A Practical Guide to Quantitative Portfolio Trading
Daniel Alexandre Bloch
Universite Paris VI Pierre et Marie Curie
After 13 years at the head of KG Funds, the firm's founder, Ike Kier, has decided to step down and return outside capital to investors. The firm manages around $613 million of assets across its funds and client accounts. According to a copy of the firm's latest investor update, Kier has decided to step down Read More
We discuss risk, preference and valuation in classical economics, which led academics to develop a theory of market prices, resulting in the general equilibrium theories. However, in practice, the decision process does not follow that theory since the qualitative aspect coming from human decision making process is missing. Further, a large number of studies in empirical finance showed that financial assets exhibit trends or cycles, resulting in persistent inefficiencies in the market, that can be exploited. The uneven assimilation of information emphasised the multifractal nature of the capital markets, recognising complexity. New theories to explain financial markets developed, among which is a multitude of interacting agents forming a complex system characterised by a high level of uncertainty. Recently, with the increased availability of data, econophysics emerged as a mix of physical sciences and economics to get the best of both world, in view of analysing more deeply assets’ predictability. For instance, data mining and machine learning methodologies provide a range of general techniques for classification, prediction, and optimisation of structured and unstructured data. Using these techniques, one can describe financial markets through degrees of freedom which may be both qualitative and quantitative in nature. In this book we detail how the growing use of quantitative methods changed finance and investment theory. The most significant benefit being the power of automation, enforcing a systematic investment approach and a structured and unified framework. We present in a chronological order the necessary steps to identify trading signals, build quantitative strategies, assess expected returns, measure and score strategies, and allocate portfolios.
A Practical Guide to Quantitative Portfolio Trading – Introduction
There is a vast literature on the investment decision making process and associated assessment of expected returns on investments. Traditionally, historical performances, economic theories, and forward looking indicators were usually put forward for investors to judge expected returns. However, modern finance theory, including quantitative models and econometric techniques, provided the foundation that has revolutionised the investment management industry over the last 20 years. Technical analysis have initiated a broad current of literature in economics and statistical physics refining and expanding the underlying concepts and models. It is remarkable to note that some of the features of financial data were general enough to have spawned the interest of several fields in sciences, from economics and econometrics, to mathematics and physics, to further explore the behaviour of this data and develop models explaining these characteristics. As a result, some theories found by a group of scientists were rediscovered at a later stage by another group, or simply observed and mentioned in studies but not formalised. Financial text books presenting academic and practitioners findings tend to be too vague and too restrictive, while published articles tend to be too technical and too specialised. This guide tries to bridge the gap by presenting the necessary tools for performing quantitative portfolio selection and allocation in a simple, yet robust way. We present in a chronological order the necessary steps to identify trading signals, build quantitative strategies, assess expected returns, measure and score strategies, and allocate portfolios. This is done with the help of various published articles referenced along this guide, as well as financial and economical text books. In the spirit of Alfred North Whitehead, we aim to seek the simplest explanations of complex facts, which is achieved by structuring this book from the simple to the complex. This pedagogic approach, inevitably, leads to some necessary repetitions of materials. We first introduce some simple ideas and concepts used to describe financial data, and then show how empirical evidences led to the introduction of complexity which modified the existing market consensus. This book is divided into in five parts. We first present and describe quantitative portfolio trading in classical economics, and provide the paramount statistical tools. We then discuss quantitative trading in inefficient markets before detailing quantitative trading in multifractal markets. At last, we we present a few numerical tools to perform the necessary computation when performing quantitative trading strategies. The decision making process and portfolio allocation being a vast subject, this is not an exhaustive guide, and some fields and techniques have not been covered. However, we intend to fill the gap over time by reviewing and updating this book.
An overview of quantitative portfolio trading
Following the spirit of Focardi et al. , who detailed how the growing use of quantitative methods changed finance and investment theory, we are going to present an overview of quantitative portfolio trading. Just as automation and mechanisation were the cornerstones of the Industrial Revolution at the turn of the 19th century, modern finance theory, quantitative models, and econometric techniques provide the foundation that has revolutionised the investment management industry over the last 20 years. Quantitative models and scientific techniques are playing an increasingly important role in the financial industry affecting all steps in the investment management process, such as
- defining the policy statement
- setting the investment objectives
- selecting investment strategies
- implementing the investment plan
- constructing the portfolio
- monitoring, measuring, and evaluating investment performance
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