This paper examines the theory and practice of regression techniques for prediction of stock price trend by using a transformed data set in ordinal data format. The original pretransformed data source contains data of heterogeneous data types used for handling of currency values and financial ratios. The data formats in currency values and financial ratios provide a process for computation of stock prices. The transformed data set contains only a standardized ordinal data type which provides a process to measure rankings of stock price trends. We evaluate Realty Income prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Beta1,2,3,4 and conclude that the O stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold O stock.

Keywords: O, Realty Income, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. How can neural networks improve predictions?
2. Can stock prices be predicted?
3. Trust metric by Neural Network

## O Target Price Prediction Modeling Methodology

Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. We consider Realty Income Stock Decision Process with Beta where A is the set of discrete actions of O 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(Beta)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) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of O stock

j:Nash equilibria

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?

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

Sample Set: Neural Network
Stock/Index: O Realty Income
Time series to forecast n: 19 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold O stock.

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 (Yellow to Green): *Technical Analysis%

## Conclusions

Realty Income assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Beta1,2,3,4 and conclude that the O stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold O stock.

### Financial State Forecast for O Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B2
Operational Risk 7544
Market Risk8962
Technical Analysis6465
Fundamental Analysis4941
Risk Unsystematic3440

### Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 661 signals.

## References

1. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
2. Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
3. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
4. J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
5. Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
6. D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
7. D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
Frequently Asked QuestionsQ: What is the prediction methodology for O stock?
A: O stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Beta
Q: Is O stock a buy or sell?
A: The dominant strategy among neural network is to Hold O Stock.
Q: Is Realty Income stock a good investment?
A: The consensus rating for Realty Income is Hold and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of O stock?
A: The consensus rating for O is Hold.
Q: What is the prediction period for O stock?
A: The prediction period for O is (n+16 weeks)