{"id":"W2963434388","doi":"10.18653/v1/n16-1041","title":"Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leverage (statistics); Word embedding; Stock market; Stock (firearms); Artificial neural network; Embedding; Computer science; Financial market; Market data; Stock price; Econometrics; Artificial intelligence; Finance; Business; Economics; Series (stratigraphy); Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005759405,0.001507703,0.0006385774,0.001869578,0.0001980299,0.0007615453,0.000439701,0.0006138664,0.001224327],"category_scores_gemma":[0.001957656,0.0003321261,0.0004010054,0.001255034,0.0001755439,0.002300949,0.0006164085,0.001190787,0.0007483776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003454429,"about_ca_system_score_gemma":0.0003938417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005279716,"about_ca_topic_score_gemma":0.009383082,"domain_scores_codex":[0.9997299,0.00004386932,0.00003297513,0.00007630142,0.00006852987,0.00004837785],"domain_scores_gemma":[0.9991636,0.0003828856,0.0001309126,0.00006896815,0.0002038064,0.00004978559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004456013,0.001091889,0.03912901,0.0001441363,0.0003618684,0.0003253228,0.00009688954,0.2153132,0.008940421,0.001978562,0.007357357,0.7248157],"study_design_scores_gemma":[0.00001114441,0.0000479871,0.002401432,0.000008971204,0.00002283358,0.00001786311,0.00001568879,0.993769,0.001589292,0.001696621,0.000408715,0.00001045086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6688083,0.004420034,0.3129307,0.001305768,0.0007730577,0.0001302294,0.002297868,0.002936878,0.006397253],"genre_scores_gemma":[0.9567097,0.0007931186,0.03675564,0.0001193311,0.0002063867,0.00003543997,0.002386472,0.00004200637,0.002951813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005279716,"threshold_uncertainty_score":0.01049799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09734152973611282,"score_gpt":0.379394159528915,"score_spread":0.2820526297928022,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}