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 evaluate ASHTEAD TECHNOLOGY HOLDINGS PLC prediction models with Modular Neural Network (Market Volatility Analysis) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the LON:AT. stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy LON:AT. stock.
Keywords: LON:AT., ASHTEAD TECHNOLOGY HOLDINGS PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Technical Analysis with Algorithmic Trading
- What are the most successful trading algorithms?
- How do you pick a stock?
LON:AT. Target Price Prediction Modeling Methodology
Stock market prediction is a crucial and challenging task due to its nonlinear, evolutionary, complex, and dynamic nature. Research on the stock market has been an important issue for researchers in recent years. Companies invest in trading the stock market. Predicting the stock market trend accurately will minimize the risk and bring a maximum amount of profit for all the stakeholders. During the last several years, a lot of studies have been done to predict stock market trends using Traditional, Machine learning and deep learning techniques. We consider ASHTEAD TECHNOLOGY HOLDINGS PLC Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of LON:AT. 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(Wilcoxon Rank-Sum Test)5,6,7= X R(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+3 month)
n:Time series to forecast
p:Price signals of LON:AT. stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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LON:AT. Stock Forecast (Buy or Sell) for (n+3 month)
Sample Set: Neural NetworkStock/Index: LON:AT. ASHTEAD TECHNOLOGY HOLDINGS PLC
Time series to forecast n: 25 Sep 2022 for (n+3 month)
According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy LON:AT. 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
ASHTEAD TECHNOLOGY HOLDINGS PLC assigned short-term Ba2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the LON:AT. stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy LON:AT. stock.
Financial State Forecast for LON:AT. Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba2 | B2 |
| Operational Risk | 34 | 45 |
| Market Risk | 88 | 65 |
| Technical Analysis | 89 | 36 |
| Fundamental Analysis | 64 | 62 |
| Risk Unsystematic | 68 | 47 |
Prediction Confidence Score
References
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- Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
- R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
- Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
- Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
- Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
- Li L, Chu W, Langford J, Moon T, Wang X. 2012. An unbiased offline evaluation of contextual bandit algo- rithms with generalized linear models. In Proceedings of 4th ACM International Conference on Web Search and Data Mining, pp. 297–306. New York: ACM
Frequently Asked Questions
Q: What is the prediction methodology for LON:AT. stock?A: LON:AT. stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Wilcoxon Rank-Sum Test
Q: Is LON:AT. stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:AT. Stock.
Q: Is ASHTEAD TECHNOLOGY HOLDINGS PLC stock a good investment?
A: The consensus rating for ASHTEAD TECHNOLOGY HOLDINGS PLC is Buy and assigned short-term Ba2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:AT. stock?
A: The consensus rating for LON:AT. is Buy.
Q: What is the prediction period for LON:AT. stock?
A: The prediction period for LON:AT. is (n+3 month)
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