Narrow markets are typically considered those that due to limited liquidity or peculiarities in its investor base, such as a particularly high concentration of retail investors, make the stock market less efficient and arguably less predictable. We show in this article that neural networks, applied to narrow markets, can provide relatively accurate forecasts in narrow markets. However, practical considerations such as potentially suboptimal trading infrastructure and stale prices should be taken into considerations. There is ample existing literature describing the use of neural network as a forecasting tool in deep stock markets. The application of neural networks to narrow markets have received much less literature coverage. It is however an important topic as having reliable stock forecasting tools in narrow markets can help with the development of the local stock market, potentially also helping the real economy. Neural networks applied to moderately narrow markets generated forecasts that appear to be comparable, but typically not as accurate, as those obtained in deep markets. These results are consistent across a wide range of learning algorithms and other network features such as the number of neurons. Selecting the appropriate network structure, including deciding what training algorithm to use, is a crucial step in order to obtain accurate forecasts.

中文翻译：

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狭窄股票市场中的神经网络

狭隘市场通常被认为是由于流动性有限或投资者基础的特殊性（例如散户投资者特别集中）而导致股票市场效率较低且可预测性较低的市场。我们在本文中展示了应用于狭窄市场的神经网络可以在狭窄市场中提供相对准确的预测。但是，应考虑实际考虑因素，例如潜在的次优交易基础设施和陈旧价格。已有大量文献描述了在深度股票市场中使用神经网络作为预测工具。神经网络在狭窄市场中的应用获得的文献报道要少得多。然而，这是一个重要的话题，因为在狭窄的市场中拥有可靠的股票预测工具可以帮助当地股票市场的发展，也可能有助于实体经济。应用于适度狭窄市场的神经网络生成的预测似乎具有可比性，但通常不如在深度市场中获得的预测准确。这些结果在广泛的学习算法和其他网络特征（例如神经元数量）中是一致的。选择合适的网络结构，包括决定使用何种训练算法，是获得准确预测的关键步骤。如在深度市场中获得的那些。这些结果在广泛的学习算法和其他网络特征（例如神经元数量）中是一致的。选择合适的网络结构，包括决定使用何种训练算法，是获得准确预测的关键步骤。如在深度市场中获得的那些。这些结果在广泛的学习算法和其他网络特征（例如神经元数量）中是一致的。选择合适的网络结构，包括决定使用何种训练算法，是获得准确预测的关键步骤。