The hottest Financial Modeling Substack posts right now

The post provides a list of favorite stocks for February 2024.
Automatic Data Processing and Text SA are among the recommended stocks.
The stocks are highlighted for their industry position, financial performance, undervaluation, and potential returns.

When analyzing data, consider thinking in terms of the number of unique data points (N) rather than the total number of observations (T).
Samples drawn from the same regime reduce the effective number of data points, impacting the reliability of quantitative analysis.
Account for autocorrelation in data to avoid biases in estimating return volatilities and risk, ensuring better comparisons across different investments.

The paper introduces a methodology using Shapley values to understand the contribution of different factors in portfolio performance.
It presents the versatile SPPC method for evaluating predictor group contributions to portfolio success.
The SPPC method quantifies predictor impacts and offers insights into changing dynamics over time in financial machine learning.

Buying the dip strategy involves waiting for an asset price to drop below a specific threshold before purchasing it, but simulation data shows that this strategy is usually less effective than buying regularly.
When dealing with volatile assets, buying the dip can be beneficial if the asset underperforms in the median case but significantly overperforms occasionally, providing exposure without heavy losses.
For stable assets or normal investors, buying regularly is usually the best strategy as it requires less effort and is generally more effective than trying to time the market by waiting for price dips.

Financial models are more than just tools for fundraising, they are essential decision-making guides for startups.
A financial model translates your business into numbers, helping predict future outcomes and guide spending.
Startups need a clear strategy before building a financial model to ensure alignment and adaptability.

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Correlations vary widely over different time intervals, affecting asset allocation decisions.
Shorter time intervals show smaller correlations, while longer intervals have greater variability.
Conservative estimates of correlations using consistent shorter intervals may be more reliable for projecting into the future.

Companies reporting pre-revenue numbers are often valued higher in the market.
Market often extrapolates current growth to predict long-term growth, leading to significant movements in growth stocks.
Assumptions and dependencies in companies' growth models can cause sharp price swings, even without financial leverage.