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Deep Learning for Stock Prediction

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Presentation by Prof Yue Zhang at DataScience SG meetup.

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Deep Learning for Stock Prediction

  1. 1. Deep  Learning  for  Stock   Prediction Yue  Zhang
  2. 2. My  research  areas Machine Learning Natural Language Processing Applications Text synthesis Machine translation Information extractionMarket prediction Sentiment analysis Syntactic analysis
  3. 3. This  talk • Reading  news  from  the  Internet  and   predicting  the  stock  market
  4. 4. Outline • Event-­‐driven  predict • Two  extensions
  5. 5. Introduction • Is  it  possible? – Random  walk  theory – Efficient  market  hypothesis – Human/algorithm  trading • Examples – Shares  of  Apple  Inc.  fell  as  trading  began  in  New  York   on  Tuesday  morning,  the  day  after  former  CEO  Steve   Jobs  passed  away – Google’s  stock  falls  after  grim  earnings  come  out  early
  6. 6. Why  events? • Previous  work – Bag-­‐of-­‐words – Named  Entities – Noun  Phrases • Examples – Oracle  Corp  would  sue  Google  Inc.,  claiming  Google’s   Android  operating  system… – Microsoft  agrees  to  buy  Nokia’s  mobile  phone   business  for  $  7.2  billion.
  7. 7. Method • Event  Representation – E=(O1,  P,  O2,  T) – Actor – Event – Object – Time
  8. 8. Method • Event  Extraction – Syntactic  parsing – Open  information  extraction
  9. 9. Method • Event  Generalization – First,  we  construct  a  morphological  analysis  tool   based  on  the  WordNet stemmer  to  extract  lemma   forms  of  inflected  words – Second,  we  generalize  each  verb  to  its  class  name  in   VerbNet • For  example – Instant  view:  Private  sector  adds  114,000  jobs  in  July. – (Private  sector,  adds,  114,000  jobs) – (private  sector,  multiply_class,  114,000  job)
  10. 10. Method • Model – Input:  events – Output:  two-­‐way  movement • Training:  historical  data • Testing:  coming  data
  11. 11. Method • Prediction  Model – Linear  model • Most  previous  work  uses  linear  models  to  predict  the  stock   market.  To  make  direct  comparisons,  this  paper  constructs  a   linear  prediction  model  by  using  SVM  with  linear  kernel – Nonlinear  model • Intuitively,  the  relationship  between  events  and  the  stock   market  may  be  more  complex  than  linear,  due  to  hidden  and   indirect  relationships.  We  exploit  a  deep  neural  network   model,  the  hidden  layers  of  which  is  useful  for  learning  such   hidden  relationships
  12. 12. … News  documents φ1 Class  +1 The  polarity  of  the  stock   price  movement   is   positive Class  -­‐1 The  polarity  of  the  stock   price  movement   is   negative Input   Layer Output   Layer Hidden   Layers … … φ2 φ3 φM
  13. 13. Method • Feature  Representation – Bag-­‐of-­‐words • TF*IDF – Events • O1,  P,  O2,  O1  +  P,  P  +  O2,  O1  +  P  +  O2 • For  Example – (Microsoft,  buy,  Nokia's  mobile  phone  business) – (#arg1=Microsoft,  #action=buy,  #arg2= Nokia's  mobile  phone   business,  #arg1_action=Microsoft  buy,  #action_arg2=buy   Nokia's  mobile  phone  business,  #arg1_action_arg2=   Microsoft  buy  Nokia's  mobile  phone  business)
  14. 14. Experiments • Data  Description – We  use  publicly  available  financial  news  from  Reuters   and  Bloomberg  over  the  period  from  October  2006  to   November  2013.  This  time  span  witnesses  a  severe   economic  downturn  in  2007-­‐2010,  followed  by  a   modest  recovery  in  2011-­‐2013.  There  are  106,521   documents  in  total  from  Reuters  News  and  447,145   from  Bloomberg  News. – We  mainly  focus  on  predicting  the  Standard  &Poor's   500  stocks  (S&P  500)  index,  obtaining  indices  and   stock  price  from  Yahoo  Finance.
  15. 15. Experiments • Evaluation  Metrics – Accuracy  and  MCC • Overall  Results
  16. 16. Experiments • Experiments  with  Different  Number  of  Hidden   Layers  of  the  Deep  Neural  Network  Model
  17. 17. Experiments • Experiments  with  Different  Quantities  of  Data
  18. 18. Experiments • Individual  Stock  Prediction
  19. 19. Experiments • Individual  Stock  Prediction
  20. 20. Experiments • Individual  Stock  Prediction
  21. 21. Conclusion • Events  are  useful.   – Events  are  more  useful  representations  compared  to  bags-­‐of-­‐words  for  the   task  of  stock  market  prediction. • Hidden  relations  useful. – A  deep  neural  network  model  can  be  more  accurate  on  predicting  the  stock   market  compared  to  the  linear  model. • Robust  results  obtained. – Our  approach  can  achieve  stable  experiment  results  on  S&P  500  index   prediction  and  individual  stock  prediction  over  a  large  amount  of  data  (eight   years  of  stock  prices  and  more  than  550,000  pieces  of  news). • Quality  of  information  is  more  important  than  quantity.   – The  most  relevant  information   (i.e.  news  title  vs news  content,  individual   company  news  vs all  news)  is  better  than  more,  but  less  relevant  information.
  22. 22. Two  extensions • Better event encodings • Long term history
  23. 23. Two  extensions • Event  sparsity – Using  structured  event  to  predict  stock  market   movement  suffers  from  increased  data  sparsity (Actor  =  Microsoft,  Action  =  sues,  Object  =  Barnes  &  Noble)
  24. 24. Two  extensions • Modeling  long-­‐term  effect  of  events – The  effect  becomes  weaker – Little  has  been  done
  25. 25. Event  Embedding • Previous work – Learning entity embedding (Socher et al. 2013)
  26. 26. Neural  Tensor  Network 𝑓(𝑒$ % 𝑊 $:( 𝑒) + 𝑉 ,- ,. + 𝑏)𝑓(𝑊𝑥 + 𝑏) Neural Network Neural Tensor Network
  27. 27. Neural Tensor Network for Event Embedding O1 T1 P T2 O2 R1 R2 U T3
  28. 28. O1 T1 P R1 𝑅$ =   𝑓(𝑂$ % 𝑇$ [$:(] 𝑃 + 𝑉 :- ; + 𝑏) Neural Tensor Network for Event Embedding
  29. 29. Training • Minimize the margin loss • 500 iterations • Standard back-­‐propagation Random replace with an object Regulation weight,set to 0.0001 Parameters
  30. 30. Deep  Prediction  Model • We model long-­‐, mid-­‐, short-­‐term events – Long-­‐term events (Last month) – Mid-­‐term events (Last week) – Short-­‐term events (Last day)
  31. 31. Deep  Prediction  Model • Architecture
  32. 32. Deep  Prediction  Model • Convolution and Max-­‐pooling – Convolution layer to obtain local feature – Max-­‐pooling to determine the global representativefeature
  33. 33. Experiments • Baselines Input Method Luss and d’Aspremont [2012] Bag of words NN Ding et al. [2014] (E-NN) Structured event NN WB-NN Word embedding NN WB-CNN Word embedding CNN E-CNN Structured event CNN EB-NN Event embedding NN EB-CNN Event embedding CNN
  34. 34. Experiments • Finds – Events  are  better  features  than  words – Reducing  sparsity if  helpful  in  the  task – CNN-­‐based  is  more  powerful
  35. 35. Experiments • 15  companies  from  S&P  500 – Consists  of  High-­‐,mid-­‐ and  low-­‐ranking  companies – Evaluation  metric:  Accuracy  and  MCC
  36. 36. Conclusion • Event  embeddings-­‐based  document   representations  are  better  than  discrete   events-­‐based  methods • Deep  CNN  can  help  capture  longer-­‐term   influence  of  news  event
  37. 37. Current • More technical enhancements • More  markets – China’s  A  market – Chinese  syntactic  and  semantic  analysis – Chinese  Open  IE

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