**Outlook:**bioAffinity Technologies Inc. Common Stock is assigned short-term B2 & long-term Ba3 estimated rating.

**AUC Score :**

**Short-Term Revised**

^{1}:**Dominant Strategy :**Hold

**Time series to forecast n:** for

^{2}

**Methodology :**Supervised Machine Learning (ML)

**Hypothesis Testing :**Pearson Correlation

**Surveillance :**Major exchange and OTC

^{1}The accuracy of the model is being monitored on a regular basis.(15-minute period)

^{2}Time series is updated based on short-term trends.

## Summary

bioAffinity Technologies Inc. Common Stock prediction model is evaluated with Supervised Machine Learning (ML) and Pearson Correlation^{1,2,3,4}and it is concluded that the BIAF stock is predictable in the short/long term. Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product.

^{5}

**According to price forecasts for 3 Month period, the dominant strategy among neural network is: Hold**

## Key Points

- Supervised Machine Learning (ML) for BIAF stock price prediction process.
- Pearson Correlation
- Can machine learning predict?
- Trading Signals
- Is Target price a good indicator?

## BIAF Stock Price Forecast

We consider bioAffinity Technologies Inc. Common Stock Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of BIAF stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

**Sample Set:**Neural Network

**Stock/Index:**BIAF bioAffinity Technologies Inc. Common Stock

**Time series to forecast:**3 Month

**According to price forecasts, the dominant strategy among neural network is: Hold**

^{6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Supervised Machine Learning (ML)) X S(n):→ 3 Month $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of BIAF stock

j:Nash equilibria (Neural Network)

k:Dominated move of BIAF stock holders

a:Best response for BIAF target price

Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product.

^{5}Pearson correlation, also known as Pearson's product-moment correlation, is a measure of the linear relationship between two variables. It is a statistical measure that assesses the strength and direction of a linear relationship between two variables. The sign of the correlation coefficient indicates the direction of the relationship, while the magnitude of the correlation coefficient indicates the strength of the relationship. A correlation coefficient of 0.9 indicates a strong positive correlation, while a correlation coefficient of 0.2 indicates a weak positive correlation.

^{6,7}

For further technical information as per how our model work we invite you to visit the article below:

### BIAF Stock Forecast (Buy or Sell) Strategic Interaction Table

Strategic Interaction Table Legend:

**X axis: *Likelihood%** (The higher the percentage value, the more likely the event will occur.)

**Y axis: *Potential Impact%** (The higher the percentage value, the more likely the price will deviate.)

**Z axis (Grey to Black): *Technical Analysis%**

### Financial Data Adjustments for Supervised Machine Learning (ML) based BIAF Stock Prediction Model

- One of the defining characteristics of a derivative is that it has an initial net investment that is smaller than would be required for other types of contracts that would be expected to have a similar response to changes in market factors. An option contract meets that definition because the premium is less than the investment that would be required to obtain the underlying financial instrument to which the option is linked. A currency swap that requires an initial exchange of different currencies of equal fair values meets the definition because it has a zero initial net investment.
- An entity must look through until it can identify the underlying pool of instruments that are creating (instead of passing through) the cash flows. This is the underlying pool of financial instruments.
- However, in some cases, the time value of money element may be modified (ie imperfect). That would be the case, for example, if a financial asset's interest rate is periodically reset but the frequency of that reset does not match the tenor of the interest rate (for example, the interest rate resets every month to a one-year rate) or if a financial asset's interest rate is periodically reset to an average of particular short- and long-term interest rates. In such cases, an entity must assess the modification to determine whether the contractual cash flows represent solely payments of principal and interest on the principal amount outstanding. In some circumstances, the entity may be able to make that determination by performing a qualitative assessment of the time value of money element whereas, in other circumstances, it may be necessary to perform a quantitative assessment.
- Fluctuation around a constant hedge ratio (and hence the related hedge ineffectiveness) cannot be reduced by adjusting the hedge ratio in response to each particular outcome. Hence, in such circumstances, the change in the extent of offset is a matter of measuring and recognising hedge ineffectiveness but does not require rebalancing.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

### BIAF bioAffinity Technologies Inc. Common Stock Financial Analysis*

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B2 | Ba3 |

Income Statement | C | Baa2 |

Balance Sheet | Baa2 | B2 |

Leverage Ratios | B3 | Baa2 |

Cash Flow | C | B3 |

Rates of Return and Profitability | Baa2 | B1 |

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.

How does neural network examine financial reports and understand financial state of the company?

## References

- Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
- Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
- Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
- Athey S, Blei D, Donnelly R, Ruiz F. 2017b. Counterfactual inference for consumer choice across many prod- uct categories. AEA Pap. Proc. 108:64–67
- Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
- Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
- Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer

## Frequently Asked Questions

Q: Is BIAF stock expected to rise?A: BIAF stock prediction model is evaluated with Supervised Machine Learning (ML) and Pearson Correlation and it is concluded that dominant strategy for BIAF stock is Hold

Q: Is BIAF stock a buy or sell?

A: The dominant strategy among neural network is to Hold BIAF Stock.

Q: Is bioAffinity Technologies Inc. Common Stock stock a good investment?

A: The consensus rating for bioAffinity Technologies Inc. Common Stock is Hold and is assigned short-term B2 & long-term Ba3 estimated rating.

Q: What is the consensus rating of BIAF stock?

A: The consensus rating for BIAF is Hold.

Q: What is the forecast for BIAF stock?

A: BIAF target price forecast: Hold

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