On 3 Dec 2018 when the US treasury yield curves inverted (a short-term US government bond yield is higher than its long-term yield), Economists quickly warned the stock market of an impending economic slowdown or even a recession. The following…

ContinueAdded by Zhongmin Luo on December 5, 2018 at 1:00am — No Comments

Two weeks ago, I was invited to present about Machine Learning and its applications in Quantitative Finance at a conference in London, UK.

Without a break, I went through four research papers which I completed over the last two years with my co-author; here is one page of the presentation.

; Brummelhuis and Luo, forthcoming in…*CDS**Rate Construction Methods by Machine Learning**Techniques: Methodology and Results*

Added by Zhongmin Luo on November 26, 2018 at 5:00am — No Comments

Imagine that one day, you see people are queuing up in front of **Bank A**; so you ask the staff at the counter, you are told that they are offering anyone (regardless of their credit history) a loan of $100,000 at a fixed annual rate at **2%**. You then look around, the **Bank B** next door offers 1-year term deposit with a fixed annual rate at **3%** for the same amount ($100,000). After 5 minutes' waiting, you sign for the loan from Bank…

Added by Zhongmin Luo on November 21, 2018 at 2:30pm — 2 Comments

Each year, Risk Quant Europe Conference, a conference well-attended by practitioners from banking, asset management, insurers as well as academics from Europe, selects two papers to present in their annual conference.

For 2018, our paper is lucky to be one of the two winning papers selected by the Advisory Board for the conference to be held in London. Please feel free to check out our paper titled *CDS Rate Construction Methods by Machine Learning…*

Added by Zhongmin Luo on February 24, 2018 at 2:00am — No Comments

Does it sound familiar to you? In order to get an idea of how to choose a parameter for a given classifier, you have to cross reference to a number of papers or books, which often turn out to present competing arguments for or against a certain parameterization choice but with few applications to real-world problems.

For example, you may find a few papers discussing optimal selection of K in…

ContinueAdded by Zhongmin Luo on June 5, 2017 at 7:30pm — 6 Comments

**Cross Validation** is often used as a tool for model selection across classifiers. As discussed in detail in the following paper https://ssrn.com/abstract=2967184, Cross Validation is typically performed in the following steps:

- Step 1: Divide the original sample into K sub samples; each subsample typically has equal sample size and is referred to as one fold, altogether,…

Added by Zhongmin Luo on June 2, 2017 at 7:00pm — 3 Comments

In practice, we often have to make parameterization choices for a given classifier in order to achieve optimal classification performances; just to name a few examples:

- Neural Network: e.g., the optimal choice of Activation Functions, # of hidden units
- Support Vector Machine: e.g., the optimal choice of Kernel Functions
- Ensemble: e.g., the number of Learning Cycles for Bagging.
- Discriminant Analysis: e.g., Linear/Quadratic; regularization…

Added by Zhongmin Luo on May 29, 2017 at 12:49am — No Comments

Past literature show that the comparisons of classifier's performance are specific to the types of datasets (e.g., Pharmaceutical industry data) used; i.e., some classifiers may perform better in some context than others. A paper titled **CDS Rate Construction Methods by Machine Learning Techniques** conducts the performance comparison exclusively in the context of financial market by applying a wide range of classifiers to provide solution to so-called **Shortage of…**

Added by Zhongmin Luo on May 23, 2017 at 1:30am — No Comments

- Inverted yield curve followed by 800-point plunge in Dow next day; how about inverted CDS curves?
- Applying Machine Learning Techniques in Quantitative Finance
- An Application of Data Science and Mathematics in Finance
- Machine Learning Techniques based paper one of the two Risk Quant Europe 2018 Call for Paper Winners
- A Guide for Applying Machine Learning Techniques in Finance
- Choice of K in K-fold Cross Validation for Classification in Financial Market
- Parameter Selection in Classification for Financial Market

- Applying Machine Learning Techniques in Quantitative Finance
- A Guide for Applying Machine Learning Techniques in Finance
- An Application of Data Science and Mathematics in Finance
- Choice of K in K-fold Cross Validation for Classification in Financial Market
- Apply Machine Learning Techniques to Problems in Financial Market
- Inverted yield curve followed by 800-point plunge in Dow next day; how about inverted CDS curves?
- Parameter Selection in Classification for Financial Market

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