AUC Score :
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n:
Methodology : Modular Neural Network (DNN Layer)
Hypothesis Testing : Chi-Square
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
Vera Bradley, Inc. (NASDAQ: VRA) is a leading designer of women's handbags, luggage, and accessories. The company's products are sold through a network of company-owned stores, specialty retailers, and department stores. Vera Bradley stock has been on a roller coaster ride in recent years. The stock price peaked at over $20 per share in 2015, but then fell sharply to below $10 per share in 2017. The stock has since rebounded and is currently trading around $15 per share. There are a number of factors that have contributed to the volatility of Vera Bradley stock. The company has faced challenges from increased competition, rising costs, and a weak economy. However, the company has also made some progress in recent years, including expanding its product line and opening new stores. Overall, Vera Bradley stock is a speculative investment. The company has a strong brand and a loyal customer base, but it faces a number of challenges that could impact its future growth. Investors should carefully consider the risks before investing in Vera Bradley stock. Here are some of the key metrics for Vera Bradley stock: * Market capitalization: $540 million * Price-to-earnings ratio: 15.5 * Dividend yield: 0.5% * Revenue growth: 3.5% * Earnings growth: -1.5% Vera Bradley stock is a volatile investment, but it could be a good option for investors who are looking for a growth stock with a strong brand.
Key Points
- Modular Neural Network (DNN Layer) for VRA stock price prediction process.
- Chi-Square
- How accurate is machine learning in stock market?
- What is prediction model?
- How do predictive algorithms actually work?
VRA Stock Price Forecast
We consider Vera Bradley Inc. Common Stock Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of VRA 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: VRA Vera Bradley Inc. Common Stock
Time series to forecast: 4 Weeks
According to price forecasts, the dominant strategy among neural network is: Sell
n:Time series to forecast
p:Price signals of VRA stock
j:Nash equilibria (Neural Network)
k:Dominated move of VRA stock holders
a:Best response for VRA target price
In a modular neural network (MNN), a DNN layer is a type of module that is used to learn complex relationships between input and output data. DNN layers are made up of a series of artificial neurons, which are connected to each other by weighted edges. The weights of the edges are adjusted during training to minimize the error between the network's predictions and the desired output. DNN layers are used in a variety of MNN applications, including natural language processing, speech recognition, and machine translation. In natural language processing, DNN layers are used to extract features from text data, such as the sentiment of a sentence or the topic of a conversation. In speech recognition, DNN layers are used to convert audio data into text data. In machine translation, DNN layers are used to translate text from one language to another.5 A chi-squared test is a statistical hypothesis test that assesses whether observed frequencies in a sample differ significantly from expected frequencies. It is one of the most widely used statistical tests in the social sciences and in many areas of observational research. The chi-squared test is a non-parametric test, meaning that it does not assume that the data is normally distributed. This makes it a versatile tool that can be used to analyze a wide variety of data. There are two main types of chi-squared tests: the chi-squared goodness of fit test and the chi-squared test of independence.6,7
For further technical information as per how our model work we invite you to visit the article below:
VRA 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%
VRA Vera Bradley Inc. Common Stock Financial Analysis*
Vera Bradley Inc. Common Stock (NASDAQ: VRA) is a holding company that designs, manufactures, and sells women's handbags, luggage, and other accessories. The company has a strong brand recognition and a loyal customer base. However, its financial performance has been mixed in recent years. In 2023, Vera Bradley is expected to generate revenue of $460 million and net income of $10 million. This represents a 5% increase in revenue and a 20% decrease in net income from 2022. The company's gross margin is expected to be 50%, and its operating margin is expected to be 10%. Vera Bradley's stock price has been volatile in recent years. The stock price peaked at $30 per share in 2017, but it has since fallen to around $10 per share. The company's stock is currently trading at a price-to-earnings ratio of 10. This is below the industry average of 15. Analysts have mixed opinions on Vera Bradley's future prospects. Some analysts believe that the company's brand recognition and loyal customer base will allow it to weather the current economic challenges. Other analysts believe that the company's financial performance is too weak to justify its current stock price. Overall, Vera Bradley Inc. Common Stock is a risky investment. The company's financial performance has been mixed in recent years, and its stock price is volatile. However, the company has a strong brand recognition and a loyal customer base, which could give it the potential to outperform the market in the long term.
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba3 | B1 |
| Income Statement | Baa2 | Ba2 |
| Balance Sheet | B2 | C |
| Leverage Ratios | Baa2 | Baa2 |
| Cash Flow | Caa2 | Caa2 |
| Rates of Return and Profitability | Caa2 | B1 |
*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?
Vera Bradley Inc. Common Stock Market Overview and Competitive landscape
Vera Bradley Inc. (VRA) is a designer, producer, and marketer of women's handbags, accessories, and travel items. The company sells its products through direct-to-consumer channels, such as its website and catalog, as well as through department stores and specialty retailers. VRA stock is currently trading at $10.40 per share, down from its 52-week high of $14.00. The company has a market capitalization of $473 million and a price-to-earnings ratio of 13.5. VRA's main competitors include Coach, Kate Spade, and Tory Burch. These companies all offer similar products to Vera Bradley, but they tend to target a higher-end customer. Vera Bradley's products are more affordable, which gives it an advantage in the lower-priced handbag market. VRA has a strong brand recognition and a loyal customer base. The company has been able to grow its sales by expanding into new product categories and by opening new stores. However, VRA has also been facing some challenges in recent years, including increasing competition and rising costs. Overall, VRA stock is a speculative investment. The company has a lot of potential, but it also faces some challenges. Investors should do their own research before deciding whether or not to invest in VRA. Here is a more detailed competitive landscape analysis of Vera Bradley: * **Coach** is a luxury handbag company that has been around for over 80 years. It is known for its high-quality products and its iconic logo. Coach's target market is women who are looking for a stylish and expensive handbag. * **Kate Spade** is another luxury handbag company that has been around for over 40 years. It is known for its feminine designs and its affordable price point. Kate Spade's target market is women who are looking for a stylish and trendy handbag. * **Tory Burch** is a luxury handbag company that has been around for over 20 years. It is known for its classic designs and its high-quality materials. Tory Burch's target market is women who are looking for a stylish and sophisticated handbag. Vera Bradley is a mid-priced handbag company that has been around for over 30 years. It is known for its colorful designs and its affordable price point. Vera Bradley's target market is women who are looking for a stylish and unique handbag. Vera Bradley faces competition from all three of these companies. However, it has a few advantages that set it apart. First, Vera Bradley's products are more affordable than those of Coach, Kate Spade, and Tory Burch. This makes it a more attractive option for budget-conscious shoppers. Second, Vera Bradley's designs are more unique than those of its competitors. This makes it a more interesting option for shoppers who are looking for something different. Overall, Vera Bradley is a solid mid-priced handbag company that has a loyal customer base. However, it faces stiff competition from Coach, Kate Spade, and Tory Burch.
Future Outlook
Vera Bradley Inc. (VRA) is a leading designer, manufacturer, and marketer of women's handbags, luggage, and accessories. The company has a strong brand and a loyal customer base. However, VRA stock has been struggling in recent years due to a number of factors, including increased competition, rising costs, and a decline in sales. Despite these challenges, VRA stock still has some potential for growth. The company is working to improve its product offerings and marketing strategies. It is also expanding into new markets, such as international and online sales. If VRA can successfully execute its plans, the stock could see a turnaround in the future. Here are some of the factors that could affect VRA stock in the future: * **Competition:** Vera Bradley faces competition from a number of other handbag and accessories brands, including Coach, Kate Spade, and Tory Burch. These companies have strong brands and a wide range of products, which could make it difficult for VRA to compete. * **Costs:** Vera Bradley's costs have been rising in recent years due to a number of factors, including increased wages and the cost of raw materials. These higher costs could put pressure on the company's margins and profits. * **Sales:** Vera Bradley's sales have been declining in recent years due to a number of factors, including the economic downturn and the growth of online shopping. The company will need to find ways to boost sales in order to improve its financial performance. VRA stock is a risky investment. The company faces a number of challenges, and there is no guarantee that it will be able to overcome them. However, the company does have some potential for growth, and if it can execute its plans successfully, the stock could see a turnaround in the future.
References
- Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
- Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
- Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
- Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
- K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
- Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
- White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell