Forecasting and optimization of stock trades

Abstract

The stock market is a complex and challenging field of research that has attracted researchers from several fields, such as for instance engineering. This dissertation approaches stock market analysis from an engineering point of view. It is empirically shown that techniques, such as neural networks, can be applied in many stock markets, as a tool creating reasonably accurate stock forecasts. This approach was also analyzed in the context of narrow markets. In this dissertation a broad definition of narrow market was followed, encompassing not only stock markets with a small trading volumes, which can potentially distort stock prices, but also markets that while having large daily trading volumes have some other features, such as a relatively large proportion of retail investors compared to institutional investors, that might result on price distortions. It is shown that neural networks can be applied, for forecasting purposes, even in narrow markets. However, some practical issues, such as stale prices, should be taken into account. The topic of technical indicator selection is also discussed in this dissertation. There is an ever increasing amount of technical indicators that are used in an attempt to discern future stock prices trends. Some of those indicators can generate contradicting signals. Therefore, it is important to choose the right combination of technical indicators when taking stock investment decisions. In this dissertation it is presented a new combinatorial algorithm for stock selection. It is shown, using this algorithm, that predictions are more accurate than using all the technical indicators together. It should also be noticed that using all the possible combinations it is not feasible given the enormous amount of potential combinations. Therefore, it is of clear importance to have algorithms that can generate adequate combinations of those technical indicators. The adaptation and application to stock forecasting of a forecasting technique based on local data is also shown in this thesis. It has been shown that this technique generates forecasts that are better than some commonly used benchmarks. At the core of this approach there is the assumption that stock prices then to follow, at least to some degree, historical patterns. Besides accuracy, an advantage of this technique is that it generates forecasts relatively fast, not requiring a time consuming training phase. Having a better understanding of the likely losses of the trade can help the investor to have a more complete investment decision process. Thus, it is also important, particularly from a risk management point of view, to have techniques that generate probability distributions for the predictions that can be used to obtain intervals that are guaranteed to contain the future real prices with a prescribed probability. In this dissertation a probabilistic price interval estimation strategy has been adapted and applied to the problem of finding $k$-step ahead price intervals, getting better results than those obtained with well-known techniques. Having good forecasting techniques is only a part of the investment process. After a buy or sell decision has been made it is important to carry out that trade efficiently in what it is commonly referred as ``best execution''. There are multiple factors to take into account after the purchase (or sell) of a stock is decided, for instance in which period of the day (or over how many days) to carry out the trade. This is also related to the idea of avoiding distorting the market with the trade, i.e., a large order might unintentionally distort market prices. In this dissertation it is presented an approach that generates better results than frequently used benchmarks, such as for instance the Time Weighted Price (TWAP) or the Volume Weighted Price (VWAP). The approach is based on the concept of receding horizon optimization, well known in the predictive control community, but not used in the optimization of the execution of large trade orders. The technique can accommodate both market and limit orders and has shown a great potential economic impact in the execution of such large orders.