Prediction of stock market movement is extremely difficult due to its high mutable nature. The rapid ups and downs occur in stock market because of impact from foreign commodities like emotional behavior of investors, political, psychological and economical factors. Continuous unsettlement in the stock market is major reason why investors sell out at the wrong time and often fail to gain the benefit. While investing in stock market investors must not forget the risk of reward rule and expose their holdings to greater risks. Although it is not possible predict stock market movement with full accuracy, losses from selling stocks at wrong time and its impacts can be reduce to greater extent using prediction of stock market movement based on analysis of historical data. We evaluate Taiwan Weighted Index prediction models with Transfer Learning (ML) and Linear Regression1,2,3,4 and conclude that the Taiwan Weighted Index 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 Buy Taiwan Weighted Index stock.
Keywords: Taiwan Weighted Index, Taiwan Weighted Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Can machine learning predict?
- Stock Forecast Based On a Predictive Algorithm
- Stock Rating
Taiwan Weighted Index Target Price Prediction Modeling Methodology
In this paper, we introduce a new prediction model depend on Bidirectional Gated Recurrent Unit (BGRU). Our predictive model relies on both online financial news and historical stock prices data to predict the stock movements in the future. We consider Taiwan Weighted Index Stock Decision Process with Linear Regression where A is the set of discrete actions of Taiwan Weighted Index 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= X R(Transfer Learning (ML)) X S(n):→ (n+4 weeks)
n:Time series to forecast
p:Price signals of Taiwan Weighted Index stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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How do AC Investment Research machine learning (predictive) algorithms actually work?
Taiwan Weighted Index Stock Forecast (Buy or Sell) for (n+4 weeks)
Sample Set: Neural NetworkStock/Index: Taiwan Weighted Index Taiwan Weighted Index
Time series to forecast n: 11 Oct 2022 for (n+4 weeks)
According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy Taiwan Weighted Index 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
Taiwan Weighted Index assigned short-term B3 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Linear Regression1,2,3,4 and conclude that the Taiwan Weighted Index 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 Buy Taiwan Weighted Index stock.
Financial State Forecast for Taiwan Weighted Index Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B3 | Ba1 |
| Operational Risk | 67 | 82 |
| Market Risk | 36 | 65 |
| Technical Analysis | 58 | 56 |
| Fundamental Analysis | 33 | 64 |
| Risk Unsystematic | 37 | 86 |
Prediction Confidence Score
References
- Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
- Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
- 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.
- Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
- Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
- Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
- Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
Frequently Asked Questions
Q: What is the prediction methodology for Taiwan Weighted Index stock?A: Taiwan Weighted Index stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Linear Regression
Q: Is Taiwan Weighted Index stock a buy or sell?
A: The dominant strategy among neural network is to Buy Taiwan Weighted Index Stock.
Q: Is Taiwan Weighted Index stock a good investment?
A: The consensus rating for Taiwan Weighted Index is Buy and assigned short-term B3 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of Taiwan Weighted Index stock?
A: The consensus rating for Taiwan Weighted Index is Buy.
Q: What is the prediction period for Taiwan Weighted Index stock?
A: The prediction period for Taiwan Weighted Index is (n+4 weeks)
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