خرید و دانلود نسخه کامل کتاب Quantitative Finance With Python: A Practical Guide to Investment Management, Trading, and Financial Engineering – Original PDF

C H A P T E R 1 Setting the Stage: Quant Landscape 1.1 INTRODUCTION Quantitative finance and investment management are broad fields that are still evolving rapidly. Technological and modeling innovations over the last decades have led to a series of fundamental shifts in the industry that are likely just beginning. This creates challenges and opportunities for aspiring quants who are, for example, able to harness the new data sources, apply new machine learning techniques or lever- age cutting edge derivative models. The landscape of these fields is also fairly broad, ranging from solving investment problems related to optimal retirement outcomes for investors to providing value to firms by helping them hedge certain undesirable exposures. In spite of the innovations, however, quants must remember that models are by definition approximations of the world. As George Box famously said “All Models are Wrong, Some are Useful”, and this is something all quants should take to heart. When applying a model, the knowledge of its strengths, weaknesses and limitations must be at the forefront of our mind. Exacerbating this phenomenon is the fact that finance, unlike hard sciences like Physics or Chemistry, does not allow for repeatable experiments. In fact, in finance we don’t even know for sure that consecutive exper- iments come from the same underlying distribution because of the possibility of a regime change. Making this even more challenging is the potential feedback loop cre- ated by the presence of human behavior and psychology in the market. This is a key differentiating factor relative to other, harder, sciences. As a result, quants should aim to make models that are parsimonious and only as complex as the situation requires, and to be aware and transparent about the limitations and assumptions1.