AUC Score :
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n:
Methodology : Modular Neural Network (Speculative Sentiment Analysis)
Hypothesis Testing : ElasticNet Regression
Surveillance : Major exchange and OTC
1The accuracy of the model is being monitored on a regular basis.(15-minute period)
2Time series is updated based on short-term trends.
Summary
Huron Consulting Group Inc. Common Stock prediction model is evaluated with Modular Neural Network (Speculative Sentiment Analysis) and ElasticNet Regression1,2,3,4 and it is concluded that the HURN stock is predictable in the short/long term. A modular neural network (MNN) is a type of artificial neural network that can be used for speculative sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of speculative sentiment analysis, MNNs can be used to identify the sentiment of people who are speculating about the future value of an asset, such as a stock or a cryptocurrency. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising.5 According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Hold
Key Points
- Modular Neural Network (Speculative Sentiment Analysis) for HURN stock price prediction process.
- ElasticNet Regression
- What are main components of Markov decision process?
- Nash Equilibria
- How can neural networks improve predictions?
HURN Stock Price Forecast
We consider Huron Consulting Group Inc. Common Stock Decision Process with Modular Neural Network (Speculative Sentiment Analysis) where A is the set of discrete actions of HURN 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
Sample Set: Neural Network
Stock/Index: HURN Huron Consulting Group Inc. Common Stock
Time series to forecast: 8 Weeks
According to price forecasts, the dominant strategy among neural network is: Hold
n:Time series to forecast
p:Price signals of HURN stock
j:Nash equilibria (Neural Network)
k:Dominated move of HURN stock holders
a:Best response for HURN target price
A modular neural network (MNN) is a type of artificial neural network that can be used for speculative sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of speculative sentiment analysis, MNNs can be used to identify the sentiment of people who are speculating about the future value of an asset, such as a stock or a cryptocurrency. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising.5 Elastic net regression is a type of regression analysis that combines the benefits of ridge regression and lasso regression. It is a regularized regression method that adds a penalty to the least squares objective function in order to reduce the variance of the estimates, induce sparsity in the model, and reduce overfitting. This is done by adding a term to the objective function that is proportional to the sum of the squares of the coefficients and the sum of the absolute values of the coefficients. The penalty terms are controlled by two parameters, called the ridge constant and the lasso constant. Elastic net regression can be used to address the problems of multicollinearity, overfitting, and sensitivity to outliers. It is a more flexible method than ridge regression or lasso regression, and it can often achieve better results.6,7
For further technical information as per how our model work we invite you to visit the article below:
HURN Stock Forecast (Buy or Sell) Strategic Interaction Table
Strategic Interaction Table Legend:
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 (Grey to Black): *Technical Analysis%
Financial Data Adjustments for Modular Neural Network (Speculative Sentiment Analysis) based HURN Stock Prediction Model
- The characteristics of the hedged item, including how and when the hedged item affects profit or loss, also affect the period over which the forward element of a forward contract that hedges a time-period related hedged item is amortised, which is over the period to which the forward element relates. For example, if a forward contract hedges the exposure to variability in threemonth interest rates for a three-month period that starts in six months' time, the forward element is amortised during the period that spans months seven to nine.
- Subject to the conditions in paragraphs 4.1.5 and 4.2.2, this Standard allows an entity to designate a financial asset, a financial liability, or a group of financial instruments (financial assets, financial liabilities or both) as at fair value through profit or loss provided that doing so results in more relevant information.
- If a variable-rate financial liability bears interest of (for example) three-month LIBOR minus 20 basis points (with a floor at zero basis points), an entity can designate as the hedged item the change in the cash flows of that entire liability (ie three-month LIBOR minus 20 basis points—including the floor) that is attributable to changes in LIBOR. Hence, as long as the three-month LIBOR forward curve for the remaining life of that liability does not fall below 20 basis points, the hedged item has the same cash flow variability as a liability that bears interest at three-month LIBOR with a zero or positive spread. However, if the three-month LIBOR forward curve for the remaining life of that liability (or a part of it) falls below 20 basis points, the hedged item has a lower cash flow variability than a liability that bears interest at threemonth LIBOR with a zero or positive spread.
- An entity can rebut this presumption. However, it can do so only when it has reasonable and supportable information available that demonstrates that even if contractual payments become more than 30 days past due, this does not represent a significant increase in the credit risk of a financial instrument. For example when non-payment was an administrative oversight, instead of resulting from financial difficulty of the borrower, or the entity has access to historical evidence that demonstrates that there is no correlation between significant increases in the risk of a default occurring and financial assets on which payments are more than 30 days past due, but that evidence does identify such a correlation when payments are more than 60 days past due.
*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.
HURN Huron Consulting Group Inc. Common Stock Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B2 | B1 |
Income Statement | Baa2 | B2 |
Balance Sheet | C | Baa2 |
Leverage Ratios | C | Ba3 |
Cash Flow | B1 | B3 |
Rates of Return and Profitability | B2 | C |
*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?
References
- Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
- V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
- Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
- A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
- Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
- V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
Frequently Asked Questions
Q: Is HURN stock expected to rise?A: HURN stock prediction model is evaluated with Modular Neural Network (Speculative Sentiment Analysis) and ElasticNet Regression and it is concluded that dominant strategy for HURN stock is Hold
Q: Is HURN stock a buy or sell?
A: The dominant strategy among neural network is to Hold HURN Stock.
Q: Is Huron Consulting Group Inc. Common Stock stock a good investment?
A: The consensus rating for Huron Consulting Group Inc. Common Stock is Hold and is assigned short-term B2 & long-term B1 estimated rating.
Q: What is the consensus rating of HURN stock?
A: The consensus rating for HURN is Hold.
Q: What is the forecast for HURN stock?
A: HURN target price forecast: Hold