Analyzing historical drops: is it actually reliable?

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Summary

The author, a software engineer, is developing a quantitative model to analyze significant single-day stock drops (e.g., >6%) to identify recurring patterns and potential buying opportunities.
The core thesis is that "not all drops are the same," and by analyzing factors like volume, price position within the yearly cycle, and drop intensity, one can identify "high-quality" drops that are more likely to rebound.
The post seeks community feedback on the validity of this historical, price-action-based approach, using a recent drop in FinecoBank (FBK.MI) as a case study.
Quality assessment: This is well-researched speculation. The author demonstrates a structured, data-driven approach that goes beyond typical WSB "DD." However, it's a back-tested model without forward-looking validation, and it relies purely on technical data, ignoring fundamental catalysts, which is a significant limitation pointed out by the community.

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Hi everyone,

I’m a software engineer with a passion for finance and statistics, and I’m currently working on a small personal project. I’d like to get some feedback from the community to understand how valuable this approach could be in real trading/investing.

In short: I’m analyzing all the significant single‑day drops of a stock (e.g., -6%, -7%, -10%) and measuring how the price behaved in the following days and months.

The goal is to understand whether there are recurring patterns that could help evaluate an entry after a sharp decline.

# What I’m analyzing exactly

For each historical drop, I calculate:

* Subsequent rebounds (1, 2, 5, 10, 20, 60, 90, 120, 180, 300 days)
* Probability of a rebound for each time horizon
* Recovery time (how many days it takes to return to the pre‑drop price)
* **VolumeShock** → how abnormal the day’s volume is compared to the average
* **CyclePos** → where the price sits within the yearly cycle (near the lows or near the highs?)
* **DropQualityScore** → a 0–100 score summarizing drop quality (intensity + volume + context)

The basic idea behind this is simple:  
**not all drops are the same.**

* A -8% drop with low volume is just noise.
* A -8% drop with 5× average volume is panic.
* A -8% drop near yearly highs is often an overreaction.
* A -8% drop near yearly lows is riskier but can produce strong rebounds.

I’m trying to understand whether these historical patterns can help:

* identify “high‑quality” drops
* estimate rebound probabilities

I started from a real case: the recent FinecoBank drop (-9%).

I analyzed the stock’s historical data over the last 10 years.

https://preview.redd.it/g8ndngjpfwjg1.png?width=1218&amp;format=png&amp;auto=webp&amp;s=6c05e2b6a8a1d169aac18ee02196e8e8ec69799c

Then I extracted only the days where the stock fell more than 6% during the period.

From there, case by case, I analyzed what would have happened if we had opened a position at the end of the drop day (during the maximum intraday decline):

https://preview.redd.it/gcwuqj4sfwjg1.png?width=2732&amp;format=png&amp;auto=webp&amp;s=598f1a2e44e9bb6cfa77ba8d20861a86fea46009

# What do you think?

Does it make sense to use this kind of historical analysis to evaluate entries on sharp drops?

Has anyone here tried something similar?

Are rebound patterns after large drops stable enough to be exploited?

Any metrics you would add?

I’m really curious to hear opinions, criticism, ideas, or suggestions.

Trade Ideas

u/Season_Famous Reddit r/wallstreetbets

FinecoBank (FBK.MI) recently experienced a significant single-day drop of -9%. The author's model analyzes historical drops of similar magnitude (>6%) for this specific stock over the past 10 years. The analysis of past drops suggests that such events are often followed by rebounds over various time horizons. By identifying the characteristics of this specific drop (e.g., high volume, context), the model aims to quantify the probability of a profitable entry, framing the drop as a potential overreaction and a buying opportunity. The author's entire project is predicated on the idea that analyzing historical drops can identify profitable entry points. By using the recent -9% FinecoBank drop as the primary example, the author strongly implies that this event fits the profile of a "high-quality" drop worth considering for a long position, based on historical patterns of recovery. The top comments highlight major risks: past performance is not indicative of future results ("No." - u/MaleCowShitDetector). The model ignores the fundamental reason for the drop, which could be a permanent negative change in the company's outlook. A single outlier event or a shift in market paradigm could invalidate the entire back-tested strategy (u/Character-Canary978).

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