Stock portfolio structure of individual investors infers future trading behavior Ludvig Bohlin∗ and Martin Rosvall† Integrated Science Lab, Ume˚a University, Sweden (Dated: July 28, 2014) Although the understanding of and motivation behind individual trading behavior is an important puzzle in finance, little is known about the connection between an investor’s portfolio structure and her trading behavior in practice. In this paper, we investigate the relation between what stocks investors hold, and what stocks they buy, and show that investors with similar portfolio structures to a great extent trade in a similar way. With data from the central register of shareholdings in Sweden, we model the market in a similarity network, by considering investors as nodes, connected with links representing portfolio similarity. From the network, we find investor groups that not only identify different investment strategies, but also represent individual investors trading in a similar way. These findings suggest that the stock portfolios of investors hold meaningful information, which could be used to earn a better understanding of stock market dynamics. Introduction explained by homophily, i.e., the tendency of individuals Stock market trading provides opportunities at the cost to behave and bond with others who are similar. In- of risk. For investors, the ultimate trading goal is to make vestors potentially trade more similarly if they share cer- as much money as possible by acting in such a way that tain properties, including, for example, age (12), gender the highest possible profit is realized at minimal risk. Yet (13) and familiarity (14). However, classifying individ- how to trade optimally is far from obvious, and many fac- ual investors into such distinct categories is not straight- tors influence trading behavior. The traditional approach forward. The classification is in some cases motivated to represent market trading has been a model perspec- by theoretical considerations, and in other cases by ob- tive, assuming that investors act as rational and identical served patterns in the data. Categories include, for ex- agents (1, 2). However, this assumption has been chal- ample, fundamentalists and chartists (15), and informed lenged by empirical evidence, which suggests that also and uninformed traders (16). Other examples of investor other elements are present in financial markets (3). For categorizations in financial data are derived from trad- example, researchers have suggested models in which the ing correlations (17), direct stock trading data (18), and economic decisions of investors also consider the effects network approaches (19, 20). of social, cognitive, and emotional factors. These factors Although the understanding of and motivation behind and their influence on trading are often studied with data individual trading behavior is an important puzzle in fi- external to the actual trading process and include, for ex- nance, little is known about the connection between in- ample, proximity (4), social media interactions (5) and vestors’ portfolio structure and their trading behavior in web engine search queries (6, 7), In this work, we instead practice. Studies have found that many individual in- focus on data more directly relevant to the trading by vestors tend to hold poorly diversified portfolios and in- relating trading patterns and behavioral biases to stock stead concentrate investments in only a few stocks (21). portfolio structure. With real financial data on individ- The difficulty of searching through all available stocks ual investors from the Swedish stock market, we study also makes it more likely for individual investors to in- the connection between what stocks investors hold, and vest in stocks that attract their attention (22), and these arXiv:1402.2494v2 [q-fin.GN] 28 Aug 2014 what stocks they buy. attention-grabbing stocks are typically the ones that in- Trading by individual investors and related behavioral vestors already hold in their portfolios. This bias indi- biases have been studied by, for example, local bias (8), cates that portfolio structure and trading decisions are overconfidence (9), sensation seeking (10) and the dis- naturally connected. position effect (11). Studies show that investors base In this paper, we explore the connection between port- trading not only on rationale information, but are also folio structure and trading behavior in individual in- affected by factors connected to both personal character- vestors. With detailed data on stock portfolios from the istics and associated external conditions. Various indi- central register of shareholdings in Sweden, we study the vidual biases give rise to a trading heterogeneity among relation between stock portfolio similarity and trading investors, or among groups of investors. The presence of similarity. Unlike most previous research, we do not an- such investor groups with similar trading behavior can be alyze the direct trading, but instead focus on long-term trading behavior by looking at changes in share portfo- lios over time. We aim to examine two main questions: (1) How do investors in the stock market structure their ∗[email protected]; Corresponding author portfolios? And (2) Can we learn about trading behavior †[email protected] by looking at the investors’ portfolio structure? 2 To answer the questions of individual trading behavior holdings. Direct holdings are registered in the investor’s we take three steps: First, we investigate how individ- name, as opposed to nominee holdings, which are reg- ual investors hold stocks, and how they trade. Second, istered and managed by an equity manager on behalf we divide investors into groups based on portfolio sim- of the investor. The direct holdings of all investors are ilarity. This division is done with a network approach, presented in each half-year report, with detailed infor- where individual investors are considered to be nodes, mation about registration type, share amount, and the and links between investors are constructed according to equity ISIN in which the shares are held. This infor- stock portfolio similarities. To group similar investors, mation makes it possible to find share changes in the we analyze the network with the community detection portfolios between reports, provided that investors have algorithm Infomap (23). Third, we analyze the derived a traceable identification number. Investors who lack a groups to investigate the relationship between portfolio Swedish identification can not be reliably tracked in the structure and trading behavior. This analysis is done data over time, and we therefore excluded these investors by comparing investor trading within groups to investor in the analysis. trading outside the group. In the following section we To reduce the effects of noise in the data, some con- present the methods and the results, and, in short, we ditions for the included stocks were established. First, find that the portfolio structure of individual investors we only considered stocks of companies that existed for holds information on trading behavior, and that investors the entire time period. We therefore excluded stocks that with similar portfolios, to a great extent, trade in a sim- were introduced or removed from the market during the ilar way. time period for any reason. This exclusion was done to enable comparisons between two share reports without Methods changes in the company domain. Furthermore, we also Data from the central register of Swedish required that the total share amount of a stock must not shareholdings have changed more than five percent during the time pe- We examined more than 100,000 individual investors who riod. This condition was set because larger changes make were actively trading in the Swedish stock market from it hard to distinguish actual active trades of investors 2009 to 2011. The investors and their stock portfolios from more passive changes in the portfolios directly re- were extracted from a dataset with around two million lated to a share amount change, as, for example, in the investors. The dataset stems from the central register case of stock splitting. As a consequence of the share of shareholdings in Sweden, and covers basically all in- amount change criteria, we excluded, for example, the vestors and their holdings in every publicly traded com- companies H&M and Swedish Match from the analysis. pany in Sweden. The dataset was provided by Euro- Finally, only listed stocks were considered in the analysis, clear Sweden AB, and permission to use the data was since these stocks are publicly traded and it is possible to given under a special agreement. Data are presented in find an explicit price for them. It is also worth noticing half-year share register reports between June 30, 2009, the distinction between stocks designated A and B on and December 30, 2011, with detailed ownership infor- the Swedish market. A company can be associated with mation of investors in each registered company. The re- more than one stock, because A and B stocks, and other ports also included the companies’ total share amount potential stock classes in a company, must have different and their corresponding stock ISIN (International Secu- ISIN codes. We considered these different stock classes rities Identification Number). Additional data, obtained as separate stocks, because classes with less voting power from the Swedish Central Statistics Office (SCB), pro- usually are more liquid, and therefore give rise to differ- vided share prices for companies listed on the Stockholm ent trading than the ones with superior voting rights. stock exchange. Those data specify share prices at stock In summary, we examined investors on the Swedish exchange closing time, i.e., the price of the latest sold stock market who are natural persons, traceable over share on the last trading day. If price data are lacking, time, active in trading and primarily registered as share- bid price and then ask price were used instead. In total, holders. This selection means that we, for example, ex- the data contain share prices for around 500 listed stocks.