Outlook: SoFi Technologies Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Time series to forecast n: 04 Mar 2023 for (n+16 weeks)
Methodology : Modular Neural Network (Social Media Sentiment Analysis)

## Abstract

SoFi Technologies Inc. Common Stock prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the SOFI stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

## Key Points

1. How do you pick a stock?
2. How do you know when a stock will go up or down?
3. What is Markov decision process in reinforcement learning?

## SOFI Target Price Prediction Modeling Methodology

We consider SoFi Technologies Inc. Common Stock Decision Process with Modular Neural Network (Social Media Sentiment Analysis) where A is the set of discrete actions of SOFI 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(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+16 weeks) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of SOFI stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

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?

## SOFI Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: SOFI SoFi Technologies Inc. Common Stock
Time series to forecast n: 04 Mar 2023 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

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%

## IFRS Reconciliation Adjustments for SoFi Technologies Inc. Common Stock

1. For the purposes of the transition provisions in paragraphs 7.2.1, 7.2.3–7.2.28 and 7.3.2, the date of initial application is the date when an entity first applies those requirements of this Standard and must be the beginning of a reporting period after the issue of this Standard. Depending on the entity's chosen approach to applying IFRS 9, the transition can involve one or more than one date of initial application for different requirements.
2. If an entity prepares interim financial reports in accordance with IAS 34 Interim Financial Reporting the entity need not apply the requirements in this Standard to interim periods prior to the date of initial application if it is impracticable (as defined in IAS 8).
3. When an entity designates a financial liability as at fair value through profit or loss, it must determine whether presenting in other comprehensive income the effects of changes in the liability's credit risk would create or enlarge an accounting mismatch in profit or loss. An accounting mismatch would be created or enlarged if presenting the effects of changes in the liability's credit risk in other comprehensive income would result in a greater mismatch in profit or loss than if those amounts were presented in profit or loss
4. Lifetime expected credit losses are not recognised on a financial instrument simply because it was considered to have low credit risk in the previous reporting period and is not considered to have low credit risk at the reporting date. In such a case, an entity shall determine whether there has been a significant increase in credit risk since initial recognition and thus whether lifetime expected credit losses are required to be recognised in accordance with paragraph 5.5.3.

*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.

## Conclusions

SoFi Technologies Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. SoFi Technologies Inc. Common Stock prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the SOFI stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

### SOFI SoFi Technologies Inc. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCC
Balance SheetCaa2Ba1
Leverage RatiosBaa2B2
Cash FlowCBaa2
Rates of Return and ProfitabilityB2Ba3

*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?

### Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 510 signals.

## References

1. G. Theocharous and A. Hallak. Lifetime value marketing using reinforcement learning. RLDM 2013, page 19, 2013
2. Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
3. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
4. A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
5. Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
6. 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
7. Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
Frequently Asked QuestionsQ: What is the prediction methodology for SOFI stock?
A: SOFI stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Wilcoxon Sign-Rank Test
Q: Is SOFI stock a buy or sell?
A: The dominant strategy among neural network is to Buy SOFI Stock.
Q: Is SoFi Technologies Inc. Common Stock stock a good investment?
A: The consensus rating for SoFi Technologies Inc. Common Stock is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of SOFI stock?
A: The consensus rating for SOFI is Buy.
Q: What is the prediction period for SOFI stock?
A: The prediction period for SOFI is (n+16 weeks)