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Big Data and Machine Learning in Quantitative Investment by Tony Guida

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CHAPTER 11Support Vector Machine‐Based Global Tactical Asset Allocation

Joel Guglietta

11.1 INTRODUCTION

In this chapter we show how machine learning, more specifically support vector machine/regression (SVM/R, can help building global tactical asset allocation (GTAA) portfolio. First, we will present a quick literature review on GTAA, explaining the different families of asset allocation. We will then go through a historical perspective of tactical asset allocation in the last 50 years, introducing the seminal concepts behind it. Section 11.3 will explain the definition of support vector machine (SVM) and support vector relevance (SVR). Section 11.4 will present the machine learning model used for tactical asset allocation and will discuss the results.

11.2 FIFTY YEARS OF GLOBAL TACTICAL ASSET ALLOCATION

Running the risk of stating the obvious, the objective of asset allocation is to obtain the best expected return‐to‐risk portfolio (Dahlquist and Harvey, 2001). The authors distinguish three families of asset allocation: (i) benchmark asset allocation, (ii) strategic asset allocation, (iii) GTAA (see Figure 11.1). The investment portfolio strategy built in this chapter belongs to the third class of model where predictions models use today's information set in order to forecast asset returns.

Flow diagram depicting three families of asset allocation with Portfolio weights, Mode, and Information and relationships indicated by arrows.

Figure 11.1 Three families of asset allocation.

Source: Dahlquist and Harvey (2001). ...

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