Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices.** We evaluate Lowe's prediction models with Statistical Inference (ML) and Linear Regression ^{1,2,3,4} and conclude that the LOW stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to HoldBuy LOW stock.**

**LOW, Lowe's, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Short/Long Term Stocks
- How useful are statistical predictions?
- What is the best way to predict stock prices?

## LOW Target Price Prediction Modeling Methodology

Stock market forecasting is considered to be a challenging topic among time series forecasting. This study proposes a novel two-stage ensemble machine learning model named SVR-ENANFIS for stock price prediction by combining features of support vector regression (SVR) and ensemble adaptive neuro fuzzy inference system (ENANFIS). We consider Lowe's Stock Decision Process with Linear Regression where A is the set of discrete actions of LOW 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(Linear Regression)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Statistical Inference (ML)) X S(n):→ (n+1 year) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of LOW 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?

## LOW Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**LOW Lowe's

**Time series to forecast n: 11 Oct 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to HoldBuy LOW 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

Lowe's assigned short-term B1 & long-term B1 forecasted stock rating.** We evaluate the prediction models Statistical Inference (ML) with Linear Regression ^{1,2,3,4} and conclude that the LOW stock is predictable in the short/long term.**

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to HoldBuy LOW stock.**

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

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B1 | B1 |

Operational Risk | 37 | 48 |

Market Risk | 38 | 59 |

Technical Analysis | 78 | 46 |

Fundamental Analysis | 63 | 56 |

Risk Unsystematic | 75 | 70 |

### Prediction Confidence Score

## References

- Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
- Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
- Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
- Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
- Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
- Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
- P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998

## Frequently Asked Questions

Q: What is the prediction methodology for LOW stock?A: LOW stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Linear Regression

Q: Is LOW stock a buy or sell?

A: The dominant strategy among neural network is to HoldBuy LOW Stock.

Q: Is Lowe's stock a good investment?

A: The consensus rating for Lowe's is HoldBuy and assigned short-term B1 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of LOW stock?

A: The consensus rating for LOW is HoldBuy.

Q: What is the prediction period for LOW stock?

A: The prediction period for LOW is (n+1 year)

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