Stock market predictions are one of the challenging tasks for financial investors across the globe. This challenge is due to the uncertainty and volatility of the stock prices in the market. Due to technology and globalization of business and financial markets it is important to predict the stock prices more quickly and accurately. Last few years there has been much improvement in the field of Neural Network (NN) applications in business and financial markets. Artificial Neural Network (ANN) methods are mostly implemented and play a vital role in decision making for stock market predictions. We evaluate Oak Street Health prediction models with Multi-Task Learning (ML) and Logistic Regression1,2,3,4 and conclude that the OSH stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold OSH stock.

Keywords: OSH, Oak Street Health, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Dominated Move
2. What is the use of Markov decision process?
3. Reaction Function ## OSH Target Price Prediction Modeling Methodology

The research reported in the paper focuses on the stock market prediction problem, the main aim being the development of a methodology to forecast the stock closing price. The methodology is based on some novel variable selection methods and an analysis of neural network and support vector machines based prediction models. Also, a hybrid approach which combines the use of the variables derived from technical and fundamental analysis of stock market indicators in order to improve prediction results of the proposed approaches is reported in this paper. We consider Oak Street Health Stock Decision Process with Logistic Regression where A is the set of discrete actions of OSH 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(Logistic Regression)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(Multi-Task Learning (ML)) X S(n):→ (n+4 weeks) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of OSH 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?

## OSH Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: OSH Oak Street Health
Time series to forecast n: 10 Sep 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold OSH 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

Oak Street Health assigned short-term Ba3 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Multi-Task Learning (ML) with Logistic Regression1,2,3,4 and conclude that the OSH stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold OSH stock.

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

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba1
Operational Risk 4367
Market Risk8356
Technical Analysis8486
Fundamental Analysis4763
Risk Unsystematic6583

### Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 853 signals.

## References

1. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
2. Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
3. B. Derfer, N. Goodyear, K. Hung, C. Matthews, G. Paoni, K. Rollins, R. Rose, M. Seaman, and J. Wiles. Online marketing platform, August 17 2007. US Patent App. 11/893,765
4. Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
5. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
6. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
7. L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
Frequently Asked QuestionsQ: What is the prediction methodology for OSH stock?
A: OSH stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Logistic Regression
Q: Is OSH stock a buy or sell?
A: The dominant strategy among neural network is to Hold OSH Stock.
Q: Is Oak Street Health stock a good investment?
A: The consensus rating for Oak Street Health is Hold and assigned short-term Ba3 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of OSH stock?
A: The consensus rating for OSH is Hold.
Q: What is the prediction period for OSH stock?
A: The prediction period for OSH is (n+4 weeks)