I'll help analyze which bank analysts have better prediction accuracy. Let me outline the approach:

1. First, I'll get analyst predictions for major banks (Goldman Sachs, JP Morgan, Morgan Stanley, etc.)
2. For each prediction, I'll compare the price target with the actual price movement to calculate accuracy
3. Calculate metrics like:
   • Average prediction error
   • Success rate (% of predictions that moved in the correct direction)
   • Average return if following their recommendations

However, before proceeding, I need a few clarifications:

1. What time period would you like to analyze? (e.g., last 1 year, 3 years, etc.)
2. Would you like to focus on specific stocks/sectors for the analysis, or should we analyze all their predictions?
3. Should we consider only price targets or include buy/sell recommendations as well?

Please provide these details so I can generate the most relevant analysis for you.

I'll analyze analyst predictions from major banks over the last 3 years. Here's the logic I'll implement:

1. Get predictions from major banks using get_analyst_predictions with company names
2. For each prediction:
   • Get the actual stock price data using Scalar Trades Data Repository
   • Calculate accuracy by comparing price target vs actual price movement over next 12 months
   • Calculate metrics like mean absolute percentage error (MAPE) and directional accuracy

Let me generate the procedure:

I understand the issue. The error occurs because we're trying to access price data for a period where no data exists. Let me fix the procedure with better error handling and debug statements:

I'll create visualizations using plotly to show the comparison of bank analysts' performance metrics. Since we already have the results, we don't need to fetch any additional data.

I'll create a more detailed visualization of directional accuracy using a combination of bar plot with error bars and a scatter plot overlay. This will help highlight the differences more clearly.

I'll create a visualization that shows both the volume (total predictions) and quality (accuracy and error rates) of predictions for each bank. I'll use a combination of bar and scatter plots to show these multiple dimensions effectively.

Here's a LinkedIn post summarizing the analysis:

📊 Just completed an analysis of Wall Street's top bank analysts over the past 3 years. Here are some fascinating insights:

🎯 Prediction Accuracy: • Citigroup leads with 54.47% directional accuracy • Goldman Sachs follows at 53.09% • Morgan Stanley at 52.95% • Wells Fargo at 51.96%

📈 Volume & Quality: • Morgan Stanley: Most active with 3,001 predictions • Wells Fargo: 1,913 predictions • Goldman Sachs: 1,554 predictions • Citigroup: 1,254 predictions

🔍 Key Takeaways:

1. All banks perform slightly better than a coin flip (50%)
2. Higher volume doesn't correlate with better accuracy
3. Citigroup shows best accuracy despite fewer predictions

💭 Food for thought: In a market where even top analysts barely beat 50/50 odds, how should retail investors approach stock picking?

#FinancialAnalysis #WallStreet #InvestmentBanking #DataAnalytics #MarketResearch

Would you like me to modify any aspect of this post?

Here's a Reddit post for r/stocks or r/investing:

Analysis: How accurate are Wall Street's top bank analysts? [3-year data]

TL;DR: They're barely better than a coin flip, but some interesting patterns emerge.

I analyzed prediction accuracy of major banks over the past 3 years (2022-2025). Here's what I found:

Directional Accuracy:

• Citigroup: 54.47%
• Goldman Sachs: 53.09%
• Morgan Stanley: 52.95%
• Wells Fargo: 51.96%

Volume of Predictions:

• Morgan Stanley: 3,001
• Wells Fargo: 1,913
• Goldman Sachs: 1,554
• Citigroup: 1,254

Average Error Rate:

• Wells Fargo: 248.66%
• Goldman Sachs: 128.14%
• Citigroup: 107.76%
• Morgan Stanley: 91.38%

Interesting Findings:

1. The most accurate bank (Citigroup) made the fewest predictions
2. The bank with most predictions (Morgan Stanley) had the lowest error rate
3. All banks barely beat a coin flip in directional accuracy
4. Wells Fargo had highest error rate despite being second in volume

Methodology:

• Analyzed all public price targets from these banks
• Compared predictions with actual price movements over 12 months
• Excluded predictions where 12-month price data wasn't available
• Counted a prediction as correct if price moved in predicted direction

What do you think this means for retail investors following analyst recommendations? Should we pay less attention to price targets and more to other factors?

Edit: Thanks for the awards! For those asking about data source - this was analyzed using Scalar's data repository.

Would you like me to modify any aspect of this post?

Here's an enhanced Reddit post with rich formatting:

🏦 Wall Street's Crystal Ball: How Accurate are Bank Analysts? [Data Analysis]

^(A 3-year analysis of top banks' stock predictions | Data: 2022-2025)

🎯 TL;DR:

Bank analysts are barely better than a coin flip, but some interesting patterns emerge in their 7,722 total predictions.

📊 The Numbers:

Directional Accuracy (% of correct predictions):

🔍 Key Findings:

1. Quality vs. Quantity

   • Citigroup: Highest accuracy ↔️ Fewest predictions
   • Morgan Stanley: Most predictions ↔️ Lowest error rate

2. Accuracy Spectrum

   • Best: Citigroup (54.47%)
   • Worst: Wells Fargo (51.96%)
   • Spread: Only 2.51% difference between best and worst

3. Error Rates

   • Best: Morgan Stanley (91.38%)
   • Worst: Wells Fargo (248.66%)
   • 🤔 Higher error rates don't necessarily mean lower directional accuracy

📈 Visualization:

[Editor's note: Graphs would be inserted here]

🧪 Methodology:

• Analyzed all public price targets (2022-2025)
• Tracked 12-month price movements
• Excluded incomplete data points
• Success = Price moving in predicted direction
• Total sample size: 7,722 predictions

💭 Discussion Points:

1. Why do you think banks with fewer predictions tend to be more accurate?
2. Is a 2.51% accuracy spread significant enough to prefer one bank's analysis over another?
3. Should retail investors care about analyst price targets given these accuracy rates?

^(Data Source: Scalar Data Repository | Analysis Date: Feb 2025)

Edit 1: Thanks for the gold! RIP inbox.

Edit 2: For those asking about methodology details, I'll post a follow-up with sector-specific analysis.

Would you like me to modify any aspect of this post? I can:

1. Add more statistical details
2. Include specific sector breakdowns
3. Add more discussion points
4. Modify the formatting