[
  {
    "id": "analyst_revision_rank_derivative",
    "description": "Change in ranking for analyst revisions and momentum compared to previous period.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "cashflow_efficiency_rank_derivative",
    "description": "Change in ranking for cash flow generation and profitability compared to previous period.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "composite_factor_score_derivative",
    "description": "Change in overall composite factor score from the prior period.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "earnings_certainty_rank_derivative",
    "description": "Change in ranking for earnings sustainability and certainty compared to previous period.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_bfl_growth",
    "description": "The purpose of this metric is to qualify the expected MT growth potential of the stock.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_bfl_momentum",
    "description": "The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_bfl_profitability",
    "description": "The purpose of this metric is to rank stock based on their ability to generate cash flows.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_bfl_quality",
    "description": "The purpose of this metric is to measure both the sustainability and certainty of earnings.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_bfl_surface",
    "description": "The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_bfl_surface_accel",
    "description": "The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_bfl_total",
    "description": "The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_bfl_value",
    "description": "The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_growth",
    "description": "The purpose of this metric is to qualify the expected MT growth potential of the stock.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_momentum",
    "description": "The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_profitability",
    "description": "The purpose of this metric is to rank stock based on their ability to generate cash flows.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_quality",
    "description": "The purpose of this metric is to measure both the sustainability and certainty of earnings.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_surface",
    "description": "The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_surface_accel",
    "description": "The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_total",
    "description": "The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "fscore_value",
    "description": "The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "growth_potential_rank_derivative",
    "description": "Change in ranking for medium-term growth potential compared to previous period.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "multi_factor_acceleration_score_derivative",
    "description": "Change in the acceleration of multi-factor score compared to previous period.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "multi_factor_static_score_derivative",
    "description": "Change in static multi-factor score compared to previous period.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  },
  {
    "id": "relative_valuation_rank_derivative",
    "description": "Change in ranking for valuation metrics compared to previous period.",
    "dataset_id": "model16",
    "dataset_name": "Fundamental Scores",
    "category_id": "model",
    "category_name": "Model",
    "region": "USA",
    "delay": 1,
    "universe": "TOP3000",
    "type": "MATRIX"
  }
]