Tracking Traders' Understanding of the Market Using e-Communication Data
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{"title"=>"Tracking traders' understanding of the market using e-communication data", "type"=>"journal", "authors"=>[{"first_name"=>"Serguei", "last_name"=>"Saavedra", "scopus_author_id"=>"25643441300"}, {"first_name"=>"Jordi", "last_name"=>"Duch", "scopus_author_id"=>"55919149800"}, {"first_name"=>"Brian", "last_name"=>"Uzzi", "scopus_author_id"=>"6603149512"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"22046335", "doi"=>"10.1371/journal.pone.0026705", "sgr"=>"80055038785", "arxiv"=>"1110.5962", "isbn"=>"1932-6203", "scopus"=>"2-s2.0-80055038785", "issn"=>"19326203", "pui"=>"362825442"}, "id"=>"fa1081b2-8e19-356b-99d3-2c0ab1320cf5", "abstract"=>"Tracking the volume of keywords in Internet searches, message boards, or Tweets has provided an alternative for following or predicting associations between popular interest or disease incidences. Here, we extend that research by examining the role of e-communications among day traders and their collective understanding of the market. Our study introduces a general method that focuses on bundles of words that behave differently from daily communication routines, and uses original data covering the content of instant messages among all day traders at a trading firm over a 40-month period. Analyses show that two word bundles convey traders' understanding of same day market events and potential next day market events. We find that when market volatility is high, traders' communications are dominated by same day events, and when volatility is low, communications are dominated by next day events. We show that the stronger the traders' attention to either same day or next day events, the higher their collective trading performance. We conclude that e-communication among traders is a product of mass collaboration over diverse viewpoints that embodies unique information about their weak or strong understanding of the market.", "link"=>"http://www.mendeley.com/research/tracking-traders-understanding-market-using-ecommunication-data", "reader_count"=>55, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>3, "Librarian"=>2, "Student > Doctoral Student"=>2, "Researcher"=>6, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>1, "Student > Master"=>9, "Other"=>4, "Student > Bachelor"=>5, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>3, "Librarian"=>2, "Student > Doctoral Student"=>2, "Researcher"=>6, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>1, "Student > Master"=>9, "Other"=>4, "Student > Bachelor"=>5, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Agricultural and Biological Sciences"=>3, "Business, Management and Accounting"=>9, "Chemistry"=>1, "Computer Science"=>7, "Decision Sciences"=>2, "Economics, Econometrics and Finance"=>7, "Engineering"=>2, "Environmental Science"=>4, "Mathematics"=>2, "Medicine and Dentistry"=>1, "Physics and Astronomy"=>3, "Psychology"=>1, "Social Sciences"=>7}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>7}, "Decision Sciences"=>{"Decision Sciences"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>3}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>6}, "Environmental Science"=>{"Environmental Science"=>4}, "Engineering"=>{"Engineering"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>7}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Computer Science"=>{"Computer Science"=>7}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>9}}, "reader_count_by_country"=>{"Sweden"=>1, "United States"=>1, "China"=>1, "Ireland"=>1, "Luxembourg"=>1, "Japan"=>1, "Switzerland"=>4, "Germany"=>1, "Spain"=>2}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/720376"], "description"=>"<p>Panel <b>A</b> shows that the cumulative frequency on a log-log scale of filtered words <i>f<sub>w</sub></i> (viz. word counts) is approximately distributed following Zipf's law according to a power law (KS test, <i>p</i> = 0.21). Panel <b>B</b> shows the evolution of traders' messaging volume defined as the total daily number of words on a log scale. Note that the daily frequency of words that follow communication routines can be approximated simply by its global frequency and the total number of words in each day.</p>", "links"=>[], "tags"=>["computer science", "physics"], "article_id"=>390739, "categories"=>["Information And Computing Sciences", "Physics"], "users"=>["Serguei Saavedra", "Jordi Duch", "Brian Uzzi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0026705.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Communication_routines_/390739", "title"=>"<b>Communication routines.</b>", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 12:22:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/720430"], "description"=>"<p>Daily frequency of illustrative extracted words. The figure shows the daily frequency (number of times a word is counted each day) of illustrative extracted words <b>A</b> S&P, <b>B</b> oil, <b>C</b> trust and <b>D</b> rumors across the observation period. Note that each word has a unique temporal dynamic characterized by brief periods of intense usage–bursts–preceded by and followed by relatively long periods of low usage.</p>", "links"=>[], "tags"=>["computer science", "physics"], "article_id"=>390796, "categories"=>["Information And Computing Sciences", "Physics"], "users"=>["Serguei Saavedra", "Jordi Duch", "Brian Uzzi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0026705.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Figure_2_/390796", "title"=>"Figure 2", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 12:22:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/720483"], "description"=>"<p>The figure shows the time dependent cross-correlations between day-to-day movements of market volatility given by the daily changes in closing values of VIX <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0026705#pone.0026705-Whaley1\" target=\"_blank\">[17]</a> at time <i>t</i>, and <b>A</b> the day-to-day movements of relative frequency of word bundle one over Δ<i>t</i> and <b>B</b> word bundle two over Δ<i>t</i>. Day-to-day movements are calculated using the first differences. High-low bars indicate the 95% confidence intervals using Fisher's transformation. The red dashed lines indicate the 95% confidence interval for cross-correlations of two independent and identically distributed random variables across the same observation period.</p>", "links"=>[], "tags"=>["volatility"], "article_id"=>390856, "categories"=>["Information And Computing Sciences", "Physics"], "users"=>["Serguei Saavedra", "Jordi Duch", "Brian Uzzi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0026705.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cross_correlations_between_market_volatility_and_word_bundles_/390856", "title"=>"<b>Cross-correlations between market volatility and word bundles.</b>", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 12:23:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/720560"], "description"=>"<p>Triangles show the relative frequency of traders' understanding of same day events (word bundle one) compared to the relative frequency of traders' understanding of next day events (word bundle two) as given by normalized to the <i>z</i>-score using the sample mean and standard deviation. The orange circles show market volatility over time normalized to the <i>z</i>-score using the sample mean and standard deviation calculated over all the data. Orange circles above dashed line represent days of high and low volatility respectively. Black triangles above the dashed line represent days when word bundle one dominated (), and green triangles below the dashed line represent days when word bundle two dominated the content of traders' IM communications (). These time-series patterns indicate that same day events (word bundle one) systematically dominate traders' understanding on days of high volatility while next day events (word bundle two) systematically dominate on days of low volatility. For visibility purposes, solid lines correspond to the moving average computed using a kernel smoothing regression with a window of one month.</p>", "links"=>[], "tags"=>["temporal"], "article_id"=>390930, "categories"=>["Information And Computing Sciences", "Physics"], "users"=>["Serguei Saavedra", "Jordi Duch", "Brian Uzzi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0026705.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Traders_temporal_understanding_of_market_volatility_/390930", "title"=>"<b>Traders' temporal understanding of market volatility.</b>", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 12:23:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/720600"], "description"=>"<p>The figure shows the relationship between the first differences of collective attention (x-axis) and first differences of collective trading performance (y-axis). We found a significant (<i>p</i><10<sup>−4</sup>), positive correlation of 0.19. The red dashed line corresponds to the best linear fit (<i>p</i> = 0.002) over all data points and it is used only to guide the eye for the positive correlation.</p>", "links"=>[], "tags"=>["computer science", "physics"], "article_id"=>390964, "categories"=>["Information And Computing Sciences", "Physics"], "users"=>["Serguei Saavedra", "Jordi Duch", "Brian Uzzi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0026705.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_between_collective_attention_and_collective_trading_performance_/390964", "title"=>"<b>Correlation between collective attention and collective trading performance.</b>", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 12:23:44"}

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  • {"unique-ip"=>"11", "full-text"=>"13", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"10", "full-text"=>"9", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"10", "full-text"=>"10", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"10", "full-text"=>"9", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"8", "full-text"=>"11", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}

Relative Metric

{"start_date"=>"2011-01-01T00:00:00Z", "end_date"=>"2011-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Computer and information sciences", "average_usage"=>[355, 609, 735, 840, 944, 1029, 1109, 1193, 1255, 1320, 1391, 1469, 1540, 1608, 1683, 1753, 1824, 1905, 1967, 2042, 2117, 2189, 2240, 2300, 2392, 2452, 2509, 2569, 2635, 2710, 2772, 2846, 2882, 2956, 3014, 3086, 3139]}, {"subject_area"=>"/Physical sciences", "average_usage"=>[303, 547, 667, 770, 875, 974, 1062, 1140, 1210, 1276, 1344, 1408, 1473, 1534, 1602, 1665, 1732, 1800, 1863, 1920, 1984, 2048, 2099, 2161, 2222, 2285, 2341, 2404, 2473, 2528, 2595, 2650, 2713, 2772, 2835, 2886, 2938]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[347, 582, 700, 805, 909, 997, 1061, 1147, 1220, 1282, 1350, 1411, 1471, 1532, 1600, 1665, 1726, 1790, 1868, 1927, 1986, 2048, 2105, 2166, 2229, 2288, 2347, 2407, 2463, 2525, 2589, 2650, 2704, 2755, 2806, 2854, 2896]}, {"subject_area"=>"/Social sciences", "average_usage"=>[340, 584, 708, 809, 915, 1005, 1089, 1174, 1254, 1334, 1408, 1478, 1563, 1648, 1716, 1791, 1847, 1915, 1979, 2044, 2105, 2163, 2218, 2297, 2382, 2436, 2493, 2553, 2618, 2682, 2741, 2786, 2847, 2937, 2989, 3039, 3102]}, {"subject_area"=>"/Social sciences/Sociology", "average_usage"=>[426, 807, 983, 1078, 1202, 1297, 1394, 1505, 1611, 1713, 1819, 1918, 2007, 2092, 2190, 2261, 2341, 2445, 2522, 2612, 2692, 2754, 2830, 2908, 2987, 3076, 3157, 3246, 3319, 3431, 3527, 3602, 3670, 3725, 3772, 3821, 3879]}]}
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