Abstract
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.
Highlights
Introduction and Related WorkA narrow stock market can be defined in several ways
Liquidity is very low in the Namibian case and occasionally the quoted price does not correspond with an executed trade that day but with the closing price on a previous day
Besides the many differences between narrow and deep stock markets it appears that neural networks are an efficient forecasting tool for both types of markets
Summary
Introduction and Related WorkA narrow stock market can be defined in several ways. In the context of this article a narrow market is considered as one that, either, is not very liquid, i.e., the investor pool is not too large, or that it has some peculiarities, such as a high proportion of retail investors, making price discovery more difficult. The objective of this article is to get a better sense of the feasibility [1], under relatively realistic assumptions, of the applicability of neural networks as a forecasting tool in narrow markets. Even in this type of narrow market, we will show that neural networks are robust enough to generate relatively accurate forecasts. When forecasting stocks or equity indexes one of the most important factors, together with the chosen algorithm, is deciding what input to use. In this case we decided to use several moving averages that will be defined in later sections
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