The presented paper modeled and predicted stock returns using LSTM. The historical data of stock market were transformed into 30-days-long sequences with 10 learning features and 7-day earning rate labeling. The model was fitted by training on 1200000 sequences and tested using the other 350000 sequences. We evaluate Best Buy prediction models with Modular Neural Network (DNN Layer) and Logistic Regression1,2,3,4 and conclude that the BBY stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold BBY stock.

Keywords: BBY, Best Buy, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. How do you pick a stock?
3. Fundemental Analysis with Algorithmic Trading ## BBY Target Price Prediction Modeling Methodology

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 consider Best Buy Stock Decision Process with Logistic Regression where A is the set of discrete actions of BBY 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(Modular Neural Network (DNN Layer)) X S(n):→ (n+6 month) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of BBY 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?

## BBY Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Time series to forecast n: 24 Oct 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold BBY 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

Best Buy assigned short-term Ba1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Logistic Regression1,2,3,4 and conclude that the BBY stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold BBY stock.

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

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba3
Operational Risk 8690
Market Risk6371
Technical Analysis5188
Fundamental Analysis6330
Risk Unsystematic8837

### Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 503 signals.

## References

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2. Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
3. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
4. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
5. T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
6. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
7. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
Frequently Asked QuestionsQ: What is the prediction methodology for BBY stock?
A: BBY stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Logistic Regression
Q: Is BBY stock a buy or sell?
A: The dominant strategy among neural network is to Hold BBY Stock.
Q: Is Best Buy stock a good investment?
A: The consensus rating for Best Buy is Hold and assigned short-term Ba1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of BBY stock?
A: The consensus rating for BBY is Hold.
Q: What is the prediction period for BBY stock?
A: The prediction period for BBY is (n+6 month)