{"id":"W2496934712","doi":"10.4018/978-1-4666-5958-2.ch010","title":"Practical Machine Learning in Financial Market Trend Prediction","year":2014,"lang":"en","type":"book-chapter","venue":"Advances in business information systems and analytics book series","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Support vector machine; Portfolio; Artificial intelligence; Computer science; Probabilistic logic; Wavelet; Machine learning; Artificial neural network; Probabilistic neural network; Econometrics; Pattern recognition (psychology); Finance; Mathematics; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004981352,0.000416801,0.0009466292,0.001453965,0.0001723938,0.0004955128,0.0002617348,0.0003984191,0.0001456406],"category_scores_gemma":[0.007769836,0.0003492204,0.00007841132,0.0004874865,0.0002391882,0.005832475,0.0001796068,0.0006149401,0.00002830136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001287718,"about_ca_system_score_gemma":0.0001735155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002729438,"about_ca_topic_score_gemma":0.0003641853,"domain_scores_codex":[0.9956769,0.0002231245,0.002200486,0.0004123052,0.001188116,0.0002990452],"domain_scores_gemma":[0.99583,0.001607568,0.001574353,0.0003907419,0.0005114792,0.00008585149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001084026,0.00003053091,0.02118902,0.001385461,0.0000498197,0.00005717235,0.001129329,0.03543205,6.419656e-7,0.4947703,0.007501822,0.4373698],"study_design_scores_gemma":[0.0004108711,0.0000601797,0.002701313,0.0006675197,0.00002013703,0.00007729434,0.0001573288,0.04477938,3.540089e-7,0.004869932,0.9459332,0.0003224599],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00005862966,0.004805364,0.0774124,0.0004368919,0.002115608,0.0006390168,0.000162067,0.0001068498,0.9142632],"genre_scores_gemma":[0.03748339,0.04168696,0.01179424,0.0005157492,0.001379424,0.0001690713,0.0005381771,0.0001537661,0.9062792],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9384314,"threshold_uncertainty_score":0.999896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04459774193976328,"score_gpt":0.3365633060561439,"score_spread":0.2919655641163806,"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."}}