Brian Huge and I just posted a working paper following six months of…

Deep Learning is picking momentum in Quantitative Finance, outside the obvious application to the prediction of asset prices (where to my knowledge it is not particularly effective) and spreading into the more serious application area of option pricing and risk management.

These two recent papers clearly demonstrate the benefits of DL as a pricing technology alternative to the classical FDM and Monte-Carlo in certain contexts:…

In my blogs, I often distinguish between event data and metrics.  I usually say something to the effect that events help to explain the metrics - or events “provide the story behind the metrics.”  In this blog, I will be discussing two competing lines of thought behind events:  internal capacity and external demand.  Why do sales appear much lower for the month of June compared to July?  Some explanations relating to internal capacity are as follows:  “There weren’t enough agents in June to…

Six Sigma is a quantitative approach to problem solving - to solve certain types of problems. At the root of Six Sigma is an improvement methodology that can be described by the acronym DMAIC: define, measure, analyze, improve, and control [1]. Those interested in reading up on Six Sigma might consider the book for dummies, which I found fairly succinct. Those wondering what I mean by "certain types of problems" should consider how to apply the approach to their own business circumstances. I…

Probably like most people, I tend to recognize data as a stream of values. Notice that I use the term values rather than numbers although in practice I guess that values are usually numerical. A data-logger gathering one type of data would result in data all of a particular type. Perhaps the concept of “big data” surrounds this preconception of data of type except that there are much larger amounts. Consider an element of value in symbolic terms, which I present below: there is an index such…

In recent blogs, I have been distinguishing between quantitative data and narrative data. I believe that I separated the two forms relatively well. Although I originally focused on the differences in data in order to give narrative "its own space," actually there can be a symbiotic relationship between the two types of data. In my last blog, I said that quantitative data can be incorporated into narrative data. In my submission today, I will be discussing how the narrative can be used to…

In recent blogs, I wrote about using codified narrative as a form of data. I also discussed using attribution models to systematically evaluate codified narrative for ontological constructs: e.g. "child abuse" "physical confinement" "cannibalism." I provide a brief overview of these topics a bit later in the blog. The third important piece to make use of narrative data involves "attribution profiling" in a process that I call "catching scent." Following the odour of data involves…

In this blog, I will be discussing the use of attribution models in relation to codified narrative. For this purpose, I will be referring to the plots of two films: the 1974 horror classic “The Texas Chainsaw Massacre”; and a 2014 dark comedy called “Tusk.” I have my own codification system called BERLIN: this is short for “Behavioural Event Reconstruction Linguistic Interface for Narratives.” An attribution model supports the inference of meaning from data. Imagine a student one day going…

Codified narrative is the product of converting human-friendly narrative into computer-friendly code. In past blogs, I discussed my own approach towards this process of codification. Here, I will be covering the idea of spatial, temporal, and contextual distribution of codified narrative. I have never suggested that narrative can or should be used in place of quantitative data. However, I have reflected on how the quantitative regime has tended to dominate discourse; this has perhaps led to…

In this blog, I will be discussing some distinct types of data involved in feedback. The types that I will be covering are as follows: 1) structural; 2) event; 3) quantitative; 4) contextual; and 5) systemic. In 2014, I recall reading a number of blogs about three types of data: prescriptive, descriptive, and predictive. There was a data scientist apparently on tour lecturing extensively about these three types. I don't recall the individual's name. Well, prescription, description, and…

The first computer program that I encountered mimicking or emulating human interaction through language was called "Eliza." The version that I knew ran on the Commodore PET. It communicated in English. Eliza made comments that made some sense but which indicated lack of understanding of the conversation. If a person mentions "mother," Eliza might…

Companies are fighting tooth and nail to stay ahead of the competition. Besides deploying aggressive market campaigns, they are focusing on increasing their dependency on research in order to understand market competition and trends. The changing market dynamics entails closer look at various aspects connected to the data.

The market research aims at giving business organizations information about customers or markets for making informed business decisions. This effort to collect…