Outlook: ContraFect Corporation Common Stock is assigned short-term B2 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
Methodology : Active Learning (ML)
Hypothesis Testing : Wilcoxon Sign-Rank Test
Surveillance : Major exchange and OTC

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

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

## Abstract

ContraFect Corporation Common Stock prediction model is evaluated with Active Learning (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the CFRX stock is predictable in the short/long term. Active learning (AL) is a machine learning (ML) method in which the model actively queries the user for labels on data points. This allows the model to learn more efficiently, as it is only learning about the data points that are most informative. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Hold

## Key Points

1. What is the best way to predict stock prices?
2. Nash Equilibria
3. What is Markov decision process in reinforcement learning?

## CFRX Target Price Prediction Modeling Methodology

We consider ContraFect Corporation Common Stock Decision Process with Active Learning (ML) where A is the set of discrete actions of CFRX 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

F(Wilcoxon Sign-Rank Test)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Active Learning (ML)) X S(n):→ 4 Weeks $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of CFRX stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Active Learning (ML)

Active learning (AL) is a machine learning (ML) method in which the model actively queries the user for labels on data points. This allows the model to learn more efficiently, as it is only learning about the data points that are most informative.

### Wilcoxon Sign-Rank Test

The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.

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

How do AC Investment Research machine learning (predictive) algorithms actually work?

## CFRX Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: CFRX ContraFect Corporation Common Stock
Time series to forecast: 4 Weeks

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

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 Active Learning (ML) based CFRX Stock Prediction Model

1. The business model may be to hold assets to collect contractual cash flows even if the entity sells financial assets when there is an increase in the assets' credit risk. To determine whether there has been an increase in the assets' credit risk, the entity considers reasonable and supportable information, including forward looking information. Irrespective of their frequency and value, sales due to an increase in the assets' credit risk are not inconsistent with a business model whose objective is to hold financial assets to collect contractual cash flows because the credit quality of financial assets is relevant to the entity's ability to collect contractual cash flows. Credit risk management activities that are aimed at minimising potential credit losses due to credit deterioration are integral to such a business model. Selling a financial asset because it no longer meets the credit criteria specified in the entity's documented investment policy is an example of a sale that has occurred due to an increase in credit risk. However, in the absence of such a policy, the entity may demonstrate in other ways that the sale occurred due to an increase in credit risk.
2. An entity's business model is determined at a level that reflects how groups of financial assets are managed together to achieve a particular business objective. The entity's business model does not depend on management's intentions for an individual instrument. Accordingly, this condition is not an instrument-by-instrument approach to classification and should be determined on a higher level of aggregation. However, a single entity may have more than one business model for managing its financial instruments. Consequently, classification need not be determined at the reporting entity level. For example, an entity may hold a portfolio of investments that it manages in order to collect contractual cash flows and another portfolio of investments that it manages in order to trade to realise fair value changes. Similarly, in some circumstances, it may be appropriate to separate a portfolio of financial assets into subportfolios in order to reflect the level at which an entity manages those financial assets. For example, that may be the case if an entity originates or purchases a portfolio of mortgage loans and manages some of the loans with an objective of collecting contractual cash flows and manages the other loans with an objective of selling them.
3. The expected credit losses on a loan commitment shall be discounted using the effective interest rate, or an approximation thereof, that will be applied when recognising the financial asset resulting from the loan commitment. This is because for the purpose of applying the impairment requirements, a financial asset that is recognised following a draw down on a loan commitment shall be treated as a continuation of that commitment instead of as a new financial instrument. The expected credit losses on the financial asset shall therefore be measured considering the initial credit risk of the loan commitment from the date that the entity became a party to the irrevocable commitment.
4. In accordance with the hedge effectiveness requirements, the hedge ratio of the hedging relationship must be the same as that resulting from the quantity of the hedged item that the entity actually hedges and the quantity of the hedging instrument that the entity actually uses to hedge that quantity of hedged item. Hence, if an entity hedges less than 100 per cent of the exposure on an item, such as 85 per cent, it shall designate the hedging relationship using a hedge ratio that is the same as that resulting from 85 per cent of the exposure and the quantity of the hedging instrument that the entity actually uses to hedge those 85 per cent. Similarly, if, for example, an entity hedges an exposure using a nominal amount of 40 units of a financial instrument, it shall designate the hedging relationship using a hedge ratio that is the same as that resulting from that quantity of 40 units (ie the entity must not use a hedge ratio based on a higher quantity of units that it might hold in total or a lower quantity of units) and the quantity of the hedged item that it actually hedges with those 40 units.

*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.

### CFRX ContraFect Corporation Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B2B1
Income StatementCaa2Ba3
Balance SheetBa1Baa2
Leverage RatiosBa1Ba3
Cash FlowBaa2C
Rates of Return and ProfitabilityCB2

*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?

## Conclusions

ContraFect Corporation Common Stock is assigned short-term B2 & long-term B1 estimated rating. ContraFect Corporation Common Stock prediction model is evaluated with Active Learning (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the CFRX stock is predictable in the short/long term. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Hold

### Prediction Confidence Score

Trust metric by Neural Network: 76 out of 100 with 456 signals.

## References

1. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
2. Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
3. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
4. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
5. J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
6. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
7. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
Frequently Asked QuestionsQ: What is the prediction methodology for CFRX stock?
A: CFRX stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is CFRX stock a buy or sell?
A: The dominant strategy among neural network is to Hold CFRX Stock.
Q: Is ContraFect Corporation Common Stock stock a good investment?
A: The consensus rating for ContraFect Corporation Common Stock is Hold and is assigned short-term B2 & long-term B1 estimated rating.
Q: What is the consensus rating of CFRX stock?
A: The consensus rating for CFRX is Hold.
Q: What is the prediction period for CFRX stock?
A: The prediction period for CFRX is 4 Weeks