The stock market has been an attractive field for a large number of organizers and investors to derive useful predictions. Fundamental knowledge of stock market can be utilised with technical indicators to investigate different perspectives of the financial market; also, the influence of various events, financial news, and/or opinions on investors' decisions and hence, market trends have been observed. Such information can be exploited to make reliable predictions and achieve higher profitability. Computational intelligence has emerged with various deep neural network (DNN) techniques to address complex stock market problems. We evaluate Fortune Brands Home & Security prediction models with Modular Neural Network (Market News Sentiment Analysis) and Pearson Correlation1,2,3,4 and conclude that the FBHS 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 FBHS stock.
Keywords: FBHS, Fortune Brands Home & Security, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Why do we need predictive models?
- What is neural prediction?
- Understanding Buy, Sell, and Hold Ratings
FBHS Target Price Prediction Modeling Methodology
The success of portfolio construction depends primarily on the future performance of stock markets. Recent developments in machine learning have brought significant opportunities to incorporate prediction theory into portfolio selection. However, many studies show that a single prediction model is insufficient to achieve very accurate predictions and affluent returns. In this paper, a novel portfolio construction approach is developed using a hybrid model based on machine learning for stock prediction. We consider Fortune Brands Home & Security Stock Decision Process with Pearson Correlation where A is the set of discrete actions of FBHS 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(Pearson Correlation)5,6,7= X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+16 weeks)
n:Time series to forecast
p:Price signals of FBHS 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?
FBHS Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: FBHS Fortune Brands Home & Security
Time series to forecast n: 10 Oct 2022 for (n+16 weeks)
According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold FBHS 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
Fortune Brands Home & Security assigned short-term B2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Pearson Correlation1,2,3,4 and conclude that the FBHS 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 FBHS stock.
Financial State Forecast for FBHS Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | Ba2 |
| Operational Risk | 46 | 85 |
| Market Risk | 53 | 40 |
| Technical Analysis | 60 | 74 |
| Fundamental Analysis | 63 | 86 |
| Risk Unsystematic | 38 | 51 |
Prediction Confidence Score
References
- Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
- Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
- V. Konda and J. Tsitsiklis. Actor-Critic algorithms. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1008–1014, 2000
- 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]
- D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
- Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
- Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
Frequently Asked Questions
Q: What is the prediction methodology for FBHS stock?A: FBHS stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Pearson Correlation
Q: Is FBHS stock a buy or sell?
A: The dominant strategy among neural network is to Hold FBHS Stock.
Q: Is Fortune Brands Home & Security stock a good investment?
A: The consensus rating for Fortune Brands Home & Security is Hold and assigned short-term B2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of FBHS stock?
A: The consensus rating for FBHS is Hold.
Q: What is the prediction period for FBHS stock?
A: The prediction period for FBHS is (n+16 weeks)
- Live broadcast of expert trader insights
- Real-time stock market analysis
- Access to a library of research dataset (API,XLS,JSON)
- Real-time updates
- In-depth research reports (PDF)