# EDGAR Filing Document

**Accession Number:** 0001592900
**File Stem:** 0001829126-23-001489
**Filing Date:** 2023-2
**Character Count:** 23830
**Document Hash:** 6789d24fb6b07a99850e4057d31f70a6
**Contains OCR:** False
**Source Format:** 

## Filing Content

## Filing Summary
**0001829126-23-001489.hdr.sgml**: 20241129

**ACCESSION NUMBER**: 0001829126-23-001489

**CONFORMED SUBMISSION TYPE**: CORRESP

**PUBLIC DOCUMENT COUNT**: 4

**FILED AS OF DATE**: 20230214

**FILER**: 

**COMPANY DATA:**
- **COMPANY CONFORMED NAME:** EA Series Trust
- **CENTRAL INDEX KEY:** 0001592900

**ORGANIZATION NAME:**
- **IRS NUMBER:** 000000000
- **STATE OF INCORPORATION:** DE
- **FISCAL YEAR END:** 0930

**FILING VALUES:**
- **FORM TYPE:** CORRESP

**BUSINESS ADDRESS:**
- **STREET 1:** 19 E EAGLE ROAD
- **CITY:** HAVERTOWN
- **STATE:** PA
- **ZIP:** 19083
- **BUSINESS PHONE:** 1.215.882.9983

**MAIL ADDRESS:**
- **STREET 1:** 19 E EAGLE ROAD
- **CITY:** HAVERTOWN
- **STATE:** PA
- **ZIP:** 19083

**FORMER COMPANY:**
- **FORMER CONFORMED NAME:** Alpha Architect ETF Trust
- **DATE OF NAME CHANGE:** 20140428

**FORMER COMPANY:**
- **FORMER CONFORMED NAME:** Empowered Funds ETF Trust
- **DATE OF NAME CHANGE:** 20131125

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February 14, 2023

Ms. Alison White

U.S. Securities and Exchange Commission

100 F Street, NE

Washington, DC 20549

---

| | |
|:---|:---|
| **Re:** | **EA Series Trust (the "<u>Trust</u>")** |

---

**Post-Effective Amendment No. 202 to the Registration Statement on Form N-1A (the**

**"<u>Amendment</u>") File Nos.: 333-195493 and 811-22961**

**Euclidean Fundamental Value ETF**

Dear Ms. White:

This correspondence responds to comments to the Trust received by the undersigned from the staff of the U.S. Securities and Exchange Commission (the "<u>Staff</u>" of the "<u>Commission</u>") with respect to the Amendment relating to the Euclidean Fundamental Value ETF (the "<u>Fund</u>"), a proposed new series of the Trust. For your convenience, the comments have been reproduced with responses following each comment. Capitalized terms not otherwise defined have the same meaning as in the Amendment.

&nbsp;&nbsp;&nbsp;&nbsp;1. <u>Comment</u>: In the *Principal Investment Strategies* section, please clarify how Step 4 of the
investment process identifies potential value traps. In addition, explain what, if any, data and/or analysis is used in Step 4 that is
not listed in Step 2 of the investment process.

<u>Response</u>: The Fund confirms that the historical fundamental and market input data used by the model is the same input data for Step 2 and Step 4 of the investment process. However, the Step 2 model was designed and is used to *forecast future earnings* (and uncertainty in those earnings) whereas the model in Step 4 was designed and is used to forecast the *probability of a company being a value-trap.* See the italicized text below for our changes in response to the above comment.

**Step 4**: Next, the model is used to identify and screen out potential "value-traps". A *potential* value trap is defined as a company that is considered inexpensive based on current *valuation multiples* but is likely to have bottom decile price performance over the subsequent year. *As in **Step 2**, the model takes as input historical company data, however, in **Step 4** the model's output is the probability of a company not being a value-trap. This probability is then multiplied by the earnings yield alue from **Step 3** and the Investment Universe is re-sorted such that, companies with a high probability of being a value-trap rank lower than companies with a low probability of being a value-trap, all else being equal. This final ranking is then used as input for **Step 5**. The model is implemented as a neural network and was developed using standard supervised machine learning techniques. The historical company data used as input to the model in **Step 4** includes financials such as income and cashflow statements, balance sheet data, short interest, historical stock price change data, industry classification and market size category. The scope and types of input data used in **Step 4** may change over time when it is found that additional types of data improve the accuracy of the model's ability to predict whether a company is or is not a value-trap.*

![](image_002.jpg)

11300 Tomahawk Creek Pkwy, Suite 310 ● Leawood, KS 66211

Practus, LLP ● Practus.com

In addition, the Fund's sub-adviser, Euclidean Technologies Management, LLC ("Euclidean" or the "Sub-Adviser"), offers the following additional background information about Euclidean, and the firm's development of its investment models.

We (Euclidean) have written an in-depth white paper that is publicly available on how the model in Step 4 identifies potential value traps. The paper can be accessed here:

<u>https://www.euclidean.com/value-traps-and-deep-learning</u>

The Sub-Adviser has developed the model to identify value-traps (defined as a company that is considered inexpensive based on current fundamentals but is likely to have bottom decile price performance over the subsequent year) using standard supervised machine learning techniques. In our approach, the model is implemented as an <u>artificial neural network</u>. The neural network represents an input-output mapping (i.e., it maps input data to output data). Before learning begins, historical input and target output data is collected and the mapping of input to output is unknown. The goal of supervised learning is to learn (via an algorithmic training process) the unknown mapping such that when the neural network is presented with an particular set of inputs it produces the correct outputs (where correct means the neural network output does not differ than the target output). In the process of training the neural network, the neural network parameters are iteratively adjusted via the learning algorithm so that the difference between the model outputs and the target training data outputs is minimized.

![](image_002.jpg)

**2**

In the value-trap model, the input data is historical fundamental data about companies (e.g., data that can be found in a company's income statement, balance sheet, and cashflow statement), company historical stock price change data, company industry classification, company market capitalization size category, and company total shares sold short divided by shares outstanding (short interest). The target output data is +1.0 (plus one) or -1.0 (negative one), depending on whether the particular company, at a particular point-in-time, is a value-trap (+1.0) or not a value-trap (-1.0). This setup is more easily visualized by the following table:

![](image_003.jpg)

Each row represents a company at a different point in time (where time is incremented in months) and the company's input data and target output data at that particular time. The data in the columns below "Fundamental Data at Time *t*" is the input data and the data in the column below "Outcomes at Time *t+12*" is the target output data (value-trap or not value-trap). The goal of the learning algorithm is teach the neural network to produce the data in the output column when presented with the data in the input columns.

The key difference between the value-trap model in Step 4 and the earnings forecast model in Step 2 is that the two models are attempting to forecast different things. In the case of the value-trap model, the model is taught to forecast whether a company, at a particular point in time, is likely to be a bottom decile stock market performer over the subsequent year (i.e., a value-trap). However, the target output of the model in Step 2 is the company's future earnings in twelve months time. So, in the case of the value-trap model, it is attempting to forecast something about a company's future stock price (how well it performs relative to other stocks). Whereas, in the case of the earnings forecast model, it is attempting to forecasts something about a company's future fundamentals, and in particular its earnings.

&nbsp;&nbsp;&nbsp;&nbsp;2. <u>Comment</u>: In the *Principal Investment Strategies* section, the Staff notes that the Fund mentions
in Step 5 of the investment process that the Sub-Adviser performs a manual evaluation of the data used in the investment model's
assessment process. It further notes that if the Sub-Adviser believes the data to be inaccurate or new data has become available that
may impact the investments risks for the Fund the Sub-Adviser may override the model. With a view to enhance disclosure under Item 9 of
Form N-1A, further explain how the model was developed and validated. Please address the data and relationships your machine learning
model relies on. Explain how the Sub-Adviser ensures such relationship(s) hold over time.

![](image_002.jpg)

**3**

<u>Response</u>: The Fund has added the following information to the Item 9 discussion to address the noted items.

**Additional Information About the Sub-Adviser's Models.** The models are implemented as an artificial neural network, and each model is trained on data through a supervised machine learning process. A supervised machine learning process is an automated process, implemented as a computer program, where an input-output mapping is learned from data. The two models are different as they serve different functions and hence implement different input-output mappings.

The first model is designed as an earnings forecasting and uncertainty quantification model (See Step 2 of the investment process). Using standard deep machine learning techniques, the model was trained to map historical data about every company in the Investment Universe, at each point-in-time, to the company's one-year ahead future earnings. It was simultaneously trained to quantify the uncertainty (using the maximum likelihood technique) in the forecast future earnings. This training process was executed in an iterative manner starting on data in the year 1970 and going through 1999. The model was validated on out-of-sample data beginning in the year 2000 and going through 2019.

The second model predicts whether an investment recommended by the first model is likely to be a value-trap (See Step 4 of the investment process). Again, using standard deep machine learning techniques, the model was trained to map historical data about every company in the Investment Universe, at each point-in-time, to the probability that the company is a value-trap (defined as having a bottom decile stock price return over the subsequent year). This training process was executed in an iterative manner starting on data in the year 1970 and going through 1999. The model was validated on out-of-sample data beginning in the year 2000 and going through 2019.

The relationships the machine learning models rely on are between the input and output data as learned by the models. Both models use the same input data: historical fundamental data about companies (e.g., data that can be found in a company's income statement, balance sheet, and cashflow statement), company historical stock price change data, company industry classification, company market capitalization size category, and company total shares sold short divided by shares outstanding (short interest). The output data for the earnings' forecast model is a company's future earnings and the output data for the value-trap model is whether a company is or is-not a value-trap (defined as having a bottom decile stock price return over the subsequent year). Therefore, each model has learned a separate input-output relationship.

These input-output relationships can change over time (called a non-stationary data distribution) and the Sub-Adviser's processes take this into account. This is accomplished by periodically updating the models so that each model is trained through the present, incorporating shifts in the input-output relationships that may have occurred since the model was last trained. However, the Sub-Adviser has observed in practice by researching how these relationships have evolved over the last 60 years, that changes happen at a very slow pace and likely only necessitating model updates every three to five years. The overall performance of each model was validated with an investment simulator over the out-of-sample period 2000 through 2019.

![](image_002.jpg)

**4**

The investment process, as described in Step 2 through Step 4 in the Principal Investment Strategies section, automatically generate a list of investments for the Fund. The manual oversight by the Sub-Adviser, as described in Step 5 of the Principal Investment Strategies section, should be viewed as a final check of the models, and does not allow for the Sub-Adviser to choose an investment for the Fund that is not considered a good investment by the models. There are two broad instances when Step 5 of the investment process plays a role: (1) the model(s) is/are receiving erroneous data to generate a specific recommendation (i.e., "garbage in, garbage out") and (2) there exists highly relevant negative information about the company that the model(s) does not have access to. To further explain the first instance, each model relies on data provided by third party data vendors (primary Standard & Poor's Compustat and CapitalIQ data services). The data includes information compiled from company SEC filings, exchanges, and other sources. In the case of SEC filings, the data is transcribed from the filing into a highly structured and normalized data format by the data provider, often very shortly after the filing has occurred. There can be errors, caused either by human or computer programs, in this transcription process. The company can also, shortly after a filing, say that the filing needs to be restated. The same can be true of market data that is provided by an exchange. In these cases, when the Sub-Adviser knows a model is making a recommendation on erroneous input data, the Sub-Adviser cannot be confident that the output of the model is of any value and therefore puts a hold on considering the particular company in question as an investment until the data error has been resolved. The second instance where Sub-Adviser oversight plays a role as part of Step 5 of the investment process is when there is highly relevant negative information about the company that the models do not have access to. Examples include, but are not limited to, the CEO being indicted of a financial crime, the company has announced undisclosed financial misrepresentations, the company files for bankruptcy, or the company is losing a customer that makes up a substantial portion of its revenue (e.g., 30%). This manual process is not designed or used by the Sub-Adviser to routinely override the models, but simply provides an additional layer of protection to the integrity of the models.

&nbsp;&nbsp;&nbsp;&nbsp;3. <u>Comment</u>: In the *Principal Investment Strategies* section, please clarify what you mean by
"machine learning" in Step 2. The investment process appears to be based on fundamental stock analysis so how does machine
learning fit into the fundamental investment process. Explain how the Sub-Adviser's investment model is different from others used
in the industry.

<u>Response</u>: The Fund has modified the discussion in Step 2 to further explain what it means by machine learning. See the revised Step 2 discussion below. In addition, as part of the Fund's response to this comment the Fund refers you to Response 2 (above) and the additional information included in the Item 9 discussion regarding the Sub-Adviser's models.

**Step 2**: In this Step, the Sub-Adviser applies its machine learning based model to generate a discounted forecast of next year's earnings for each company within the Investment Universe. This discounted earnings forecast is then used as input for **Step 3**. The model is implemented as a neural network and was developed using standard supervised machine learning techniques. The model was trained to implement an input-output mapping which maps historical company data to future (forecast) company earnings. The model was also trained to estimate the uncertainty in its forecasts and this uncertainty is used to discount the earnings forecast such that the final product of the model in **Step 2** is a *discounted earnings forecast*. The historical company data used as input to the model in **Step 2** includes financials such as income and cashflow statements, balance sheet data, short interest, historical stock price change data, industry classification and market size category. The scope and types of input data used in **Step 2** may change over time when it is found that additional types of data improve the accuracy of the discounted earnings forecast generated in **Step 2**.

![](image_002.jpg)

**5**

As it relates to the comment regarding how machine learning fits into what appears to be a fundamental stock analysis strategy, the Fund first refers you to Response 2 above where additional information about the Sub-Adviser's Model is presented. The Fund and the Sub-Adviser are not clear on what part of Step 2 of the investment process implies that the "investment process appears to be based on fundamental stock analysis." Step 2 uses the machine learning based model described herein to generate a discounted earnings forecast for each company in the investment universe. That discounted earnings forecast is then used to calculate an earnings yield factor by dividing the discounted earnings forecast by the company's enterprise value. All companies in the universe are then ranked (sorted) by this earnings yield factor such that less expensive companies sort higher on the list than more expensive companies. The process described in Step 2 is entirely systematic without any human fundamental stock analysis. It is simply applying a programmatic model (implemented by computer programs) to evaluate stocks as potential investments. The Fund does acknowledge that the Model uses historical fundamental stock and market data as its input data for each of the companies in its investment universe.

Lastly, as it relates to the comment that asks: "Explain how the Sub-Adviser's investment model is different from others used in the industry." The Fund and the Sub-Adviser are not aware of any other investment manager that uses machine learning to manage a fund in the way described herein and in the updated prospectus. As previously noted above, the Sub-Adviser has provided additional information regarding its Model in the prospectus as part of its Item 9 discussion. The Sub-Adviser has also provided information on two peer reviewed papers on the machine learning approach used to develop the underlying model for Step 2 of the investment process (see below). Again, the Sub-Adviser believes the investment process that will be used for the Fund is unique to others in the industry but cannot speak to how other investment advisers use machine learning in their investment processes.

1) <u>Uncertainty-Aware Lookahead Factor Models for Quantitative Investing.</u> Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, PMLR 119, 2020. Authors: Lakshay Chauhan & John Alberg, Euclidean Technologies. Zachary C. Lipton, Carnegie Mellon University, Amazon AI, Microsoft Research.

2) <u>Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals.</u> Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. Authors: John Alberg, Euclidean Technologies. Zachary C. Lipton, Carnegie Mellon University, Amazon AI, Microsoft Research.

The conferences ICML and NeurIPS (formerly NIPS) are generally considered the most prestigious and most highly selective machine learning conferences in the world.

&nbsp;&nbsp;&nbsp;&nbsp;4. <u>Comment</u>: In the *Quantitative Security Selection Risk* discussion, there is disclosure that
the investment model "may contain design flaws or faulty assumptions" – does this refer to coding and computation errors
or does this include other items? If it includes items beyond coding and computation errors, expand the disclosure to indicate what the
other items are.

<u>Response</u>: The Fund confirms that the phrase "may contain design flaws or faulty assumptions" is referring to coding and computation errors. No additional disclosure has been added to the risk discussion.

![](image_002.jpg)

**6**

&nbsp;&nbsp;&nbsp;&nbsp;5. <u>Comment</u>: In the *Sector Risk* discussion, there is a reference to the Fund being overweight
the information technology sector. If that is accurate, please expand the Fund's principal investment strategies discussion to include
mention of investments in the information technology sector.

<u>Response</u>: The Fund has removed the reference to the information technology sector from the sector risk discussion. The Fund reiterates it is possible at some point in time the Fund may be overweight a particular sector and, if that proves to be the case, sector specific disclosure will be added to the prospectus as applicable.

&nbsp;&nbsp;&nbsp;&nbsp;6. <u>Comment</u>: In the Item 9 discussion where the Fund provides additional information about the Fund's
principal risks there is disclosure about the risks associated with investments in cash and cash equivalents. The Staff notes that the
Item 4 discussion does not include risk information on investments in cash and cash equivalents. Please remove or reconcile the disclosure.

<u>Response</u>: The Fund has removed *Cash and Cash Equivalents Risk* from the Item 9 discussion.

&nbsp;&nbsp;&nbsp;&nbsp;7. <u>Comment</u>: In the Item 9 *Geopolitical/Natural Disaster Risks* discussion, consider removing
the following sentence:

"Some interest rates are very low and in some cases yields are negative."

<u>Response</u>: The Fund has removed the sentence from *Geopolitical/Natural Disaster Risks* discussion.

&nbsp;&nbsp;&nbsp;&nbsp;8. <u>Comment</u>: On page 5 of the Statement of Additional Information, there appears to be some disclosure
missing at the end of the last sentence from the following paragraph:

"For purposes of applying the limitation set forth in the concentration policy, the Fund, with respect to its equity holdings, may use the Standard Industrial Classification (SIC) Codes, North American Industry Classification System (NAICS) Codes, MSCI Global Industry Classification System, FTSE/Dow Jones Industry Classification Benchmark (ICB) system or any other reasonable industry classification system (including systems developed by the Adviser) to identify each industry. Securities of the U.S. government (including its agencies and instrumentalities) and some tax-free securities of state or municipal governments and their political subdivisions (and repurchase agreements collateralized by government securities)."

<u>Response</u>: The Fund has revised the disclosure as follows:

"For purposes of applying the limitation set forth in the concentration policy, the Fund, with respect to its equity holdings, may use the Standard Industrial Classification (SIC) Codes, North American Industry Classification System (NAICS) Codes, MSCI Global Industry Classification System, FTSE/Dow Jones Industry Classification Benchmark (ICB) system or any other reasonable industry classification system (including systems developed by the Adviser) to identify each industry. Securities of the U.S. government (including its agencies and instrumentalities) and some tax-free securities of state or municipal governments and their political subdivisions (and repurchase agreements collateralized by government securities) ***are not considered to be issued by members of any industry.***

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**7**

If you have any questions regarding the above responses, please do not hesitate to contact me at (513) 304-5605 or Wade.Bridge@practus.com.

Sincerely,

/s/ Wade Bridge

Wade Bridge

Counsel

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**8**