In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior. Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks. The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis. We evaluate Taiwan Weighted Index prediction models with Modular Neural Network (Market News Sentiment Analysis) and Spearman Correlation1,2,3,4 and conclude that the Taiwan Weighted Index 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 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
- Prediction Modeling
- Investment Risk
- Operational Risk
Taiwan Weighted Index Target Price Prediction Modeling Methodology
Predicting stock index with traditional time series analysis has proven to be difficult an Artificial Neural network may be suitable for the task. A Neural Network has the ability to extract useful information from large set of data. This paper presents a review of literature application of Artificial Neural Network for stock market predictions and from this literature found that Artificial Neural Network is very useful for predicting world stock markets. We consider Taiwan Weighted Index Stock Decision Process with Spearman Correlation 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(Spearman Correlation)5,6,7= X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+6 month)
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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Taiwan Weighted Index Stock Forecast (Buy or Sell) for (n+6 month)
Sample Set: Neural NetworkStock/Index: Taiwan Weighted Index Taiwan Weighted Index
Time series to forecast n: 12 Sep 2022 for (n+6 month)
According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold 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 B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Spearman Correlation1,2,3,4 and conclude that the Taiwan Weighted Index 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 Taiwan Weighted Index stock.
Financial State Forecast for Taiwan Weighted Index Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B1 | B1 |
| Operational Risk | 79 | 62 |
| Market Risk | 73 | 70 |
| Technical Analysis | 78 | 54 |
| Fundamental Analysis | 33 | 45 |
| Risk Unsystematic | 50 | 55 |
Prediction Confidence Score
References
- V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
- M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
- Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
- Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
- V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
- Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
- S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
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 Modular Neural Network (Market News Sentiment Analysis) and Spearman Correlation
Q: Is Taiwan Weighted Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold Taiwan Weighted Index Stock.
Q: Is Taiwan Weighted Index stock a good investment?
A: The consensus rating for Taiwan Weighted Index is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of Taiwan Weighted Index stock?
A: The consensus rating for Taiwan Weighted Index is Hold.
Q: What is the prediction period for Taiwan Weighted Index stock?
A: The prediction period for Taiwan Weighted Index is (n+6 month)
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