Swedish Pensions Agency. The amount placed in these employer-provided schemes averages to be approximately 4.5% of an employee s earnings. In addition to the public pension and the employer-provided pension, individuals are allowed and encouraged to engage in private pension savings and investments. The Swedish tax system allows for tax deductions for some forms of pension savings. It also allows the individual to decide whether he or she wants to be taxed 30% on realized profits or whether he or she wants to pay an annual flat tax of approximately 0.75% of the value of his or her investments. B. Inferring Transaction Dates For each year I have per transaction data on the stocks each individual sold during the year. The data contains the total value of the transaction as well as the number of stocks sold, but does not contain the date of the transaction. Fortunately, for a majority of the transactions I am able to infer the transaction date by calculating the per stock price of the transaction and comparing this price to market data. Let P i,j be the per stock transaction price of stock j in transaction i and let H j,t and L j,t be the highest and lowest observed transaction price on the market for stock j at time t. I can now infer the set of possible transaction dates T using equation (1) below. T i = (t : L j,t <= P i,j <= H j,t ) (1) Note that since the set T i might contain more than one possible transaction the actual transaction date is observed with some noise. The method for inferring transactions successfully pinpoints the date of transaction in roughly 40% of the cases. For an additional 45% the cardinality of T is less than 5. In these cases I consider the transaction to have occurred on either of these dates. Section (IV) contains a discussion on how this indeterminateness is incorporated in the empirical methodology. The remaining 15% where the transaction date cannot be inferred with reasonable precision are dropped from the sample. C. Quantifying Textual Data There are a number of challenges involved in converting textual data into something suitable for econometric methods. In this section I discuss these challenges and the methods I use to solve them. The process consists of three separate stages. First I need to determine the subject of the article, i.e., which of the 10

11 companies in the sample, if any, the article is about. Second I need to determine the tonality of the article, i.e., I need to determine whether the article talks about the company in a positive or a negative way. Finally I need to determine the complexity of the article. Each of these steps will be discussed in detail. Finding the topic of the article involves searching for keywords in the article text. For each of the companies in my sample I develop a list of keywords that are commonly used to refer to the company. Consider for example H&M; popular ways to refer to the company would include H&M, HM, or Hennes & Mauritz. I follow Tetlock et al. (2008) and identify articles where one of these keywords appears within the first 25 words, including the headline, of the article. I run several tests on my list of keywords to make sure none of the keywords is erroneously picking up unrelated articles. Some heuristics and manual work was required to disambiguate certain company names; consider for example the company Ericsson which is also a very common Swedish surname. Any articles where more than one company is mentioned within the first 25 words is dropped from the sample. The result of this procedure is a list of articles each associated with exactly one company. The sentiment of each article is estimated by identifying positive and negative words in the article text. In order to determine whether words are negative or positive I need a dictionary with words categorized by their implied sentiment. Several papers use the Harvard-IV-4 psychosociological dictionary, however, Loughran and McDonald (2011) argues that the Harvard-IV-4 is poorly suited for financial texts where, for example, liability is a common and not necessarily negative word, and develop a new dictionary more suited for determining the sentiment of financial texts. Common to all of the wordlists is that they are for the English language. Since this the dataset used in this paper contains articles exclusively in Swedish, and since no comparable dictionary exists for the Swedish language I had to develop my own dictionary. Following Loughran and McDonald (2011) I manually create a list of Swedish words that signify positive or negative sentiment. I make sure that the list does not contain any words that could produce false positives in a financial text in Swedish. The result is a list of 1147 positive and 274 negative unique words. For each word I also produce a list of different grammatical forms of the same word. After counting the number of positive and negative words in each article I follow Tetlock et al. (2008) and calculate my measure of sentiment as in equation (2) where P is the number of positive words and N is the number 11

12 of negative words. ( ) 1 + P log 1 + N (2) The last step of the process is to determine the complexity or readability of each article. There are several readability measures that were developed intended to be used for the analysis of texts in English. 2 Fortunately there is a similar test with a similar structure that was developed for the Swedish language. The measure, called LIX (Björnsson, 1968), is defined as in equation (5) where long words is defined as words with more than 6 letters. For each of the articles in my sample I calculate this LIX measure. ( ) total words long words total sentences total words After following these three procedures I end up with a list of articles each associated with exactly one company and each with calculated measures of sentiment and complexity. Finally I follow Tetlock et al. (2008) and filter out articles that contain less than 50 words, or does not contain at least 5 words that are either positive or negative. I use the quantified measures to classify the articles in two dimensions: positive or negative, and complex or non-complex. Articles that have a sentiment score of zero or less will be quantified as negative. This corresponds to roughly 75% of all articles in the sample. In my analysis, since I am investigating decisions to sell, I will only consider those articles that were classified as negative. Using a threshold of 35, the articles with a LIX score above the threshold will be considered complex, while articles below the threshold will be considered non-complex. The sample contains roughly 75% non-complex articles and 25% complex articles. 2 The most commonly used readability measure in the English language is the Gunning fog index (Gunning, 1952). The formal definition of the Gunning fog index is shown in equation (3) where complex words are words with three or more syllables not counting proper nouns, familiar jargon, or compound words. The Gunning fog index is decreasing with the complexity of a text and a text intended for a wide audience generally requires a Gunning fog index of less than 12. [( ) ( )] words complex words (3) sentences words Another popular measure for determining the complexity of texts in English is the Flesch- Kincaid measure defined as in equation (4). ( ) ( ) total words total syllables (4) total sentences total words (5) 12

13 D. Sample Restrictions I place a number of restrictions on the investors I consider. For every year I drop the top 1% in terms of financial wealth from the sample. These individuals are likely to differ significantly from the result of the population and their reasons for trading are likely not representative for the rest of the population. I also drop those individuals that own less than 5 unique stocks or perform less than 5 transactions during any given year. Finally, I drop individuals less than 20 or older than 65 years of age. About 25% of the adult population owns stock outright. Out of these another 25% are actively trading according to my definition. After applying these restrictions the final sample contains roughly individuals. For each of these individuals I record their demographic data as well as yearly observations of their portfolio holdings. Collectively, these individuals were responsible for roughly 6 million transactions or about transactions per year. For each of these transactions I record the total value of the transaction, the number of stocks sold, the transaction price, and the inferred date of transaction. The dataset of quantified articles contain roughly observations which are predominantly spread over 50 unique periodicals. For each of these observations I record the date of publication, the company the article pertained, and the complexity level of the article. E. Summary Statistics Table (I) contains summary statistics on the individuals in the sample divided into two groups based on their level of sophistication. Unsophisticated investors are slightly more likely to not be employed with slightly higher fractions of unemployed, entrepreneurs and students. Among the employed, we note that sophisticated investors earn higher salaries and that their disposable incomes are higher. While the sample is dominated by males we note that the proportion of women is higher among the sophisticated investors. The age profiles of the two groups of sophisticated and unsophisticated investors are almost identical. Turning our attention to the financial situation of the two groups we see that sophisticated investors are wealthier, although not dramatically so. Sophisticated investors are consistently on average about 10% wealthier than unsophisticated investors in all of the wealth measurements reported. We also note that sophisticated investors tend to be more active with their stock portfolios owning an average of stocks and transacting on average times per year, compared to the unique stocks and transactions of the average 13

14 unsophisticated investor. Figure (I) plots the number of articles grouped by how many other articles were published on the same date about the same specific company. We note that the two largest groups are the cases where the article was the only article about that company on that date, and the case when 10 or more articles where published simultaneously about the same company. On any given day with at least 2 news articles there are 3 different possible cases: either (1) all articles are non-complex, (2) all articles are complex, or (3) there is at least one noncomplex and one complex article. Figure (II) plots the proportions of these cases with respect to the total number of articles that were published about the same company on the same day. We see that on the days when there was only a single article about a given company published the likelihood of that article being complex was almost 50%. This number should be considered together with the fact that only about 25% of articles were categorized as complex. Starting from three simultaneous articles we note that the probability of seeing both a non-complex as well as a complex article is monotonically increasing. IV. Empirical Methodology After quantifying my dataset of articles I arrive at a new dataset more suitable for econometric methods. Each observation in the dataset contains 3 variables: the company the article pertains to, the date of publication, and a binary variable indicating whether the article was written in complex language. I define variable N NC c,t to be equal to 1 if for company c at any time between time t and time t 5 an article classified as non-complex was published where the topic was the company in question. If no such article was published within the specified time frame the variable is equal to 0. Similarly, I define N C c,t to be equal 1 if an article classified as complex was published about company c within the same time frame, and 0 otherwise. For the purpose of identifying the trading behavior of individual investors and the ex post result thereof I develop three measures that will be used as dependent variables in our econometric estimations. First, I will consider an individual investor s propensity to trade; second, the size of their transactions; and third, the resulting ex post performance of their transactions. A. Methodology and Specification I wish to estimate the effect news has on the propensity to trade, i.e., the probability that a news event will cause an investor to sell at least one stock in the 14

15 company in question. In order to estimate this probability I need to consider not only the transactions that did occur, but also the transactions that did not occur. For each investor and year I consider the stock portfolio of the investor going into the year. Let n be the number of stocks and let A i be a n 52 matrix, with one row for each stock and one column for each week of the year. Now, for each investor, let A i c,t be equal to 1 if the investor sold at least one stock of company i during week j. A i 1 if stocks in c sold during week t by investor i > 0 c,t = (6) 0 otherwise In my econometric estimation I am going to let my dependent variable Y i,j,c,t be equal to A i c,t where the frequency of t is weekly. Note that the number of observations for the 8 years of our panel will be equal to 8 52 the average number of investors per year the average number of stocks per investor. In our sample this would mean roughly 250 million observations which would be infeasible to estimate. In order to facilitate estimation, each year before constructing the variable I randomly sample 10% of the investor population. In addition to measuring the binary sell-or-hold response we also want to measure the magnitude of the reaction. For each individual and year I consider their portfolio at the start of each year and all their transactions during the year. For each transaction i in company c I construct a relative measure of size by dividing the value of the transaction with the total value of the fraction of the equity portfolio that was invested in company c at the beginning of the year y. Y i,j,c,t = Value of transaction i,j,c,t /Value of position i,c,y (7) For the rare cases when the stock was purchased and sold during the same year I assign a value of Y i,j,c,t = 0.5. Dropping these observations do not significantly alter the results of the estimation. Note that for this outcome variable the unit of observation is per transaction and the frequency of t is daily. Finally, I want to consider the performance of the sales conducted. I develop a measure of stock-picking ability by for each transaction recording the transaction price and comparing this to market price of the stock in question 12 months into the future. If the transaction price was higher than the market price 12 months into the future we consider the sale successful and let Y i,j,c,t = 1 otherwise we 15

16 let Y i,j,c,t = 0. 1 if transaction price i,j,c,t > market price c,t+365 Y i,j,c,t = 0 otherwise (8) Note that for this outcome variable the unit of observation is per transaction and the frequency of t is daily. I employ the same estimation strategy for each of my three dependent variables with only a slight difference for my propensity to trade measure. Consider equation (9) below where Y i,j,c,t is the dependent variable under consideration. Let M i be a dummy variable equal to 1 if investor i is male and 0 otherwise, and let S be a dummy variable if investor i has completed the equivalent of a bachelor s degree and 0 otherwise. I will estimate the equation using OLS with standard error clustered on the company-year level. For each individual i I control for a list of characteristics including: log of financial wealth, log of real estate wealth, log of total liabilities, log of disposable income, log of labor income, and age, as well as dummy variables for unemployment status, entrepreneur status, student status, and immigrant status. Additionally I control for stock characteristics (Z i,t ) including past returns, past volatility of returns, as well as for weekday effects. Y i,j,c,t = [α 0 + β 0 M i + δ 0 S i + γ 0 M i S i ] (9) +N U c,t[α 1 + β 1 M i + δ 1 S i + γ 1 M i S i ] +N S c,t[α 2 + β 2 M i + δ 2 S i + γ 2 M i S i ] +φ 0 X i,t + φ 1 Z c,t + φ 2 I c + φ 3 I y + φ 3 I wd + ɛ i,j,c,t For my transaction size and performance measure the unit of observation is transactions and the frequency of t is daily. For my propensity to trade measure the frequency of t is weekly and the unit of observation is investor-week-stock. Furthermore, when I estimate the propensity to trade I will need to redefine N U c,t and N S c,t slightly. I will let these variables be equal to 1 if an article of the respective complexity level was published during week t. Estimations with the propensity to trade will be conducted using a yearly random sample of 10% of the investor population while estimations for the remaining two measures will be conducted using the entire dataset. 16

17 B. Summary Statistics Table (II) contains summary statistics on the outcome variables discussed above. I report statistics grouped by two dimensions. First, the statistics are grouped by the sophistication level of the investor that made the trade. Second, the statistics are grouped by the complexity level, if any, of the article that preceded the trade. Note that there are four possible combinations: either there was no article, there was a non-complex article, there was a complex article, or there was both a complex as well as a non-complex article. Transaction sizes are consistently larger for unsophisticated investors. For example, subject to both a complex and a non-complex news even unsophisticated investors sold on average roughly 90% of their position in the stock in question. Sophisticated investors exhibit a higher propensity to trade in cases where the transaction was preceded either by no news events or by complex news events. Unsophisticated investors exhibit a higher propensity to trade only when the transaction was preceded by only a non-complex article. If transactions were preceded by both a non-complex as well as complex news event the propensities to trade among the two groups is roughly equal. Finally, we note that sophisticated investors exhibit better stock-picking performance in all cases except when the trade was not preceded by any news events. In those cases unsophisticated investors perform slightly better. V. Results In this section I discuss the estimated results obtained through the use of the empirical methodology described above. The main results are contained in three tables. First, I estimate the effects of demographics on my measures of trading behavior unconditional on news events. Second, I estimate the same regression this time including demographic characteristics with a dummy that is equal to 1 if a news event preceded the transaction and 0 otherwise. Finally, I divide news events into non-complex and complex news events based on the level of complexity of the article. Table (III) contains the estimation results unconditional on news events. Comparing the coefficient estimates to a mean propensity to trade of around 2% we note that sophisticated investors trade almost as much as 50% more often than unsophisticated investors. Furthermore we note that unsophisticated males trade more than twice as often as unsophisticated females, with the gender gap being considerably smaller among sophisticated investors. Worth noting is that the group with the highest trading frequency of trading are the sophisticated 17

18 males. Looking at transaction sizes we note that unsophisticated males are responsible for the relatively largest individual transactions with transaction sizes being on average 10 percentage points larger than those of unsophisticated females. Sophistication reduces transaction sizes by about 8 percentage points. While males trade more than their female peers in both groups of sophistication the gender gap is considerably smaller among sophisticated investors with the gap being roughly 2 percentage points compared to the 10 percentage points of unsophisticated investors. Finally, turning our attention to the performance measure we see no large differences neither in terms of level of sophistication nor in terms of gender. All coefficients are insignificant with the exception of the gender dummy with a very small coefficient of roughly half a percentage point. Table (IV) contains estimation results where demographic dummies have been interacted with a dummy signifying news events of any complexity level. We note that news events increase the propensity to trade among all groups but that the effect is smaller for sophisticated investors. Unsophisticated males exhibit the largest propensity to trade with an increase of roughly 1.6 percentage points which is a 0.71 percentage points larger increase than for unsophisticated women. Among the sophisticated investors the effect is also stronger for men but the gender difference is considerably smaller than for unsophisticated investors. Transaction sizes following news events are larger for all groups of investors. In particular we observe an increase in transaction sizes of more than 16 percentage points for unsophisticated men. We note that effect is smaller for sophisticated investors and that sophisticated males actually tend to use slightly smaller transaction sizes than those of sophisticated women, i.e., in this case the gender gap points in the opposite direction. Finally, we note that reacting to news has a positive effect on performance. All groups of investors perform better if the transaction occurred subsequent to a news event. In particular, the increase in performance among sophisticated investors is almost 9 percentage points. We do not observe a large difference in performance between the genders with the only significant gender-related coefficient being very small. Table (V) contains regression results where news events have been divided into two groups based on the complexity of the article. Looking at the propensity to trade we note that while both non-complex and complex news events have a positive effect on the propensity to trade whereas sophistication reduces the propensity to respond to non-complex articles it increases the propensity to 18

19 respond to complex articles. For non-complex articles we note that there exists a gender a gap for both sophisticated as well as for unsophisticated investors. However, for complex articles we observe a gender gap only for the unsophisticated investors. Note also that the effects of non-complex articles is positive but much smaller for the unsophisticated investors. In the case of transaction sizes we note that sophisticated investor trade using smaller transaction sizes than unsophisticated investors in all cases. However, transaction sizes for sophisticated investors following complex news events are considerably larger than in the case of no news event. Furthermore, we note that there is a sizeable gender gap among unsophisticated investors for both non-complex as well as complex news events. For sophisticated investors we see a negative gender gap with regards to non-complex articles with sophisticated men using slightly smaller transaction sizes. The difference in transaction sizes among sophisticated investors in response to complex articles is negligible. Finally, looking at performance we note that while trading following noncomplex articles has a small but positive effect on performance the gain in performance from trading following a complex article is as much as 16 percentage points for the best performing group of sophisticated males. Sophistication increases performance both for non-complex news events as well as for complex news events. We observe no gender differences among the sophisticated investors, however for sophisticated investors we observe a positive gender difference with regards to complex articles with sophisticated men performing slightly better than sophisticated women. In interpreting these results it is useful to consider existing literature. The fact that sophisticated investors tend to trade more often unconditional on news can be seen in the light of Calvet et al. (2009a) which finds that sophisticated investors tend to rebalance their portfolios more often. This interpretation is further strengthened by the fact that we observe smaller individual transaction sizes for sophisticated investors. The observed reactions to news confirms the predictions of Odean (1998). Sophisticated investors, exhibiting lower levels of overconfidence, will be more selective in the news they choose to act on and more cautious when they do decide to act. The better observed performance among sophisticated investors can be seen as an effect of their higher ability to discern which articles are worth acting on and their increased access to complex information through their higher levels of reading comprehension. In summary we find that sophisticated tends to have a negative effect on transaction sizes and a positive effect on performance. The effect of sophistication on the propensity to trade depends on the motive for trading. Sophisticated 19

20 investors tend to trade more often unconditional on news. Conditional on news, sophisticated trade more often if the news event was complex and less often if the news event was non-complex. We observe gender differences in the propensity to trade and in transaction sizes. While both sophisticated and unsophisticated men tend to trade more often than women, we actually observe that sophisticated men use smaller individual transaction sizes than sophisticated women when acting in response to news. We observe no clear gender differences in performance with the exception that men tend to do slightly better following complex news. VI. Robustness This paper contains a number of robustness checks. In unreported results I have tried varying all of the parameters involved in my sample restrictions with no qualitative difference in results. The results are robust to various different lengths of event windows. For example, I have tried using event windows with lengths of 3 days and 7 days, and the results were qualitatively consistent with the results obtained using a 5 day long event window. Furthermore, the results are robust to including individuals of all ages as well as to including the top 1% individuals in terms of financial wealth. One potential concern would be if the companies covered by articles of different complexity levels were fundamentally different. In Table (VII) I list the companies for which I recorded the largest number of articles grouped by the level of complexity of the articles. Note that the two lists are very similar, hence there appears to be no fundamental difference in coverage with regards to complexity levels. Similarly, another concern would be that since investors can only sell the stocks they already own the results could be biased if for some reason exogenous to the analysis of this paper sophisticated investors held fundamentally different portfolios compared to unsophisticated investors. In Table (VIII) I list the most popular stocks by number of individuals owning at least one stock in the company grouped by the sophistication level of the investor. Note that the most popular stocks for unsophisticated investors is very similar to those of sophisticated investors and there seems to be no fundamental differences that would bias the results. In Table (VI) I report the results of a placebo test. I conduct the placebo test by randomly reassigning the date of publication within the same year for all of the articles in the sample, and rerunning my regressions on this placebo dataset. Note that the coefficients not interacted with news events remain qualitatively 20