Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Application of a Shallow Neural Network to Short-Term Stock Trading   Machine learning is increasingly prevalent in stock market trading. Though
neural networks have seen success in computer vision and natural language
processing, they have not been as useful in stock market trading. To
demonstrate the applicability of a neural network in stock trading, we made a
single-layer neural network that recommends buying or selling shares of a stock
by comparing the highest high of 10 consecutive days with that of the next 10
days, a process repeated for the stock's year-long historical data. A
chi-squared analysis found that the neural network can accurately and
appropriately decide whether to buy or sell shares for a given stock, showing
that a neural network can make simple decisions about the stock market.

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The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat