After some years working in the “data science” domain (too broad concept), it has become clear where Luis’s passion relies. Above all, he wants to be an applied scientist, that is both capable of fundamental research (proven by some scientific publications accomplished) as well as of being a machine learning engineer. Following the state-of-art approaches, adapting, evolving and applying them is what drives him, but also share a passion for technology and developing proper projects and code to truly support a live machine learning product.

MSc Thesis

Title: Actionable Forecasting and Activity Monitoring: applications to financial trading

Supervisor: Luis Torgo

MSc in Computer Science, FCUP-U. Porto

Finished: Aug/2015

Abstract

The thesis addresses a particular class of decision making problems. We name these tasks Actionable Forecasting problems. The main distinguishing feature of this type of decision problems is the fact that decisions are to be taken based on predictions of a numeric variable. Examples of such tasks include for instance the medical diagnosis of a patient based on predictions of some numerical indicator, or deciding which trading action should be taken depending on the prediction of the future evolution of the market prices. We study and compare two different alternative ways of addressing these decision problems: (i) using standard regression models to forecast the numeric variable and on a second step transform these numeric predictions into a decision according to some pre-defined and deterministic decision rules; and (ii) use models that directly forecast the right decision using classification models thus ignoring the intermediate numeric forecasting task. The objective of this study is to determine if both strategies provide identical results or if there is any particular advantage worth being considered that may distinguish each alternative. We also consider some potential limitations of each alternative, where we consider solutions such as the usage of cost-benefit matrices as well as re-sampling algorithms.

We carry out two major studies to compare both alternatives to solve actionable forecasting tasks: (i) one involving a large set of generic and non-temporal tasks; (ii) and a second involving financial trading problems that use price time series with the goal of deciding whether to invest or not in the market with short and long positions.

Decision making in the context of financial trading is a very relevant problem with very high economic impact. We argue that these tasks can be solved as a special instance of actionable forecasting problems.

We have gathered enough experimental evidence to support the conclusion that classification models may be preferable to model the generic tasks, mainly when used together with cost-benefit matrices. We have also observed some differences according to the number of possible decisions per task. The higher the number of possible decisions/actions to make, the higher will be the advantage of the classification models over the alternative of using regression models. With respect to the specific tasks of financial trading, both modelling alternatives revealed similar potential. The usage of re-sampling on such tasks brings too much risk to the models while using cost-benefit matrices on the classification models was beneficial once again.

The last topic addressed in this thesis was the evaluation of financial trading systems. Based on the theoretical framework of activity monitoring, we have proposed a formalisation of financial trading as an instance of these data mining tasks. Using this formalisation we have described algorithms that allow to obtain the ideal timings for holding market positions given some trading preference criteria. These ideal timings can be used as an optimal benchmark against which real trading records can be compared to. The main advantage of this evaluation framework is its adaptability to the investor’s preference biases and also the interpretability of the evaluation outcomes. We present the evaluation framework and test it using trading records of real investors.