txt/2105.11780.txt

Look Inside. Predicting Stock Prices by Analysing an Enterprise 
Intranet Social Network and Using Word Co -Occurrence 
Networks  
 
Fronzetti Colladon, A., & Scettri, G.  
 
 
This is the accepted manuscript after the review process, but prior to final layout 
and copyediting . Please cit e as:  
 
Fronzetti Colladon, A., & Scettri, G. (2019). Look Inside. Predicting Stock 
Prices by Analysing an Enterprise Intranet Social Network and Using Word Co -
Occurrence  Networks. International Journa l of Entrepreneurship and Small 
Business, 36(4), 378 -391. https://dx.doi .org/10.1504/IJESB.2019.098986  
 
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letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.  1 
 Look Inside. Predicting Stock Prices by Analysing an 
Enterprise Intranet Social Network and Using Word Co -
Occurrence Networks  
 
Fronzetti Colladon , A., & Scettri, G. 
 
 
 
 
Abstract  
This study looks into the employees’  communication behaviours taking place in an intranet  social 
network , offering novel metrics of Semantic and Social Network Analysis, which can help predict a 
company stock price.  To this purpose, w e studied the intranet forum  of a large Italian company, 
exploring  the interaction s and the use of language of about 8,000 employees . We analyse d more 
than 48,000 news and comments , over a period of 94 weeks . In addition to using more traditional 
semantic and social network metrics,  we built a network  linking words  included in  the general 
discourse. In this network, we focused on the posit ion of the node representing  the company brand.  
We f ound that a lower se ntiment  of the language used , a higher  betwe enness centrality of the 
company brand, a denser word co -occurrence network and more equally distributed centrality 
scores  of employees  (lower group betweenness centrality) are all significant predictors of higher  
stock  prices.  Our findings contribute to the strea m of research concerned with  the prediction of 
stock prices , offering new metrics that can be helpful for scholars , company man agers  and 
professional investors  and could be integrated in to existing forecasting models  to improve their 
accuracy . We also show the importance of looking at internal communication streams  while  
analysing  a company’s financial performance . Lastly , we contribute to the research on word co -
occurrence network s by extend ing their field of application.  
 2 
 Keywords:  stock price; economic forecasting; intranet; social network;  web forum;  semantic 
analysis; word co -occurrence  network . 
 
1. Introduction  
The question  whether  stock market prices are predictable is frequent in literature, especially after 
financial crisis  (Cowles, 1933; Bollerslev and Ole Mikkelsen, 1996; Barro, 2015) . In recent years , 
new approaches were presented, comprising the analysis of co mplex system s and data mining and 
machine learning techniques (Kuo, Chen and Hwang, 2001; Sornette, 2003) . With the complex 
system approach it is poss ible to model  the influence  of the  various agent s that operate in a market 
(investors, companies , bank s, nations , etc.. ), investigating  their reciprocal interactions (Grimm, 
2005) . Data mining  offers  the possibility to analyse  large volumes of data , retrieved  from  different 
sources , such as  stock indices , social media  or other online platforms . Nowadays researchers  can 
take advantage of  datasets never seen before, in which interesting information can be hidden.  
In this paper, we combine methods and tools from Social Network  and Semantic Analysis 
(Wasserman and Faust, 1994; Aggarwal and Zhai, 2013) , while investigating the interactions taking 
place in the intranet  forum  of a large Italian company. We study the interaction patterns of the 
company employees with regard to their activity levels, network positions an d use of language , 
while posting news and comments . Additionally, we pro pose a novel method to transform the 
general discourse into a network of words (Danowski, 2009) , thus analys ing the positions and 
fluctuations of the company brand in that network.  Our scope is  to understand if there are  hidden  
associations  between th ese patterns and the  company stock price  at specific time lags . In this 
regard, we offer a novel contribut ion since  we use a new information source – which, to the extent 
of our knowledge, has never been used for the same exact purpose – and study original metrics 
obtained from  the combination  of semantic and social network analysis . In other words, the 
objective of this paper is to extract new variables fr om the analysis of the communication  taking 3 
 place in a large  intranet social network , show ing the  value  of these new predictors  for the 
forecasting of stock prices : we investigate  if it is possible to look at employees’ communication 
behaviours to better predict the firm market value . This research also  offers an additional  
contribut ion to increase  the knowledge about  possible use s of word co -occurrence network s.  
 
2. Network  and Semantic  Analysis for Stock Market Predictions  
The use of network theory to analyse  stock market trend s evolved around two main approaches: 
empirical and theoretical. T he first is centred  on the  experimental way s by which network s are 
created, as in the work of  Sun et al. (2015) , who connect ed stock  prices  with other market  attributes 
(e.g., trading volume s or net return s). The t heoretical approach , on the other hand,  is focused on the 
evaluation  of the  problem in a general form : given some mathematical conditions related to  the 
network, the aim is to demonstra te the existence and uniqueness of a solution (Barucca  et al. , 2016) .  
Studies applying  network theor ies were  successfully implemented to analyse  the behaviour  of 
Brazilian (Tabak et al. , 2009) , Chinese (Huang, Zhuang and Yao, 2009)  and Indian (Pan and Sinha, 
2007)  stock marke ts. Tabak et al. (2009)  tried to figure out if the Brazilian stock price returns 
present ed a power law distribution . Their result s show ed that, for most of the  study  period , a power 
law distribution is not representative of the phenomenon.  This suggests that the dynamics of stock 
prices may change abruptly , being more complicated than a power law distribution,  especially when 
critical events occur . Huang and colleagues (2009)  used a threshold method to build a social 
network considering  the correlations among  1080 stocks  in the Chinese market  and their daily 
prices for about four years. Testing the topological stability of the network, they found it to be  
robust against random vertex failures, but fragile against targeted  attack s; in this way they offered 
insights for risk and portfolio management . Similarly, Pan and Sinha (2007)  analysed  the cross -
correlation matrix of stock price s fluctuations in the Indian National Stock Exchange. This market 
exhibit ed strong er correlations when compared to  more  mature markets , such as the New York 4 
 Stock Exchange . Pan and Sinha (2007) showed that the existence of  an internal structure made of 
multiple  connected  groups is a  possible indicator of market development.  
Other authors addressed the problem of interdependence between stock markets (Morana and 
Beltratti, 2008; Sedik and Williams, 2011) , suggesting the inclusion of control  indices  while 
making stock price predictions. In particular,  Sedik and Williams  (2011)  used a GARCH model to 
study the influence of the volatility of U.S. and regional equity markets on the conditional volatility 
of stock prices in  the Gulf Cooperation Council’s m arket. Morana and  Beltratti  (2008) , showed a 
link among the variance s of the stock market indices of US, UK, Japan and Germany . 
Recently , the improvement of sentiment analysis and other social media  related  research , offered 
new possibilities, combining stock market predictions with  metrics extracted from online  platforms , 
such as Twitter or Facebook  (Zhang, Fuehres and Gloor, 2011; Chen and Lazer, 2013; Makrehchi, 
Shah and Liao, 2013) . Measuring  the sentiment  of the conversations about a specific company, 
scholars proved the influence of positive and negative feelings on stock market prices (Chung and 
Liu, 2011; Elshendy et al. , 2017) . To this purpose, Khadjeh et al.  (2014)  and Schumaker and Chen 
(2009)  used sentiment analysis combined with linear regression models  and support vector 
machines . In the  former  study, authors used the text of breaking financial new s-headlines  to 
forecast  currency  movements in the foreign exchange market . In the latter study, autho rs succeeded 
in partially  predict ing future stock price s twenty minutes after a financial article was released : they 
used several different textual representations  – such as  Bag of Words, Noun Phrases  (Caropreso and 
Matwin, 2006)  and Named Entities  (Diesner and Carley, 2005 ). Zhang et al. (2011)  analysed  
Twitter  with a similar purpose, tagging tweets according to feelings of  fear, general concern  and 
hope ; they  found a negative correlation between  the sentiment trend of the tagging variables and  the 
Dow  Jones, NASDAQ and S&P 500 . Antweiler and Frank (2004)  studied the influence of messages 
in Yahoo!Finance over a set of  45 stock market prices  of private companies . Similarly, Xie et al. 
(2013)  crawled  Yahoo!Finance  and developed  a tree representation  for word s in sentences,  which 
performed  significantly  better than  approaches based on  bag-of-words  in predicting  the polarity  of 5 
 stock price s trends . A different approach  for the  sentiment analysis of tweets  is to use  non-
parametric topic mode lling algorithms , as proposed by Si et al. (2013) .  
In the paper  of Bollen Mao and Zeng  (2011)  the prediction of  the Dow Jones Industrial Average 
was done  by analysing the  text content of daily Twitter feeds by means of two mood -tracking tools : 
OpinionFinder – which tracks positive and negative mood s – and Google  Profile of Mood States 
(GPOMS) , which  measure s language mood in term of six dimensions (Calm, Alert, Sure, Vital, 
Kind, and Happy) . Rechenthin et al. (2013)  tried to understand if there were financial agents that 
could manipulate sentiment trend s, to influence stock market price s posting specific, either positive 
or negative, messages on specialized financial website s. Nguyen et al.  (2014)  analyzed the main 
topics and sentiment of eighteen  Yahoo!Finance message boards for eighteen  stocks . On message 
boards, users discussed company news, fa cts or comments (often negative) about specific company 
events, and personal forecasts. Analysing such platforms can be helpful, also because users have the 
possibility to annotate message with tags (e.g., Strong Buy, Buy, Hold, Sell and Strong Sell). 
Auth ors proved that adding sentiment analysis to models based on historical prices  trends  can 
increase the predictive power of such models.  
Yang et al. (2015)  demonstrated the existence of Twitter ’s financial communities – which presented 
a small -world structure  – inferred studying  friend -following relationship s and user profile s, 
including language preference s, location s, account creation date s and  time zone s. Looking at the 
sentiment of the tweets sent by  people  in the most central network positions,  the authors could 
predict  financial market indices.   
Lastly, other techniques were  also implemented with the aim of improving stock prices prediction s, 
such as a rtificial neural network s. Patel and Yalamalle  (2014) , for example,  achieved good results 
using neural networks  to predict stock price s in India.   
Semantic and social network analysis  of the communication of employees proved their  value for 
several purposes, such as predicting turnover intentions (Gloor et al., 2017 a), improving customer 6 
 satisfaction (Gloor and Giacomelli, 2014; Gloor et al., 2017 b), or promoting innova tion within, and 
across, organis ational boundaries  (Wright and Dana, 2003; Dana, Etemad and Wright, 2008; Allen 
et al., 2016) . These  analyses , which  also stress ed the importance of looking at the communication 
styles and interactions taking place within the organizational boundaries,  were often carried out  
using traditional surveys or exploring e -mail networks.  
Starting from the  insights  that emerge  from past research, we developed t he present  study with the 
idea of offering a triple contribution: first ly, we present semantic and social network metrics that  
can be integrated in existing financial models to increase their forecasting accuracy; second ly, we 
give evidence to the value of transforming text data into words of networks, to extend the 
informative power of traditional semantic analysis; third ly, we show how the market value of a firm 
can be at least partially inferred by looking at the internal interactions among employees, when 
considering an  intranet social network.  
An intranet is a private network based on web protocols , belonging to an organization, and usually 
accessible only by the organization's members.  The websites  and software applic ations  of an 
intranet  look and act just like any others, but the firewall  surrounding an intranet fends off 
unauthorized access and use  (Beal, 2017) . An intranet  forum is a social network among  all the 
people  inside a company, in which employees can exchange text messages, share news and 
comments, or multimedia files.  
Enterprise intranets proved to offer important insights to business mangers  (Eppler, 2001) , also 
being a valuable tool to promote  knowledge sharing (Hollingshead, Fulk and Monge, 2002), 
facilitate HR activities  and assess the internal mood  (Sulaiman, Zailani and Ramayah, 2012) . From 
an intranet social network it is  often  possible to extract knowledge maps (Eppler, 2001) . These are  
graph s that provide a visual info rmation about knowledge sources  and help evaluating strengths and 
weaknesses of knowledge related assets . Intranet s are usefu l also from  a Human Resource  point  of 
view , to understand role s and skills  within the organization, current activities and possessed 
knowledge  (DiMicco et al., 2009).  7 
 To the extent of our knowledge, there is no research  trying to combine  words of networks  with 
semantic and social network  analysis of an intranet social network,  with the aim of forecasting  the 
stock price  of a company.  
 
2.1.  Words  and Networks  
In this paper, we propose the analysis of a word co -occurrence network , as an addition to traditional 
semantic analysis. There are  several  studies in this field, for example  Diesner and Carley  (2005)  
showed how  to detect social structure s through text analysis: by analysing  major newspaper s they 
managed to discover the social structure of covert networks – terrorist groups operating in the West 
Bank.  
Network s of words  can be buil t in different ways, such as the analysis of  words  co-occurrence s in 
single sentences or text excerpts (Dagan, Marcus and Markovitch, 1995; Bullinaria and Levy, 
2012) . Centering resonance analysis was also  proposed as a  method for creating a network from a 
text by analysing its centres  (Corman et al. , 2002) . Other scholars used  techniques based on 
hypertext s (Trigg and Weiser, 1986)  or semantic webs (Tim and Berners -Lee, 2006; van Atteveldt, 
2008)  focusing on specific  words and links between  online  pages.   
In our  explorative  study,  we create d a word co -occurrence network , considering  the messages 
extracted from the  intranet forum  of a large Italian company  and the co -occurrence of these words . 
We investigated the position in the graph  of the company brand  – i.e. its centrality measures 
(Freeman, 1978)  – to better predict  the company stock price  (together with the use of more 
conventional social network and semantic variables) . 
 
3. Case Study  
In our case study, we were able to fully crawl the intranet forum  of a large Italian  company , with 
more than 50,000 employees, out of which about 8,000 were actively partic ipating to the online 8 
 discussion s. As per agreed privacy arrangements, w e are prohibited from revealing  more details 
about the company.  
In the intranet forum , employees were al lowed to post news and to comment on their own posts or 
those of others, with no restrictions or pre -approval s from  moderators.  Starting from the analysis of 
these interaction patterns, we were able to create a first Interaction Network , with n nodes  
(employees)  and m directed arcs  (posts) . In this network, there is an arc originating at the node i and 
terminating at the node j, if the social actor i answer s to a comment or a news of the actor j. This 
network – built considering the news and comments posted between September 2014 and June 
2016 , for a total of 94 weeks  – comprises  8320 nodes and 48020 links.  
In addition to studying the interactions  among  people working in the company, we also analysed the 
relationships among the words used in the posts. In particular, we transformed the text of news and 
comments into a Word Network , based on words co -occurrences  (Dagan, Marcus and Markovitch, 
1995;  Bullin aria and Lev y, 2012) . In this network, nodes are representative of single words and arcs 
connect words that co -occur in the text, either conside ring the words before, or after  a specific one, 
with a maximum distance of seven words. To put it in other words , for each co -occurrence within 
this range we created a link going from one word to the other, with a directionality which  respect s 
the order of appearance in the text. To give an example, if a post is “Hello D olly”, then we would  
have two nodes – “Hello” and “D olly”  – with an arc originating at the first node and terminating at 
the second one. Before building the word network, we corrected or removed the misspelled words 
and removed the stop words  - i.e. most common words in the language which usually do not contain 
important significance  - using the Python NLTK library (Perkins, 2014) . The resulting network is 
made of  about 16,00 0 words and more than 6,000,000 links.  
When building networks of words,  differen t techniques can be used  and the maximum range for 
considering a co-occurrence can vary. The choice of a maximum interval of 7 words provided the 
best results in our case study; however , we maintain that the analyst should be free to adjust this 
interval according to the specific context and dataset analysed.  We also considered the possibility to 9 
 simplify our network , by applying lemming or stemming algorithms to reduce words to their root 
forms (Korenius et al. , 2004; Perkins, 2014) . However, a test in this direction produced no b etter 
results than those obtained when we worked  on the full network.  
 
3.1. Study Variables  
Considering the two networks described in the previous section, we extracted  weekly measures for 
a specific set of variables, representative of the social structure, the activity and the employees’  use 
of language. To measure structural positions,  we referred to well -known centrality metrics of Social 
Network Analysis : betweenness and degree centrality (Freeman,  1978) .  
Degree centrality  measures the number of direct connection of a node in the network  and 
corresponds to the number of incident arcs, either originating or terminating at that node . 
Group Degree Centrality  quantifies the variability of individual degree centrality scores and  it is 
used to measure how much centralized a network  is, and so how much  dominated by a set of highly 
central nodes. This measure reaches its maximum of 1 for a star graph (Wasserman and Faust, 
1994) . 
Betweenness Cen trality  is a measure that reflects the number of times a node lies in -between the 
shortest paths that connect every other pair of nodes (Wasserman and Faust, 1994) . 
Group Betweenness Centrality  reflects the heterogeneity of betweenness centrality scores of  single 
actors: for a star graph the value is maximum and equal to 1; for a network where each actor has the 
same level of betweenness, this index is equal to zero  (Wasserman and Faust, 1994) . 
With  regard to the interaction n etwork, we considered group level metrics  with the intent of 
investigating the full network activity, without focusing on single employees; for the word n etwork, 
on the other hand, we focused on the  structural  position s of the node associated to the company 
brand  (which also co rresponds to the company name in the stock market) . Our aim was to test if the 10 
 fluctuations of the brand in this network could be used as a predictor of the  company  stock price  at 
the end of each week (which is our dependent variable) . 
To investigate the u se of language by the  employees  in the intranet forum , we carried out a 
semantic analysis of the content of news and comments, mapping their average  weekly  level of 
sentiment, emotionality and complexity.  
Sentiment  is a measure of the positivity or negativity of a text, calculated  by using a multi -lingual 
classifier based on a machine learning algorithm (trained on large datasets extracted from Twitter) 
(Brönnimann, 2014) . A score of 0.5 represents a neutral sentime nt, whereas higher scores represent 
a positive sentiment , and lower scores a negative one.  
Emotionality  measures the variation in the overall sentiment and it is calculated as its standard 
deviation. If all the messages converg e towards a positive  sentimen t score,  the emotionality will be 
low; by contrast , if there is a frequent  alternation of positive and negative messages,  the 
emotionality will be high  (Brönnimann, 2014) . 
Complexity  measures the distribution of words within a text, c onsidering as more complex the 
words that are less frequently used in a specific context  (Brönnimann, 2014) . Accordingly, a rare 
word that is commonly used in a n intranet forum  will not increase the general level of complexity.  
As the last predictor in our study, we considered the weekly levels of activity  as the number of 
messages posted during a week ( Activity ) or the number of word co -occurrences ( Activity Words ), 
i.e. the nu mber of links in the word network . 
The semantic analysis, as well  as the computation of the centrality  and activity  measures, were 
implemented  by using the software Condor1 and the package NLTK developed for the 
programming language Python 3  (Perkins, 2014) . 
 
1 www.galaxyadvisors.com/product -extended.html  11 
 Lastly, to control for the general stock mar ket fluctuations, and  the events which could affect all the 
market, we included the FTSE MIB in the predictive models . This i ndex is the primary benchmark 
for the Italian equity market; it captures about 76% of the domestic market capitalization.   
As per agreed privacy arrangements, we could not collect other control variables, such as 
employees’ age, gender, job role or busines s department.  
 
4. Results  
We combined the variables  presented in Section 3 to see if they c ould be used as predictors of a 
company price on the  Italian  stock market. Our aim is not to present a final model to make stock 
market prediction s, but to draw  the attention of scholar s on new variables that can be extracted from 
a company intranet forum  and easily integrated in more complex models, to improve the ir 
predictive power.  
We started our analysis by looking at the correlation s between our variables  and the company stock 
price , at various time lags. Specifically, we analysed the insights coming from the intranet forum  
during the same week in which the stock price was collected  (Current Week ) and in the two weeks 
before  (named respectively 1 Week Lag  and 2 Week s Lag ), to see which time interval was more 
informative  (Table 1) . 
 
 
 
 
 
 
 
 12 
  
Table 1. Pearson’s correlation coefficients with stock price (N=94).  
 
For an easier reading , we only reported the correlation coefficients of each variable with the 
company stock price . As the table shows , there are  relatively high correlation s of group  degree and  
group betweenne ss centrality wi th price . These  correlations are  negative  at all lags,  suggesting that 
a more heterogeneous network, less centred on few dominant nodes, is a better indicator of higher 
prices . As regards the semantic variables, sentiment is the only one which shows a significant 
correlation (at lag 1 an d 2); this correlation is negative, probably reflecting the fact that a  more 
technical language  is usually associated to news related to  firm performance and to financial 
events.  As expected, FTS E MIB is highly correlated with  stock price, proving its val ue as a control 
variable.  Lastly, the degree centrality of the company brand  in the word network proved to be 
significantly correlated with price: when the company name is more frequently mentioned in the  
general  discourse, in association with a higher number of different co -occurring words, the stock 
price is higher.  To consider possible autocorrelation ef fects , we implemented  a first differencing of 
the dependent variable  and the Granger causality tests presente d in Table 2 . We considered up to 
three -week  lags, as suggested by the models diagnostics.  
  
Current Week  
Price  Price 1 Week 
Lag Price 2 Weeks Lag  
Activity Words  .421**  .447**  .426**  
Activity (Interaction Network)  -.128 -.121 -.180 
Group Betweenness Centrality (Interaction 
Network)  -.302**  -.256*   -.272**  
Betweenness Centrality (Word Network)  .142 .107 .283*  
Complexity (Interaction Network)  .091 .043 -.034 
Degree Centrality (Word Network)  .369**  .358**  .330**  
Emotionality (Interaction Network)  -.165 -.194 -.148 
Sentiment (Interaction Network)  -.185 -.230*  -.264*  
FTSE MIB  .877**  .863**  .877**  
Group Degree Centrality (Interaction Network)  -.389**  -.286**  -.252*  
*p<.05; **p<.01.     13 
 Dependent: Price  2 
FTSE MIB  3.796 ^ 
Activity (Interaction Network)  .994^  
Activity Words  5.246^  
Group Betweenness Centrality 
(Interaction Network)  3.984^  
Betweenness Centrality (Word 
Network)  3.777^  
Complexity (Interaction Network)  
5.806^  
Degree Centrality (Word Network)  .023^  
Emotionality (Interaction Network)  1.543^  
Sentiment (Interaction Network)  .831^  
Group Degree Centrality (Interaction 
Network)  1.574^  
^p > .05.  
 
Table 2 Granger causality tests ( Chi-squared values, N=94) . 
 
As Table 2  shows , all our predictors can significantly  granger -cause the company stock  price , 
suggesting that they could all be considered while making new predictive models.  
As a last step of our analysis, we b uilt multiple regression models  to forecas t the company stock 
price and comment on the variance explained by each predictor  (Table 3) . We did not use more 
complex time series models – such as ARIMAX  – because the autocorrelation of the stock price 
was not significant in the Durbin -Watson test.  
 
 
 
 
 
 
 
 
 
 14 
  
Table 3. Stock price prediction: multiple regression models.  
 
In the first models, we tested the contribut ion of predictors in blocks,  avoiding to put together 
measures for which  we found multicollinearity problems ( e.g., group degree and betweenne ss 
centrality).  In Model 8,  we combined  all the significant variables which c ould better explain the 
price  variance . With respect to the time  lags of each variable , we chose those who better performed  
in the models.  We found that the FTSE MIB alone explain ed 75.8% of the price variance. The new 
predictors  introduced by this study could increase the variance explained by 9.2%. In particular, we 
found that  negative sentiment  at two weeks lag,  higher activity in the word network at one week 
lag, higher  betweenne ss centrality of the company brand  and lower  group betweenne ss centrality  
are all significant indicators of a higher stock price.  Figure 1 summarizes our findings.  
 
  
Model 1  Model 
2 Model 3  Model 4  Mod
el 5 Model 
6 Model 
7 Model 8  
FTSE MIB  5.34x10E -
5**    
   
5.88x10E -
5** 
Complexity (Interaction 
Network) Lag 0   
.029   
    
Emotionality (Interaction 
Network) Lag1   
-.058   
    
Sentiment (Interaction 
Network) Lag2   
-.108*    
   
-.053**  
Activity Words Lag1    
0.222**   
   
.076**  
Activity (Interaction 
Network) Lag 0     
1.59x10
E-5     
Group Degree Centrality 
(Interaction Network) 
Lag 0      -
.409
*    
Group Betweenness 
Centrality (Interaction 
Network) Lag2     
-.236*     
-.053*  
Betweenness Centrality 
(Word Network) Lag 0      
 .100*   
.114**  
Degree Centrality (Word 
Network) Lag0      
  
.001*
*  
Constant  -.085 1.081*
* .899**  1.081**  1.06
9* .963*
* .922*
* -.179**  
 Adjusted R Square d .758 .046 .205 .069 .142 .032 .127 .851 
*p<.05;
**p<.01  15 
  
 
 
Figure 1 . Significant predictors from the intranet social network . 
 
5. Discussion  and Conclusions  
The objective of this paper was to study  the employees’ communication behaviour  on a large 
company intranet forum , to extract new variables that could be useful to forecast a company’s  stock 
price . We did not mean to p resent final forecasting models  but to give evidence to the importance of 
new metr ics that can be integrated in existing financial models to improve their accuracy . In this 
sense, our work has practical  and theoretical  implications for scholars, financial analysts and 
professional investors.    
By means of Granger causality tests, we proved the value of our metrics up to two -week lags. 
Subsequently, we included our predictors in multiple regression models  to see whether they could 
improve the models accuracy with respect to  the control va riable FTSE MIB.  
Our work  is consistent with previous studies linking sentiment analysis and stock prices (Chung and 
Liu, 2011; Zhang, Fuehres and Gloor, 2011) : we found a negative corre lation of the stock price with 
the sentiment of messages posted  in the company intranet forum . This cou ld be partially explained 
by a more technical language used in news which deal with firm performance (with a lower 
sentiment , for example,  when compared to posts where employees discuss their leisure activities 
16 
 promoted by the company) . It could be the case that more irrelevant and positive messages  are 
dominant in the network when there are no big events that can positively affect the stock  price (this 
hypothesis was partially confirmed by an interview with the people in charge of monitoring the 
employees’ activity on the intranet forum).  A more “democratic” network structure  (lower group 
betweenness centrality) , less dominated by a smaller number of social actors, also proved to be 
predictive of higher prices.  
We offer a new contribution  since we are among the first scholars linking a company stock price 
with the communication  behaviours  on its intranet forum . Even if the informative power  of the  
analysis of the  employees’ activity on enterprise intranets has been discussed in the past ( Eppler, 
2001; DiMicco et al., 2009) , its association with  the market value of a company remain s mostly  
unexplored.  We support the id ea that internal  communication behaviours can affect and  partially 
mirror  business performance  (Gloor et al., 2017 b), thus influencing  a company ’s stock price.  
An additional contribution of this research is to present a new use of word co-occurrence network s, 
which  helps financial predictions . We transformed the content  of the messages posted on the 
intranet forum into a word co -occurrence network  – where every node, representative of a specific 
word, was linked to the others based on th eir co -occurrences  in messages  (Dagan, Marcus and 
Markovitch, 1995 ); on this  network , we studied the associations between specific positions of the 
company  brand and its stock price. Looking at the patt erns interconnecting words, we saw  that a 
higher activity  – i.e. a denser  graph with more co -occurrences  – and a higher betweenness centrality 
of the company brand can predict higher prices. This last result  could be representative of cases 
where the brand is more widely used, spreading ac ross many different news and comments  and 
therefore being more central in the discourse .   
Some of the l imitations of this study are due to  our impossibility to analyse  more companies or 
different  business sector s. Moreover, we could not consider the communication and the interactions 17 
 between employees  which  went  through other media  – such as emails, instant messaging  apps, 
phone calls or face -to-face communication.  
To extend our results , we advocate further researc h to study the intranet forum s of more firms, also 
taking into account  their size and business sector.  It would also be important to consider larger time 
frames , to test other variables and network construction techniques , and to develop predictive 
models based on more complex machine learning algorithms (which already proved their value in 
the past).   
Due to privacy agreements, we could not access to more control variables to differentiate  the 
interactions based on the individual differe nces of employees. Therefore, future researchers should 
consider taking  into account  factors  such as employees’ age, gender and job role . Lastly , it could be 
interesting to study if more accurate models c an be obtained  by isolating the communication taking 
place in specific business departments or geographical locations . Depending on the business 
context,  one could focus the  attention to  the communication of employees  working in specific 
business units  (customer care, financial department, etc ...). 
 
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