Workspace + refinitiv.data – Analyst EPS history: some brokers visible, others “Permission Denied

petaleks

Hi everyone,

I’m working with Refinitiv Workspace and the Refinitiv Data Library for Python (refinitiv.data) for academic research on analyst EPS consensus.

My goal is to retrieve analyst-level EPS broker estimates over time for an equity (here: ASSAb.ST), so that I can study how individual analyst contributions to the consensus evolve relative to various factors. I do not necessarily need the real broker/analyst names – stable anonymous identifiers would be fine – but I do need to be able to track individual contributors over time.

Based on an example provided by Refinitiv Support, I am using the following Python code:

#1. activate 
import os
import pandas as pd
pd.set_option("future.no_silent_downcasting", True)
import numpy as np
import refinitiv.data as rd
from refinitiv.data import get_data
from datetime import datetime, date
import matplotlib.pyplot as plt

rd.open_session() 



# 2. Parameters
RIC    = "ASSAb.ST"
PERIOD = "1FQ2019"          # e.g. "FY1", "1FQ2024", etc.
FRQ    = "FQ"               # "FQ" = quarterly, "FY" = yearly
START  = "2017-10-01"
END    = "2019-01-29"

# 3. Analyst-level EPS history with time window
df = get_data(
    universe=[RIC],
    fields=[
        "TR.EPSEstValue",           # EPS estimate
        "TR.EPSEstValue.BrokerName",
        "TR.EPSEstValue.AnalystName",
        "TR.EPSEstValue.AnalystCode",
        "TR.EPSEstValue.FPeriod",   # fiscal period
        "TR.EPSEstValue.Date",
    ],
    parameters={
        "Period": PERIOD,
        "Frq":    FRQ,
        "SDate":  START,
        "EDate":  END,
        # "Curn": "USD",            # optional, if you need a specific currency
    }
)

# 4. Clean + order
if "Date" in df.columns:
    df["Date"] = pd.to_datetime(df["Date"], errors="coerce")

if "Broker Name" in df.columns and "Date" in df.columns:
    df = df.sort_values(["Broker Name", "Date"])

df

broker_analyst_over_time.jpg

This works in the sense that it returns analyst-level rows over time.
However, the result looks like this (simplified):

• some rows:
  • Broker Name = "PARETO SECURITIES AS"
  • Analyst Name = "Roslund, Anders"
  • Analyst Code = "7JD2"
• other rows for the same RIC / period:
  • Broker Name = "Permission Denied 1120"
  • Analyst Name = "Permission Denied"
  • Analyst Code is empty

This is the same pattern I see in Workspace Excel: some contributors are fully visible, others show as "Permission Denied ####".

For my research, I am not asking to reveal the hidden names.
What I really need is:

1. Analyst-level EPS estimates over time (which this query already provides), and
2. Some way to track each hidden contributor consistently over time, even if anonymous.

So my questions are:

1. Do the "Permission Denied ####" codes correspond to stable anonymous identifiers (i.e., does the same number always refer to the same hidden broker/analyst across time and instruments), or are they just generic placeholders with no stability guarantee?
2. Is there any recommended API approach or alternative fields that would allow me to obtain stable, anonymous contributor IDs for EPS estimates, suitable for research on analyst consensus, without revealing actual names?
3. If this is not possible via TR.EPSEstValue and related fields, are there other datasets or APIs within refinitiv.data that are better suited for analyst-level historical consensus research?

Environment details (if helpful):

• Workspace desktop session + refinitiv.data Python library
• Python 3.11 (Windows)
• Data requested via rd.open_session() (Workspace) and refinitiv.data.get_data(...) as above.

Any guidance or clarification on how to handle these "Permission Denied ####" rows for research purposes would be greatly appreciated. I am happy to provide a small sample of the DataFrame or the equivalent Excel output if needed.

Many thanks in advance,
Aleksandar

Jirapongse

@petaleks

Thank you for reaching out to us.

This forum is dedicated to software developers using LSEG APIs. The moderators on this forum do not have deep expertise in every bit of content available through LSEG products, which is required to answer content questions such as this one.

The best resource for content questions is the Helpdesk support team, which can be reached by submitting queries through LSEG Support. The support team will either have the required content expertise ready available or can reach out to relevant content experts to get the answer for you. The data returned by both Excel and the API comes from the same source, so the content team should be able to provide an explanation.

You need to ask for the Excel formula, such as =RDP.Data, that can be used to retrieve the required data. Then, we can help you converting that formula to Python code.

Otherwise, you can use the Data Item Browser tool to search for fields and parameters. Then, apply those fields and parameters to the Python code.

Please include the URL of this discussion in your raised question to prevent it from being redirected back to this Q&A forum.