York City Taxi Patterns
Zhenhao.piao@warrington.ufl.edu
Acknowledgments: I thank my committee members: Marcus Kirk, W. Robert Knechel (chair), Paul Madsen, Michael
Mayberry, and Andy Naranjo, for their advice, guidance, and encouragement throughout the dissertation process. I
also thank Bobby Carnes, Owen Davidson (AAA discussant), Justin Kim, Brian Miller, Don Monk, Mike Ricci,
Clemens Sialm, Jenny Tucker, Diana Weng, Russell Wermers, Mark Zakota, Rachel Zhang and workshop participants
at the 2020 AAA/Deloitte Foundation/J. Michael Cook Doctoral Consortium, University of Florida, 2021 AAA
Annual Meeting, and the 2nd Ph.D. Student Symposium on Financial Market Research and Policy Developments (2021)
for their thoughtful comments and suggestions throughout the development of this paper. I am grateful for the generous
financial support from the University of Florida’s Fisher School of Accounting. All remaining errors are my own.
Taxi Patterns
information through on-site meetings with public firms and the informativeness of their trading
decisions conditioning on investors’ public information processing skills. Using the taxi traffic
between an institutional investor’s office and the headquarters of its local investee as a novel proxy
for private interactions, I find institutional investors benefit incrementally from private information
acquisition. Institutional investors’ trades in local stocks are more informed when there is a higher
degree of private interactions with local firms. Further, consistent with public information leveling
the playing field by constraining investors’ returns from private information, results from cross-
sectional tests suggest that institutional investors’ returns from private information acquisition
mainly concentrate on firms with opaque information environments. Overall, this study suggests
that private interactions with public firms reward institutional investors with information that
improves their trading decisions.
Keywords: Institutional investors, private informational advantage, corporate disclosure
Data Availability: Data are publicly available from the sources identified in the paper.
JEL Classification: G23, G14, K22, G11
1
the levels and changes in institutional ownership predict stock returns, suggesting institutional
investors possess some form of information advantages over retail investors (e.g., Gompers and
Metrick, 2001; Nofsinger and Sias, 1999; Ali et al., 2004). However, the specific mechanisms
through which institutional investors acquire their information advantage are not well understood.
Specifically, data limitations inhibit disentangling whether the superior trading decisions of
institutional investors are due to their unique access to acquire new private information (e.g.,
Solomon and Soltes, 2015) or their ability to process public information more effectively (e.g.,
Ben-Rephael, Da, and Israelsen, 2017; Campbell, Ramadorai, and Schwartz, 2009).1
In this study, I examine the effect of private access to firm management on investors’
trading decisions at the investor-firm level, which enables me to estimate the importance of private
information acquisition incremental to investors’ information processing capabilities. Using the
taxi trip records from the New York City Taxi and Limousine Commission (TLC), I construct a
novel proxy for investors’ private information acquisition that varies both cross-sectionally at the
investor-firm level and spans a long time horizon. This granular measure of investors’ private
information acquisition enables me to hold investor-specific information processing abilities
relatively constant and to isolate the informational value of private information access.
Public companies in the U.S. provide a large volume of financial and nonfinancial
information via public disclosure channels, aiming to promote the fairness and efficiency of the
capital markets. Recent literature on disclosure processing suggests that institutional investors
process public firm disclosures more effectively and in a more sophisticated manner than
1 For this study, I define public information as any information provided by firms that reach all market participants
simultaneously such as SEC filings, management forecasts, press releases, etc.
2
institutional trading could be attributable to their superior processing of public disclosures.
Meanwhile, another stream of literature suggests that institutional investors have exclusive private
access to corporate managers and can extract substantial information advantages from such private
information channels (Solomon and Soltes, 2015; Soltes, 2014; Bushee, Gerakos, and Lee, 2018;
Cen et al., 2020). Both channels could be at work simultaneously, but the practical implications
are different for each. While all market participants can strive to process public information more
effectively, individual and retail investors are likely not able to match institutional investors’ direct
access to corporate managers. Thus, the extent to which institutional investors’ abnormal returns
are driven by private access to managers as opposed to superior skill at processing public
information remains an important question for market participants and policymakers and requires
more careful examination.
Private investor-firm interactions and services that facilitate such exclusive connections
represent an important revenue-generating service line for banks. In the U.S., money managers
spend an estimated total of $1.4 billion per year for private meetings with corporate managers,
while banks and brokerage firms that facilitate company-investor connections generate
approximately $900 million in revenue in 2018 (Ng and Troianovski, 2015; Hoffman and Rogow,
2019). Nevertheless, the degree to which this spending is rewarded with new, firm-specific
information or other types of payoffs (e.g., industry expertise) is not clear. My data enables me to
examine interactions between particular investors and particular firms more precisely than prior
research and, as a result, describe with more specificity what institutional investors are purchasing
in this market for access.
3
Prior studies on private interactions typically compare the return predictability of trades
across investors who have various levels of private interactions with corporate managers (Solomon
and Soltes, 2015; Bushee, Jung, and Miller, 2017; Bushee et al., 2018).2 The insight from this
literature is that investors who are more privately informed make trading decisions that predict
future returns, while less privately informed investors do not exhibit such predictability. However,
it is not possible to conclude from the existing research that the superior return predictability of
investors who have private access to management is attributable to the new private information
these investors learn through their private access to managers, given that these investors are also
likely to be more skilled at processing public information.
This study examines two interrelated research questions. First, I study whether institutional
investors extract private information that improves their trading decisions conditioning on their
disclosure processing skills. Second, I investigate whether a firm’s public information environment
moderates the value investors can extract from their investments in private access to firm
management. I address my research questions by examining the private interactions between
institutional investors and public firms located in New York City (New York henceforth). Unless
institutional investors and their investees are geographically proximate, private in-person
interactions require physical presence, which requires some form of transportation, such as taxis,
Uber or Lyft rides, subways, or private livery vehicles. I focus on taxi trips because a taxi is the
most likely means of transport that institutional investors have available in New York when
visiting local investees during my sample period due to point-to-point convenience and efficiency
2 Specifically, Solomon and Soltes (2015) compare trades in a NYSE-traded firm by investors who meet with the
firm’s management against trades made by others who did not attend private meetings. Bushee et al. (2018) compare
trades of local institutional investors versus non-local investors.
4
(Wang and Ross, 2019). 3 The TLC has collected and published taxi trip records under the Taxicab
Passenger Enhancement Program (TPEP) since 2009. The comprehensive dataset published by the
commission records billions of yellow taxi trips that occurred in New York with detailed
information, including the start and end times of the trips and precise pickup and dropoff locations.
I exploit this detailed data to capture potential private interactions between institutional investors
and their local investees that were previously unobservable to researchers.4 The granularity of the
data allows me to capture new information about private interactions between unique investor-
investee dyads and, therefore, to shed new light on the incremental informational value of direct
investor-firm interactions.
Taxi rides between two locations in an urban space could occur for any reason. Thus, it is
not clear, a priori, whether it is possible to measure investor-firm interactions using these data. As
a result, I first conduct a validation test to determine whether my empirical proxy reliably indicates
private interactions between investors and their portfolio firms by examining the determinants of
direct taxi trips. If institutional investors acquire information through direct interactions with the
management of investee firms and if they take taxi trips when carrying out these interactions, I
expect that trips will be associated with the information acquisition incentives of investors.
Consistent with economic incentives to search for private information, I find that the number of
3 For-hire vehicles through ride-sharing apps such as Uber and Lyft also represent a possible means of commute
similar to taxicabs. However, the TLC only started to collect data on for-hire vehicles from 2015, and the data is not
as comprehensive as the yellow taxi data. Despite increased popularity, Uber’s market share in Manhattan was still
less than 15% as of June 2015, and taxicabs dominated the market in NYC throughout my sample period (Schneider,
2015). The omission of Uber/Lyft rides is unlikely to introduce systematic bias to my empirical proxy for private
interactions. 4 This empirical strategy is motivated by geographical information science (GIS) theories. A stream of GIS literature
suggests that people, as physical carriers, motivate the transfer of urban stocks, such as information, across different
areas in urban space and create physical interactions between these areas (Zhong et al., 2014). Theories also suggest
human movements are outcomes of economic activities and that transportation facilitates information transfer or
diffusion between two locations (Rodrigue, Comtois, and Slack 2006, p.2). It is unlikely that all the taxi rides between
institutional investors’ offices and local investees represent private meetings between investors and corporate
managers. However, I argue and show that at least some of these taxi trips capture meaningful private interactions
between investors and their investee firms.
5
taxi rides originating near an investor’s business office and ending near an investee’s headquarters
is higher when the investee’s prospects are uncertain and when its information environment is
opaque. I find the taxi trips between the two locations are lower when the investee provides more
voluntary disclosure and has a greater analyst following. These results show that information
acquisition incentives are associated with taxi traffic and are also consistent with that the scarce
public information motivates investors to seek information from alternative channels.5
Having established that direct taxi trips can indicate private investor-firm interactions, I
then examine whether private information acquisition provides information advantages to
institutional investors conditional on their information processing capabilities. My results suggest
that private interactions, measured by increased taxi traffic, provide institutional investors with a
significant information advantage conditional on investor-specific public information processing
capabilities. Using portfolio sorts that hold investor-specific information processing abilities
constant, I document that investors’ recent buys outperform their recent sells at the subsequent
earnings announcements only when there is a relatively high level of taxi traffic between the
institutional investors and their investees. In my main analyses, I further include investor-firm and
year-quarter fixed-effects. The results suggest that institutional investors’ trades in a firm’s stock
are more informed when they interact more with the firm in the concurrent quarter. Specifically,
more private interactions are associated with statistically and economically significant trading
gains for the investors at the following earnings announcement. An interquartile increase in the
number of direct taxi rides is associated with a 15–18 basis point increase in abnormal returns
during the subsequent three-day earnings announcement window. The results are economically
significant, given that the average trading gains of the institutional investors in the earnings
5 In Appendix D, I use an investor conference as setting to provide descriptive evidence to validate that taxi traffic
enables researchers to track movements of institutional investors and investment professionals.
6
announcement window during the sample period are less than one basis point. These results
collectively suggest private information acquisition is particularly important to institutional
investors and contributes to their performance incremental to their superior processing of public
information.
Prior theories offer contrasting predictions on the effect of public information on private
information acquisition. For instance, Diamond (1985) suggests that public information reduces
traders’ investments in private information acquisition, while McNichols and Trueman (1994)
postulate that anticipation of public disclosure stimulates private information acquisition. To shed
new light on this debate, I examine whether a firm’s public information environment moderates
investor gains from private information acquisition. Results from the cross-sectional tests show
that institutional investors’ private information advantages mainly concentrate among firms with
opaque information environments, namely those that are small, have low analyst following and
issue less voluntary disclosure. These results suggest that public information levels the playing
field among investors by constraining opportunities to profit from private information (Diamond,
1985).
Collectively, my study shows that private interactions are an important source of
information for institutional investors in addition to their information processing skills. However,
the transparency of firms’ information environments significantly constrains investors’ private
information advantages as greater transparency facilitates higher price efficiency and reduces
opportunities to profit from private information.
I contribute to the literature on several fronts. Studies identify private interactions between
managers and investors using broker-hosted investor conferences (Bushee et al., 2017), corporate
jet schedules (Bushee et al., 2018), and proprietary meetings record (Solomon and Soltes, 2015)
7
and show that these private interactions are associated with superior investment performance.
Despite these insightful findings, the empirical settings used in these studies do not enable
researchers to measure and control for variation in the information processing skill of institutional
investors.6 Consequently, they cannot differentiate between abnormal investment performance
attributable to investor skills (that are correlated with private access to investee management) and
abnormal performance attributable to the acquisition of new private information through private
access. Focusing on the investor-firm level, I show that private investor-firm interactions provide
incremental information value to institutional investors conditioning on their information
processing capabilities. In addition, I demonstrate the viability of utilizing large-scale human
movement data to identify private interactions within a large sample of investors and public firms,
which could be a fruitful approach for researchers to study the interactions among market
participants. Indeed, in a contemporaneous working paper, Cicero et al. (2021) use the taxi data to
study the private information-gathering activities of mutual fund managers in NYC.7
Second, I contribute to the local bias literature by identifying a source of local investors’
information advantage. This literature suggests investment managers and individual investors tilt
their portfolio allocations toward firms located nearby and earn substantial abnormal returns from
these investments (Coval and Moskowitz, 2001; Ivkovi and Weisbenner, 2005; Bernile et al.,
6 Solomon and Soltes (2015) have a very precise measure of private meetings, but their small sample size limits the
generalizability of their results. Bushee et al. (2017) and Bushee et al. (2018) were unable to connect private
information access with specific investors and their trading decisions. Advances in the disclosure processing literature
suggest that there is significant inter-investor heterogeneity in public information processing skills (Blankespoor et
al., 2020). Therefore, it is important to control for investor-specific information processing skills in studying the
informational value of private interactions. 7 My study is different from Cicero et al. (2021) across several notable dimensions. First, while Cicero et al. (2021)
exclusively focus on mutual funds, I examine a broader set of institutional investors that exhibit greater heterogeneity
in public information processing capabilities and investment styles. Second, my analysis is conducted at the investor-
firm level while the analysis of Cicero et al. (2021) is conducted at the fund-level. Specifically, I compare the
profitability of an investor’s trades in the same local company when there is a varying level of private interactions. In
addition, I study how a firm’s public information environments moderate investors’ returns from private information
acquisition, which is not investigated in and cannot be inferred from Cicero et al. (2021).
8
2019). Given that private access to manages is likely available at a relatively low cost to local
investors, local investors may be more likely to have superior information. My findings suggest
that the superior performance of local investors could be attributable to their more convenient
access to local companies.8
Lastly, I contribute to the literature by providing new findings on the effects of public
information on private information collection. With a novel proxy for private information
acquisition, my findings suggest that rich public information environments significantly curb
investors’ returns from private information-gathering activities, yielding support for the
predictions of Diamond (1985). My findings suggest that more transparent information
environments also reduce information asymmetry among investors, thereby leveling the playing
field between investors with private access to management and those that do not have such
information channels.
2.1 Literature
My study relates to several strands of literature. First, it relates to the literature on informed
trading by institutional investors. This literature documents robust associations between the levels
of and changes in institutional ownership on future stock returns at the firm level. Gompers and
Metrick (2001) find a positive association between the level of institutional ownership and future
returns, while Nofsinger and Sias (1999) document that changes in institutional ownership forecast
returns, suggesting institutions may possess information advantages. Later studies extend these
early findings by either decomposing aggregate institutional ownership based on institutional
8 Hong, Kubik, and Stein (2005) propose a “local-investors-relations” hypothesis, which suggests “local fund
managers might be able to obtain inside information directly from the managers of the local companies which they
invest in.” However, they did not directly test this hypothesis.
9
investors’ characteristics or associating institutional ownership with various market anomalies
(e.g., Yan and Zhang, 2009; Bartov, Radhakrishnan, and Krinsky, 2000; Ke and Ramalingegowda,
2005; Ali et al., 2004).9 The takeaway from this literature is that institutional investors’ trades, on
an aggregate basis, contain superior information about firms’ prospects. However, this stream of
research is relatively silent on whether institutional investors’ informed trading is due to their
superior ability to process public information or their unique access to private information.
The local bias literature provides more nuanced insights and finds institutional investors’
trades are differentially informed, based on the geographical proximity of the investments (Coval
and Moskowitz, 2001; Ivkovi and Weisbenner, 2005; Bernile et al., 2018). 10 Coval and
Moskowitz (2001) find that mutual fund managers earn an additional annual return of 2.67% from
their local holdings relative to their nonlocal ones. Similarly, Baik, Kang, and Kim (2010) find
both the level of and change in local institutional ownership forecast returns. Despite these
important findings, there is scant empirical evidence on how investors acquire superior information
about local companies.11
9 For instance, Yan and Zhang (2009) find the positive association between institutional ownership and equity returns
is mainly driven by short-term-focused institutions, suggesting these institutions are better informed than ones with a
long-term focus. Bartov et al. (2000) document that institutional ownership is negatively associated with post-
announcement abnormal returns. Ke and Ramalingegowda (2005) suggest transient institutions’ trades are informed,
as they exploit the post-earnings announcement drift. Closely related to this study, Ali et al. (2004) find changes in
institutional ownership during a calendar quarter are positively associated with the subsequent earnings announcement
returns, implying institutional investors, on average, can trade in the right direction. My study is distinct from the
work of Ali et al. (2004), because I focus on the relation between institution-specific private information acquisition
and the information advantages at the investor-firm level, providing direct evidence on institutions’ information
acquisition and trading. 10 This literature assumes that locals have lower costs of information acquisition and thus greater information
advantages than nonlocals (Coopers, Sercu, and Vanpée, 2012). Prior work documents that local advantages in a
variety of settings. For example, local financial analysts possess information advantages over nonlocal analysts, as
evidenced by more timely revisions and more accurate earnings forecasts (Malloy, 2005; Bae, Stulz, and Tan, 2008).
In addition, local auditors also can provide higher-quality audits, compared to nonlocal ones (Choi et al., 2012). 11 There exist other ways for local investors to obtain stock information. For instance, Hong et al. (2005) document
that mutual fund managers’ trading decisions resemble those of other fund managers located in the same city,
suggesting a potential word-of-mouth communication channel. However, they did not explicitly test whether this
provides investors information advantages. Christoffersen and Sarkissian (2009) find that knowledge spillover or
learning at the city level helps mutual fund managers achieve superior performance.
10
Second, my study is related to the burgeoning literature on private interactions, which
suggests investors can extract value-relevant information from direct access to corporate
management. Solomon and Soltes (2015) study the record of proprietary meetings of an NYSE-
traded firm and find that meetings with management appear to assist participating investors’
informed trading decisions. Focusing on invitation-only investor conferences, Bushee et al. (2017)
document that trade sizes increase during the hours in which investors are granted offline access
to corporate managers, implying investors receive value-relevant information from selective
access to corporate managers. Further, Bushee et al. (2018) use corporate jet trips to cities with
large institutional investor presence to identify private meetings and find these “roadshows”
provide participating investors an advantage over nonparticipating investors.
Despite these insightful findings, important questions remain. Recent literature on
disclosure processing suggests that there is significant heterogeneity in how investors process
disclosures and incorporate public information in trading decisions (Blankespoor et al., 2020; Lee,
1992; Blankespoor et al., 2019). Even among professional investors, considerable differences exist
in the way that they process and assimilate public information (Yan and Zhang, 2009). Given that
institutional investors who have more resources at processing public information are also more
capable of investing in private information acquisition, it is ex-ante unclear to what extent that the
private interactions contribute to institutional investors’ superior trading performance. To
disentangle the informational value of private interactions from investors’ public information
processing capabilities, the ideal empirical strategy is to hold investor-specific processing skills
constant and compare trades in firms with which they have varying levels of private interactions.
However, data limitations from prior studies prevent such a research design. For instance, Solomon
and Soltes (2015) compare trades in an NYSE-traded firm of investors who met with the firm’s
11
management with those of investors who did not attend such meetings, while Bushee et al. (2017)
and Bushee et al. (2018) are unable to connect private access to investors. Thus, it is not possible
to conclude from prior studies whether the return predictability of those attending private meetings
is attributable to private information extracted from these meetings or participating investors’
superior disclosure processing skills.
It is unclear whether investors, conditioning on their information processing skills, are able
to extract new and firm-specific information from private meetings with corporate management.
First, promulgated in 2000, Reg FD strictly prohibits the disclosure of material nonpublic
information to selective individuals before publicizing it (Koch, Lefanowicz, and Robinson,
2013).12 Failure to comply with the regulation will result in enforcement actions and penalties. To
the extent that corporate managers strictly abide by Reg FD, investors are unlikely to obtain
information that could directly translate into superior trading decisions. Second, it is also ex-ante
unclear what incentives might drive corporate managers to selectively disclose material public
information to investors given the heightened litigation risks.
Nonetheless, the fact that investors are still paying for private access with management
during the post-Reg FD era suggests investors must deem such endeavors worthwhile (Grossman
and Stiglitz, 1980). On the one hand, Reg FD does not prohibit the disclosure of nonmaterial
information that helps investors or analysts complete their information “mosaic.” Thus, investors
may be able to obtain nonmaterial insights from private access that complement their existing
information set, translating into superior trading decisions. On the other hand, investors may be
12 Shao, Stoumbos, and Zhang (2021) provide evidence that the explanatory power of earnings announcement returns
on annual stock returns increases dramatically after 2003. One of the possible explanations of their results is that Reg
FD started reducing information leaks around 2004, suggesting Reg FD might be effective in curtailing selective
disclosure of public information.
12
able to generate useful information from nonverbal cues, such as facial expressions, eye contact,
and tone of voice, during private meetings with corporate managers (Ng and Troianovski, 2015).
Research supports this possibility. For instance, Hobson, Mayew, and Venkatachalam (2012) find
that the voice dissonance markers of CEOs are useful in detecting financial irregularity. Due to
these possibilities, investors might be able to profit from private interactions without violating Reg
FD. Therefore, I state my first hypothesis in the null form, as follows.
H1: Institutional investors’ private interactions with firms have no association with the
informativeness of the investors’ trading decisions.
My second hypothesis tests whether public information moderates investors’ gains from
private information. Diamond (1985) analyzes the effect of public disclosure on private
information acquisition in a rational expectations model with constant absolute risk aversion.13
Diamond’s (1985) model suggests that traders could improve their individual welfare by acquiring
private information in the absence of public disclosure. However, the release of public information
reduces the incentives to acquire private information because it increases the homogeneity of
traders’ beliefs. Further, a more transparent information environment improves stock price
efficiency and constrains the opportunities to profit from private information (Fishman and
Hagerty, 1989). Therefore, firms’ information environments could potentially moderate investors’
realized gains from private information. Specifically, theory suggests that investors’ private
information advantages should concentrate among firms with scarce public information (Diamond,
1985). Thus, I make a directional prediction for my second hypothesis.
13 Based on a different framework from Diamond (1985), McNichols and Trueman (1994) provide a contrasting
prediction that forthcoming public disclosures increase investors’ investments in collecting private information. They
rely on the Kyle (1985) framework, where there is a single informed trader, who endogenously selects the precision
of his private information, and a competitive market maker, who establishes the prices. While Diamond (1985)
assumes investors observe private information and public disclosures at the same time and hold the firm until the
liquidation, McNichols and Trueman (1994) allow the trader to acquire and trade on private information before the
release of public disclosures (i.e., when the liquidation value of the firm is revealed).
13
with more opaque information environments.
I expect any association between investors’ private information acquisition and trading
gains to vary cross-sectionally across firms with heterogeneous information environments. The
rationale is that higher information transparency facilitates a speedier price formation process and
greater price efficiency, limiting potential gains from private information (McMullin, Miller, and
Twedt, 2019). If institutional investors benefit from their private information acquisition, I should
observe a positive association between private interactions and trading profitability, and this
association should be more pronounced in firms with more opaque information environments.
3. DATA AND SAMPLE
3.1 Data
I obtain the yellow taxi trip records data from the New York City Tax and Limousine
Commission (TLC). 14 The commission started to collect taxi trip data from technology service
providers authorized under the Taxicab Passenger Enhancement Program (TPEP) in 2009, and the
data has been publicly accessible since 2015. The database provides information regarding the
pickup and dropoff dates/times, locations, trip distances, and itemized fares of all taxi trips under
commission regulation. Researchers in labor economics, transportation studies, computer
engineering, accounting, and finance have used the data for various research questions (e.g., Farber,
2008; Rajgopal and White, 2019; Finer, 2018; Bradley et al., 2020).15
14 The Taxi and Limousine Commission licenses two types of taxi medallions: yellow and green. Yellow taxis can
pick up passengers in all five boroughs (Brooklyn, the Bronx, Manhattan, Staten Island, and Queens). Green taxis,
also known as the “boro taxis,” are only allowed to pick up passengers in Brooklyn, the Bronx, Queens (excluding
LaGuardia and Kennedy airports), Staten Island, and upper Manhattan (i.e., above W 110th St/ E 96th St in Manhattan).
I only use yellow taxi trip records in this study, because most public firms and institutional investors in my sample
reside in lower Manhattan, where green taxis are not allowed to pick up passengers. 15 Finer (2018) uses the NYC taxi data to examine interactions between the insiders of the New York Fed and major
commercial banks. His study uses coincidental taxi dropoffs of passengers and direct taxi rides between the Fed and
14
The data presents a unique setting to study the private information flow between
institutional investors and public firms. First, the data captures comprehensive taxi trips in New
York, which has the largest professional investor base in the United States and many public firm
headquarters (Dyer, 2021). 16 Second, compared to other transit modes, taxi rides are the most
plausible transit mode for managers and employees of New York investment institutions to visit
their investees located in downtown Manhattan, given the typically short waiting time and greater
point-to-point convenience (Wang and Ross, 2019). Despite facing increasing competition from
other for-hire vehicles (i.e., Uber and Lyft), taxicabs dominated Manhattan’s ride service market
during the period of this study. In Appendix D, I provide a specific example that illustrates how
institutional investors commute using taxicabs in Manhattan.
Most importantly, the TLC data provides precise geographical coordinates of the pickup
and dropoff locations of the taxi trips. This feature allows me to locate and trace the trips to the
buildings or landmarks near the trip origination and destination locations (i.e., office buildings
with institutional investors’ registered business offices and public firm headquarters). 17 The
location data provides some insights into the identities of the investors who engage with local
investees. Thus, it allows me to construct a proxy for investor-firm interactions that vary both
cross-sectionally at the investor-firm level and across a long time horizon.18
commercial banks in Manhattan as indicators of offsite meetings and finds the frequency of the offsite meetings
increases around the Federal Open Market Committee meetings. Appendix C provides further details about the data. 16 Between 2001 and 2019, approximately 24.53% of the 13F holding reports are filed by U.S. domestic professional
investors based in New York. During the same period, 9.53% of 10-K filings are filed by public firms headquartered
in the city. Therefore, the data can capture private interactions among a large sample of investors and firms. 17 GPS systems built in the taximeters record the data from every ride provided by licensed NYC taxis (Rajgopal and
White, 2019). When a new taxi ride starts, drivers activate the taximeter, and the system automatically records the
GPS coordinates of the pickup location and the starting time. Meanwhile, drivers need to input other information, such
as the passenger count. As drivers complete trips and disengage fare meters, the system transmits the destination’s
geographical coordinates along with other trip information, such as fare, duration, and the driver’s identification
information to the commission’s servers. 18 Private meetings could occur in various venues, but I focus on investors’ headquarters visits, which likely
encompass in-house meetings with investees’ senior leadership and investor-relations staff. Research suggests that
15
I use the WRDS SEC Filings dataset to obtain the registered business office addresses of
the institutional investors and Compustat data to retrieve the investees’ headquarters addresses. In
addition, I obtain institutional investors’ quarterly holding information from the Thomson Reuters
13F dataset, investees’ financial information from Compustat, stock returns data from CRSP, and
investees’ 8-K filings information from the WRDS SEC Filings dataset.
3.2 Sample
My sample period spans from 2009 to 2015, consisting of approximately 1.1 billion yellow
taxi trips. I end my sample period in December 2015 because the TLC stopped providing precise
geographical coordinates of the taxi trips after 2015 to protect the privacy of drivers. Since 2016,
pickup and dropoff locations have been provided in designated taxi zones, prohibiting researchers
from identifying the buildings surrounding the originations and destinations.
I require both the institutional investors and the public firms to be located in New York,
resulting in 166,620 investor-firm-quarters observations during my sample period. More than one
institutional investor residing in the same building may hold the same local stock. Consequently,
this could lead to misattribution of an investor’s information acquisition activities and induce
measurement errors.19 Thus, I require each pair of addresses to uniquely identify one institutional
investor and one public firm. This screening criteria substantially reduces the sample size to 85,700
investor-firm-quarters but is necessary to increase identification accuracy. I also require sample
firms’ fiscal quarters to correspond with the standard calendar year. I impose this criterion to
ensure consistent measurements of investors’ private information acquisition, trades, and other
control variables. After excluding observations with missing variables for regression analyses, my
headquarters are a common venue for private meetings between investors and corporate executives (Prinsky and Wang,
2006; Park and Soltes, 2019). 19 For instance, both Highbridge Capital Management LLC and Mackay Shields LLC, with registered business offices
at 9 West 57th Street, had investments in Ann Taylor Store Corporation in 2009.
16
final sample size is 73,931 investor-firm-quarters. For each year in my sample period, the number
of unique institutional investors (public firms) ranges from 425 to 514 (192 to 238). Table 1, Panel
A, provides further details of sample selection.
4. RESEARCH DESIGN
For all the institutional investors and firms in the sample, I first geocode their office’s street
addresses using the 2018 version of the Census Bureau TIGER/Line shapefiles.20 If the first step
fails to yield geographical coordinates, I manually geocode the street address using Google Maps.
With the geocoded street addresses, I match taxi rides that originated within a radius of 80 meters
(0.05 miles) of an institutional investor’s business offices and then completed within the same
radius of a portfolio firm’s headquarters in a given calendar quarter.21 I restrict the taxi rides to
occur between 7:00 am to 6:00 pm on weekdays because investors are unlikely to interact with its
portfolio firms outside regular business hours and on weekends.22 I designate this identification
approach to capture private onsite meetings held at an investee’s headquarters, a common venue
for such interactions (Solomon and Soltes, 2015). My identification strategy is motivated by a GIS
theory, which suggests that human movement is the outcome of economic activities (Rodrigue et
al., 2006). Taxi rides across any pair of locations in an urban space potentially capture meaningful
interactions between people residing at the two ends of the trip. Therefore, I posit that taxi rides
from investors’ offices to investees’ headquarters potentially capture private investor-firm
meetings, a very costly form of information acquisition.23
20 This approach yields very precise longitudinal and latitudinal coordinates up to the ninth decimal place. 21 Bradley et al. (2020) use a similar “circle-based” classification method but with a wider range (0.10 miles). 22 In an untabulated analysis, I run my main regressions using the number of taxi rides between investors and their
investees that occurred on weekends. I did not find any significant association between the taxi rides on weekends
with the profitability of investors’ trades, suggesting taxi rides on weekends are unlikely to capture private information
acquisition. 23 Although private communication could occur in the form of emails and phone calls, these interactions are strictly
confidential, and no reasonable empirical proxy is available (Bushee et al., 2018). In addition, investors are unlikely
17
I focus on institutional investors’ trading because trades involve substantial costs and
adjustments in the investors’ portfolios. Therefore, the trades are more likely to be driven by
investors’ receipt of new information than existing holdings (Baker et al., 2010). I measure
investors’ trades and private information acquisition in calendar quarter t. I capture the
informativeness of the trades with the realized cumulative abnormal returns (CAR) of their trades
at the upcoming earnings announcement when the investee’s earnings for the quarter t is released.24
The literature suggests the earnings announcement represents the most concentrated time window
in which information of the firm’s earnings prospects is publicly released (Baker et al., 2010).
Recent evidence suggests that earnings announcement returns better capture firms’ fundamental
news, especially in years after 2003 (Shao et al., 2021). Conceptually, private interactions allow a
select group of institutional investors to obtain, ahead of the market, a more precise signal of the
firm’s future earnings. If the informed investors trade on their private information related to
forthcoming earnings, they should realize returns when firms subsequently reveal these earnings,
and the market updates its expectation of earnings.25
5. RESULTS
5.1 Summary Statistics
Table 1 presents the summary statistics. Sample firms experience an average CAR of -
0.004% in the earnings announcement windows. Meanwhile, the mean Trading_Gain is 0.01%,
suggesting institutional investors’ trades, on an individual basis, do not systematically earn
to obtain the same amount of nonverbal cues from emails and calls. In contrast, taxi rides can capture meaningful face-
to-face interactions through which investors could possibly acquire valuable private information. 24 Appendix B provides a timeline of the research design and details about the measurements of my main variables. 25 Research documents institutional investors’ superior private information is impounded into price quickly and any
returns on the private information are typically earned over a relatively short time window (Bushee and Goodman,
2007). Therefore, I argue it is reasonable to measure the informativeness of institutional investors’ trades in the short
time window surrounding the earnings announcement. Table 4, Panel B, tabulates results using alternative earnings
announcement windows. The results are consistent, but the magnitude of the coefficient attenuates as the time
windows extend farther beyond the earnings announcement day.
18
positive abnormal returns during the upcoming earnings announcement windows. On average,
approximately 50 taxi trips originate near an institutional investor’s business offices with the
destination of a public firm’s headquarters during normal business hours on weekdays in a calendar
quarter. In addition, there is a large degree of cross-sectional variation in the taxi traffic between
an investor and an investee firm, as indicated by the standard deviation of 76.97. The sample public
firm has a mean (median) market value of equity of $8.24 billion ($10.78 billion). The average
sample firm has a quarterly return on assets (ROA) of 1.00%, a book-to-market (BTM) ratio of
0.67, and an average (median) institutional ownership of 71.51% (73.98%). The firms in my
sample, on average, have nine analysts following them in a calendar quarter and issue
approximately four 8-K filings, which include three items that are voluntary. Institutional investors
in my sample manage a portfolio with a mean (median) size of $2.7 billion ($2.1 billion).
5.2 Validation Test
To validate that the number of taxi rides proxies reasonably for private interactions between
investors and firms, I first conduct a test to examine the determinants of the number of direct taxi
rides. If this proxy is reliable, the significant determinants of these rides should relate to factors
that incentivize investors to meet with their investees. Based on this premise, I regress the natural
logarithm of one plus taxi rides count between an investor i and investee firm j in each calendar
quarter t on a vector of firm and investor characteristics. The regression is specified as follows.
Log (Ridesi, j, t) = α + β Firm Characteristicsj, t + γ Investor Characteristicsi, t + Year-
Quarter Fixed-Effects + Industry Fixed-Effects + i,j,t. (1)
I posit investors are more likely to acquire private information whenever the firm’s
prospects are uncertain, or public disclosure cannot meet their information needs. I include three
.
RET-3,0 is the firm’s cumulative returns in the calendar quarter t, while RET-12,-3 is the firm’s
19
cumulative returns in the three quarters prior to the quarter t (i.e., quarters t-4 through t-
1). Stk_Vol -24,0
is the standard deviation of the firm’s stock returns during the past two years.
Studies suggest poor performance drives corporate restructuring decisions, such as sell-offs
(Duhaime and Grant, 1994) or equity carve-outs (Allen and McConnell, 1998). When a firm
consistently performs poorly, the managers are more likely to seek strategic changes to turn around
the business. This tendency introduces uncertainty to the firm’s outlook and higher information
asymmetry between the firm and outsiders. As a result, institutional investors will have more
motivation to seek information from managers.
The availability of public information should also influence investors’ desire for private
meetings. On the one hand, disclosure could mitigate the information asymmetry between firms
and outsiders, satisfying some of the investors’ information demands (Diamond and Verrecchia,
1991; Beyer et al., 2010). On the other hand, a more transparent information environment reduces
the benefits of private information acquisition. I include five variables to capture the firm’s
information environment: Analysts j,t
, MEFj,t, 8Ksj,t, V8K_CT j,t
is
the natural logarithm of one plus the number of analysts following investee firm j during quarter t.
Analysts serve as information intermediaries for investors, whose research and forecasts could
serve as a substitute for information acquisition through private meetings (Bradshaw, Ertimur, and
O’Brien, 2016). MEFj,t is the number of management earnings forecasts issued by the firm in the
current quarter. Research suggests that managers issue earnings forecasts to reduce information
asymmetry problems, and earnings forecasts could mitigate information asymmetry (Coller and
Yohn, 1997; Hirst, Koonce, and Venkataraman, 2008). I include two measures based on 8-K filings:
8Ksj,t and V8K_CT j,t
. 8Ksj,t is the count of 8-K filings, while V8K_CT j,t
is the count of 8-K items
classified as voluntary (items 2.02, 7.01 and 8.01). Log_Spread j,t
is the natural logarithm of the
20
mean daily bid-ask spread of the firm’s stock during the quarter. If my empirical proxy based on
taxi rides captures investor-firm interactions, I expect negative associations between taxi rides and
the measures of the investee firm’s information environment. Following the literature, I also
control for some common firm fundamentals, such as returns-on-assets (ROA), book-to-market
ratio (BTM), size (LNMVE), leverage (LEV), intangibles (INTAN), institutional ownership
(INSTOWN), and shares turnover (MTurnover).
Furthermore, I control for three variables related to the investor. Log_AUM i,t
is the natural
logarithm of the investor’s total assets under management (AUM) calculated as the total market
value of the investors’ reported equity holdings at the end of the calendar quarter. Weight i,t
is the
weight of the investor i’s position in firm j, calculated as the value of the holding in firm j’s stock
divided by the investor’s AUM. Stake i,t is the investor i’s percentage ownership in firm j.26 I
include year-quarter and industry fixed-effects to control for general time trends and time-invariant
industry characteristics that could drive investors’ demand for private information.
Table 2 tabulates the results from the validation test. Consistent with my prediction, both
recent (RET-3,0) and previous stock market performance (RET-12,-3) are negatively associated with
taxi rides from the investor to the firm. Stock price volatility (Stk_Vol -24,0
) is positively associated
with Log( Ridei,j,t). These results are consistent with my prediction that investors seek more private
meetings with investees whenever the prospects of the firm are uncertain. Further, I find my proxy
for private meetings is negatively associated with three of the five variables capturing the
information environment surrounding the firm. The results suggest Log( Ridesi,j,t) is lower when
the firm has a greater analyst following and when it provides more voluntary disclosures. In
26 Appendix A provides detailed definitions of these variables.
21
addition, I find the weight of the holdings in the firm’s stock in the investor’s portfolio is positively
associated with Log (Ridesi,j,t), consistent with the notion that an investor is more likely to acquire
private information about a firm when the firm represents a larger portion of the investor’s
portfolio. Overall, the validation test suggests that taxi rides from the investor to the firm capture
meaningful interactions between the investors and the firms located in NYC and that the level of
these taxi trips is associated with the intensity of information exchanges through offline channels.
5.3 Portfolio Analysis
I first follow the approach used by Baker et al. (2010) in conducting portfolio-level
analyses.27 Studies have documented that institutional investors can profit from their private
information access (Ali et al., 2004; Pinnuck, 2005). However, due to resource constraints,
investors are unlikely to acquire the same amount of private information for all their holdings. If
institutional investors are rewarded for their information acquisition, I expect them to realize
greater returns on the firms they interact with more frequently.
I investigate whether this conjecture holds in the portfolio-level analyses. For each calendar
quarter (13F report date), I first sort each investor’s holdings into two portfolios based on whether
the investee firm headquarters in NYC. Then I further sort the stocks in the local portfolio into two
groups based on the amount of taxi traffic from the investor to the firm during the concurrent
quarter. This methodology implicitly incorporates an investor-quarter fixed-effect structure that
holds investor-specific public information processing skills constant and helps me isolate the effect
of private interactions on the investors’ informed trading. I categorize firms with rides above
(below) the median rides count in the investor’s local portfolio as the high-interaction (low-
27 Baker et al. (2010) find that the stocks recently purchased by mutual funds outperform those that are recent sells
around the next earnings announcement. They attribute this pattern to the trading skills possessed by mutual fund
managers.
22
interaction) stocks. I further sort the local stocks into different portfolios based on investors’
trading directions in the stocks. As higher traffic represents a higher likelihood of private
interactions and greater information exchange, I predict that investors’ trades should realize greater
returns in the high-interaction versus the low-interaction category.
In Table 3, Panel A, I partition each of the investor’s local holdings into two portfolios
based on how the stock’s weight in the investor’s portfolio shifts during the quarter. In Table 3,
Panel B, I classify the stocks into three portfolios based on whether the number of shares held by
the investor increases, decreases, or does not change, compared to the previous quarter. After this
step, I calculate the value-weighted portfolio CAR at the following three-day earnings
announcement event window, using the weights of the holdings in its respective portfolio and
examine differences across the different portfolios in each panel.28
Table 3, Panel A, shows that in the high-interaction category, the weight-increase portfolio
has a value-weighted CAR of 0.170%, while that of the weight-decrease stocks has a value-
weighted CAR of -0.102%. The difference of 0.272% is statistically significant (p-value < 0.01).
However, in the low-interaction category, the result is reversed. The portfolio of stocks with a
weight increase (decrease) experiences a return of -0.424% (-0.161%). The difference is also
statistically significant, but the direction is the opposite of the results from the high-interaction
category (p-value < 0.05).
Results from Table 3, Panel B, are similar. In the high-interaction category, the recent buys
experience a value-weighted portfolio earnings announcement return of 0.142%, while the recent
sells have a return of -0.035%. The spread of 0.176% is statistically significant (p-value < 0.1). In
contrast, within the low-interaction category, the trades do not seem to be informed. The recent
28 As a robustness check, I also conduct the analyses using the weights of the stocks in the investor’s entire portfolio,
and the results are qualitatively similar and statistically significant.
23
buys (sells) portfolio has a value-weighted return of -0.372% (-0.259%), but the difference is
statistically indistinguishable from zero. These results collectively suggest that holding investors’
disclosure processing skills constant, they are more likely to trade in the right direction only in the
stocks within the high-interaction category, in which they are more likely to possess superior
private information. The trades in the firms for which investors have a higher degree of private
interaction appear to be well informed and generate positive returns for the investor, illuminating
the potential value of private information. As a benchmark, I also calculate the value-weighted
earnings announcement returns for the investors’ nonlocal holdings. Institutional investors
arguably have significantly less regular private interactions with their nonlocal investees than with
their local ones due to greater information acquisition costs. The empirical finding supports this
conjecture, and there is no distinguishable difference in returns among nonlocal portfolios at the
subsequent earnings announcement. I tabulate the results from the portfolio-level analyses using
size-adjusted returns in Table 3, Panels C and D.
5.4 Regression Analysis
To test H1, I specify my regression framework as follows.
Trading_Gain i,j,t = α + β Log (Ridesi, j, t) +Firm Characteristicsj, t + γ Investor
Characteristicsi, t + Year-Quarter Fixed-Effects + Investor-Firm Fixed-Effects + i,j,t (2)
Similar to Bushee et al. (2017) and Ivashina and Sun (2011), my dependent variable
Trading_Gain i,j,t
is the interaction term of (1) the cumulative, market-adjusted abnormal returns
(CAR) during the three-day window [-1, +1] centered on the upcoming earnings announcement
and (2) the direction of the investors’ trades during quarter t. I code the direction of the trades as
24
1 (-1) if the investor increases (decreases) the number of shares held in a firm’s stock and 0 if the
investor does not change the number of shares held in the firm’s stock.29
The intuition is that if an investor obtains superior private information from a firm, its
trades in the firm’s stock must earn returns or avoid losses when the firm releases its earnings
numbers. By adopting this measure, I define informed trades as trades in the same direction as the
price movement during the subsequent earnings announcement windows.30 Unlike prior studies
that examine the associations between the change in aggregate institutional ownership (IO) and
equity returns, I investigate whether investors’ investments in private information acquisition
manifest to greater trading profitability at the investor-firm level. Thus, using this composite
measure is a more appropriate choice than raw stock returns. I control for a vector of firm and
investor characteristics similar to those included in the validation test. Most importantly, I
incorporate investor-firm fixed effects to control for unobservable factors unique to a specific
investor-firm pair, such as investors’ public information processing skills specific to an investee
firm. I further include year-quarter fixed effects to control for common time trends.
Table 4, Panel A, Column (1), tabulates my baseline results, where I only include my main
variable of interest. Consistent with the portfolio-level analyses above, I find a positive and
significant relation between Log( Ridei,j,t) and Trading_Gain i,j,t
(p-value < 0.05). The result is also
economically significant. An interquartile increase in Log( Ridei,j,t) is associated with an additional
return of 18 basis points in the following earnings announcement window. The incremental 18
29 Appendix B provides a detailed numerical example of the calculation of the measure. 30 The assumption is plausible, based on conventional wisdom. Suppose an investor receives private information from
the firm that, in combination with the investor’s existing information, suggests the firm is performing beyond (below)
the market’s expectation in the concurrent quarter. The investor’s rational action is to increase (decrease) its position
in the firm. When the earnings are released, the price will rise (fall) as the market corrects its initial beliefs. Thus, if
private interactions convey useful information, investors should be more likely to make trades that generate higher
returns during the earnings announcement windows.
25
basis points returns are significant compared to the mean of Trading_Gain i,j,t
, which is
approximately one basis point. The result suggests private interactions can improve investors’
trading decisions even when I hold constant the investor-firm pair. In Table 4, Panel A, Column
(2), I control for firms’ fundamentals and find the results qualitatively and quantitatively similar.
The coefficient on Log( Ridei,j,t) drops slightly to 0.104 (p-value < 0.05). In Column (3), I further
control for firms’ information environments to see whether private interactions provide value
incremental to firms’ public disclosures. The result in Column (3) shows that both the magnitude
and significance of the coefficient on Log( Ridei,j,t) drop slightly. The coefficient declines to 0.097
(p-value < 0.1), suggesting an interquartile increase in Log( Ridei,j,t) is associated with an
additional return of approximately 15 basis points. This result has three important implications: (1)
It shows that private meetings provide significant benefits to investors, even after controlling for
the amount of disclosures. (2) It suggests a more transparent information environment potentially
constrains the opportunities of a selective group of traders to profit from private information,
suggesting public disclosures can level the playing field. (3) It supports earlier studies that find
private access provides information advantages to investors even after the promulgation of Reg
FD as my sample period is entirely within the post-Reg FD era.
Across Columns (2) and (3), the coefficients on Stk_Vol-24,0 are consistently negative and
significant (p-value < 0.1), suggesting investors’ trades are less profitable if the firms’ stock prices
are volatile, consistent with my earlier finding that investors are more likely to seek for private
interactions when there is more uncertainty in the firm’s earnings prospects. Size (LNMVE) is
negatively and significantly associated with Trading_Gain i,j,t
(p-value < 0.01). Thus, the negative
26
association is consistent with that a more transparent information environment limiting privately
informed trading by institutional investors (Maffett, 2012).31
5.5 Monte Carlo Falsification Test
I conduct a falsification test using a Monte Carlo simulation. Following Mayberry (2020),
I assign each investor-firm pair observation a pseudo taxi rides count that is the actual count of
taxi trips for another pair of investor-firm and re-estimate the main analysis (Table 4, Panel A,
Column (2)) using the placebo sample. I repeat the procedure 1,000 times and generate a
distribution of the 1,000 coefficient estimates from the placebo samples. Figure 2 plots the
distribution of the 1,000 pseudo coefficient estimates with the actual coefficient highlighted. The
pseudo estimates follow a normal distribution with a mean of 0, while the real coefficient estimate
exceeds approximately 99% of the pseudo coefficients. Using the average (-0.0001) and standard
deviation (0.020) of the 1000 placebo tests, I find an insignificant placebo effect (t-stat: -0.006).
This insignificant coefficient from the Monte Carlo simulation alleviates concerns that the
documented effect is due to chance.
5.6 Cross-Sectional Tests
To test H2, I conduct cross-sectional analyses by partitioning my sample on variables that
measure a firm’s information environment. The intuition is that if public information limits the
incentives to acquire private information, investors should only realize their private information
advantages in more opaque firms. Specifically, I partition my full sample into subsamples based
on three variables: firm size, analyst following, and the number of voluntary 8-K items filed.
31 I include a long vector of variables to control for potential confounding factors. Jennings et al. (2020) suggest that
measurement error in the control variables can “infect” the coefficient on the variable of interest. I follow their
practical guide by progressively including control variables and fixed effect and building up my regression
specifications. The results are consistent across all regression specifications. Furthermore, my results are robust with
standard errors clustered at either firm-level or investor-level. In untabulated analyses, I replace market-adjusted
returns with size-adjusted returns in calculating the dependent variable and re-run all the analyses using the size-
adjusted measure. The results are similar in terms of magnitude and statistical significance.
27
First, firm size reasonably proxies for the firms’ information environment because large
firms provide more and higher-quality disclosures than small ones (Lang and Lundholm, 1996;
Waymire 1985). Hence, I expect investors’ private information advantages to concentrate among
small firms. Results from Table 5 support this prediction. I partition my sample into two
subsamples based on the investees’ market value of equity and re-estimate the regressions in Table
4 in the two subsamples, respectively. I find the coefficients on Log( Ridei,j,t) are all positive and
statistically significant in the Small Firm subsample (p-value <0.05). The coefficients range from
0.216 to 0.229, suggesting investors realize substantial returns from their private information
acquisition on small firms. In contrast, across Columns (4) – (6), Log( Ridei,j,t) are statistically
insignificant, implying that investors are unable to gain an edge from their private information
acquisition in large firms whose information environments are relatively transparent.32
Second, analysts supply a large volume of information that shapes firms’ information
environments via their research coverage. More analyst coverage mitigates information asymmetry
between firms and investors and thus improves firms’ information environment (Frankel and Li,
2004). Thus, I predict investors to realize their private information advantages in firms with
relatively low analyst following. Results from Table 6 also support this prediction. Within the Low
Analyst Following subsample, the coefficients on Log( Ridei,j,t) range from 0.236 to 0.248 and are
32 Following Cleary (1999), I use a bootstrapping procedure to test whether the coefficients on the main variable of
interest are equal across the subsamples. Specifically, I randomly assign the observations into two subsamples,
estimate the coefficients on the subsamples and calculate the difference in the coefficient estimates from the simulation.
I obtain the empirical distribution of the difference in coefficient estimates after repeating the simulation procedure
1,000 times. I report the empirical p-value which is the percentage of simulation trials in which the difference in
coefficient estimates exceeds the actual observed difference in the coefficient estimates (Cleary, 1999). For instance,
an empirical p-value of 0.045 suggests only 45 out of 1,000 simulated outcomes exceed the actual observed difference
in the coefficient estimates, rejecting the null hypothesis that the difference in coefficient estimates is zero at a
significance level of 5 percent. I use this approach to test the equality of coefficient estimates across subsamples
because the use of traditional statistical tests (e.g., the Chow test and the Wald test) requires strong assumptions on
the regression residuals, which are likely to be violated in a panel dataset.
28
statistically significant (p-value < 0.05). However, I do not find similar results in firms with a
relatively high level of analyst following.
Finally, I partition the samples based on the number of voluntary 8-K items a firm issues
in the quarter (V8K_CT j,t
). Results from Table 7 suggest that investors’ private information
advantages only exist in firms with scarce voluntary disclosure. Across Columns (1) through (3)
in Table 7, the coefficients on Log( Ridei,j,t) are positive and statistically significant (p-value < 0.01)
for the groups of firms with a relatively low level of voluntary disclosure, with magnitude ranging
from 0.252 to 0.288. In contrast, the coefficients on Log( Ridei,j,t) are statistically insignificant
within firms with a high level of voluntary disclosure.
Overall, the cross-sectional tests support my prediction that a transparent information
environment constrains investors’ private information advantages as the results mainly concentrate
on firms with opaque information environments. These results are consistent with the conjectures
from the Diamond (1985) model that public signals can substitute for private information and are
inconsistent with those from McNichols and Trueman (1994). From a more practical standpoint,
these results suggest public information and disclosure regulation can help level the playing field
in capital markets, as transparency of corporate environments attenuates private information
advantages. From another perspective, the results also suggest that direct investor-firm interactions
may be a particularly important channel for firms that are small and have low analyst following to
inform their investors. Further, this result is consistent with prior findings that document Reg FD
had a more pronounced impact on smaller firms (Gomes, Gorton, and Madureira, 2007).
5.7 Alternative Explanations
I assume that the primary motive for investors to meet with the managers is to acquire
private information for their trading decisions. However, the meetings could also occur for other
29
purposes. Because the passengers’ identities and the trip purposes are unobservable in my data,
my results are subject to alternative explanations. For instance, among others, Log( Ridei,j,t) could
potentially capture investors’ trips made for active investor monitoring or loan negotiations rather
than mere private information acquisition. I conduct tests to rule out these alternative explanations.
5.7.1 Investor Monitoring
important monitoring roles. Thus, an alternative motive of direct investor-firm interactions is to
acquire monitoring information, which could also help investors achieve information advantages.
Distinguishing these two incentives is important because investors who are not directly meeting
with the management can also benefit if private meetings serve as a governing mechanism of local
investors, and such investor monitoring improves the firm’s performance. Following Bowen,
Rajgopal, and Venkatachalam (2008), I estimate the following models to evaluate this possibility.
ROAi,t+τ = β0 + β1 Log (Ridesi, t) + β2 ROAi,t + β3 Std_ROAi,t + β4 Log (Salesi,t) + Controls
+ Year-Quarter Fixed-Effects + Industry Fixed-Effects + i,t+τ (3a)
CFOi,t+τ = β0 + β1 Log (Ridesi, t) + β2 CFOi,t + β3 Std_CFOi,t + β4 Log (Salesi,t) + Controls
+ Year-Quarter Fixed-Effects + Industry Fixed-Effects + i,t+τ (3b)
Specifically, I regress a firm’s future performance on the firm-level taxi trips count, which
is the total number of taxi trips from the firm’s New York investors in the current quarter. The
intuition is that if private meetings serve as a governing mechanism, I expect firms facing greater
monitoring, as captured by more taxi traffic from local investors, to show greater improvement in
future performance, ceteris paribus. I measure future performance with future return-on-assets
(ROA), and cash flows from operations (CFO). The results are tabulated in Table 8, Panels A and
30
B. In sum, I fail to find Log(Ridesi,t) to be associated with future performance, which is inconsistent
with that monitoring is the dominant motive for investors to engage with managers privately.33
5.7.2 Lending Relationship
Prior studies suggest that the private information that institutional investors acquire from
lending relationships benefits their trading decisions in the borrowers’ stocks (Ivashina and Sun,
2011). Thus, another alternative explanation is that an investor-firm lending relationship drives the
taxi traffic from the investor to its investee firm while the investor’s information advantages result
from the private information acquired from loan (re)negotiations. Using Dealscan data, I identify
and exclude observations in which there is an active lending relationship between the institutional
investor and the firm and re-run the main analysis.34 I report the results in Table 9, Column (1).
The coefficient on Log (Ridesi,t) is 0.105 (p-value < 0.05), which is very similar to the results in
Table 4, Panel A, Column (2) in terms of magnitude and statistical significance. Because loans are
actively traded in the secondary market, investors might engage in a lending relationship even they
do not invest in the loans at the origination. (Ivashina and Sun, 2011). As a result, I risk omitting
some lending relationships by relying on lender information at the loan origination. Thus, in Table
9, Column (2), I exclude firms with syndicated loans outstanding during my sample period. Finally,
I drop observations associated with institutional investors whose legal type is bank trust. The
results are consistent with Table 4, Panel A. Therefore, it is highly unlikely that investor-firm
lending relationships can explain my findings.
33 I also estimate models (3a) and (3b) with the firm fixed-effect structure and the inference stays the same. I do not
use firm fixed-effect as my main specification because I include the lagged performance measure in the models. The
bias on the coefficient estimates can be severe given that I have a short panel dataset. 34 I use the linking table provided by Professor Michael Roberts to connect my data with Dealscan to identify sample
firms that have an outstanding syndicated loan (Chava and Roberts, 2008). I use a fuzzy matching procedure to identify
institutional investors who serve as lenders for their investee firms by matching the investor’s name (i.e., mgrname)
in the 13F data with the lender name (i.e., company) in Dealscan. In addition, I obtain data on the legal type of
institutional investors from Professor Brian Bushee’s website.
31
Institutional investors value and trade on private information differently due to
heterogeneity in their resources for private information acquisition and regulatory requirements
(Bushee and Goodman, 2007; Abarbanell, Bushee, and Ready, 2003). Large institutions may have
more resources to acquire private access to corporate management. Thus, I partition my sample on
the size of the institutional investors. I measure institutional investors’ size with their total assets-
under-management (AUM). Results from Table 10 suggest that investors’ information advantages
acquired through private interactions are more prevalent in institutions that are large.35
6. CONCLUSION
This study demonstrates that private interactions between investors and firms are an
important channel through which information is disseminated in capital markets. Despite the
promulgation of Reg FD, investors still make significant investments in securing costly corporate
access. Given that investors who can procure such private access are also likely to possess superior
information processing skills, it is challenging to isolate the extent of information advantage that
such private channel provides to investors.
The use of human movement data represents a potentially fruitful approach to study the
flow of market information in the future as advanced technologies make more data available.
Large-scale human movement data allows me to focus on a more granular unit of analysis and
shed new light on the informational value of private interactions incremental to investors’
information processing skills. I find such private interactions facilitate informed trading even after
accounting for the heterogeneity in investors’ abilities to process public information. Further, my
findings on the effect of public information on private information advantages imply that well-
35 In untabulated analysis, I find the results are mainly driven by large investment advisors and investment companies
as such institutions are less risk-averse and have limited fiduciary responsibilities.
32
designed disclosure regulation could level the playing field by curbing investors’ opportunities to
profit from private information. This effect offers a different justification for the need for
disclosure regulation in the capital markets.
There are important limitations of my findings. First, I capture investors’ trading using 13F
data. Therefore, my study is subject to the general limitation facing the literature that utilizes
quarterly data to examine investors’ stock trading. Second, due to data availability, I confine my
analysis to institutional investors and firms based in NYC, a city in which interpersonal
connections and social networks are particularly important. Whether a similar channel is at play in
other geographic areas requires further examination.
33
References
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