Author Archives: chico_x

Li’s family business map and spring layout analysis

15 Sunday Apr 2018

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Summary: In this article, from the graph of major holding companies and it’s main shareholders under Richard Li Tzar Kai, Victor Li Tzar Kuoi, and Li Ka-shing, we found in what areas are they investing and the complex network of relationships between subsidiaries. By analyzing the annual reports of Victor Li Tzar Kuoi and Richard Li Tzar Kai’s company’s companies, we can determine whether Li Ka-shing made the right decision when he announced his retirement. 

Background:

Li Ka-shing, a Hong Kong billionaire who will turn 90 this summer, announced his official retirement in May 2018. In January 2015, “Forbes” magazine announced the ranking of Hong Kong’s richest man-Li Ka-shing’s net assets totaled 33.5 billion U.S. dollars, or 260 billion U.S. dollars, making him the richest man in Hong Kong, only replaced by SF Express’s founder Wang Wei in 2017.

He announced on March 16th that he’ll step down as chairman of CK Hutchison Holding Ltd. and CK asset Holding Ltd., making way for his eldest son, Victor Li Tzar Kuoi. Victor assisted Li Ka-shing to run CK holding Ltd. for many years and his youngest son, Richard Li Tzar Kai mainly invested in communications and media business outside.

Richard Li Tzar Kai

We grabbed the information from Who’s Who and HKEX(The Stock Exchange of Hong Kong Limited) and got the name of major holding companies and main shareholder of companies. after establishing their relationship, we got graph below.

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Business Map of Richard Li Tzar Kai

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Who control the discourse power in 红楼梦？

15 Sunday Apr 2018

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Summary: Dream of the Red Chamber, also called The Story of the Stone, composed by Cao Xueqin, is one of China’s four great classical novels. Long considered a masterpiece of Chinese literature. The novel is generally acknowledged to be the pinnacle of Chinese fiction. We use the graphics tool to analyze the short path, centralization, structure of degree, clustering coefficient and the clique of those data. And try to find out how closely those characters are connected, and who might be the Social Queen or King in this story.

According to analyst Jiang Qi, there are 448 characters in “A Dream of Red Mansions”. We picked 187 characters who are relatively more important than others in this book and use the graphics tool to analyze these data and get the network below to show their relationship in the Dream of the Red Chamber.

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The relationship between the 187 characters in the book

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Analyze tracking errors between ETF and stock market index in the last decade

26 Monday Mar 2018

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5571521786922_.pic_hd.jpgSummary: In this article, under the context of Hong Kong’s Mandatory Provident Fund scheme (MPF), we find that some Exchange Traded Fund (ETF) occupy large holdings in a particular MPF fund. By calculating the tracking errors over the past ten years, the year of 2008 stands out with the highest tracking error, which corresponds with the global financial crisis. In other years with more stable stock markets, ETF performs relatively well. We also calculate returns between two ETFs and the indexes they track respectively; the results turn out to be different.

We use Python as a calculator, rely on pandas to read csv files downloaded from Yahoo Finance, and time series to present data in the form of charts.

Background

Hong Kong has a rapidly aging society. According to the Population Projections released by Census and Statistics Department, HK is expected to have a third of its population that aged 65 and above by the mid of 21st century. There is a growing concern over the issue of social security especially for the elderly, since the burden for the employed population in the future to take care of the retirees will be unbearable.

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Inside Douban’s Top 250, a door pries into the world of audiences in mainland China

15 Thursday Mar 2018

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Summary: In this article, we crawl and analyze the top 250 films evaluated by DouBan users, find their preference on specific directors, types and regions, and also see trends of movies in different regions, particularly in America and Hong Kong. Besides, we pay attention to analyze the rise and down of Hong Kong film productions, and also add some background information for a better understanding. 

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Douban top 250

In general, the best ways to reflect a movie’s preference and prevalence are through box office and ratings. But box office is biased since lots of movies are blocked in China and people are likely to be affected by several factors.  Thus, it’s more objective to examine it through ratings.

Douban Movie is a famous Chinese film rating site, with millions of users watching, rating, and commenting on movies day by day. Considering a large number of Douban users and the relatively objective measurement, data from the Top 250 list is suitable for our analysis.

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Hong Kong Midnight Dinning Guide

02 Friday Mar 2018

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Summary: Hong Kong is a commercial hub that never sleeps in Asia. There are numerous restaurants feeding workaholics who work overtime and party animals who indulge in strongly beating music and alcohol at midnight. We search on OpenRice, the most popular dining guide website in Hong Kong, and find that there are 942 restaurants still open after 11.30am in Hong Kong. Among them, we crawl the information of 250 most popular ones among them in order to pitch an overall scene of Hong Kong’s midnight dinner.

We try to figure out four points below:

  • Where to hunt midnight food in Hong Kong?
  • How much do you need to pay a meal at midnight?
  • What kinds of food are provided at midnight?
  • What kinds of restaurants can you choose at midnight?

After that, we make a recommendation on midnight restaurants based on our analysis results.

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Earthquakes in Southeast Asia in 50 years

02 Friday Mar 2018

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Summary: We used API (Application Programming Interface) as the source to extract data from the USGS database in order to analyze the last 50 years and estimate the frequency of earthquakes in Southeast Asia. With the help of Python, the extracted data was exported into CSV file for categorizing different parameters such as by country, magnitude and year.

Introduction

Application programming interface (API) is commonly used to extract data from a remote website server. In layman term, API is used to retrieve data or information from another program. There are several websites such as Facebook, the USGS, Twitter, Reddit, which offer web-based API helping get information or data.

In order to retrieve data, we will send requests to the host web server where you want to extract the data and tweak parameters like URL in the module to connect to the server. Different websites have different requests format and can easily be accessed through the host’s website.

In our module, we will be extracting the data of earthquakes that hit Southeast Asia in the last 50 years from the web server of USGS using API.

One of the most frequent natural disasters on planet earth is earthquake. The sharp unleash of energy from Earth’s lithosphere generates seismic waves which lead to sudden shaking of the surface of the Earth. This natural disaster has led to the death of thousands of millions of people all around the world.

The strength of earthquakes is measured through Richter magnitude scale or just magnitude. The magnitude is the scale which ranges from 1-10.

The most highly sensitive region in the world prone to the earthquakes are Southeast Asian countries. To find the trend in the region, we extracted 50 years data from USGS by using API and convert the numbers into CSV file through Python coding for a comprehensive understanding of earthquakes situation in Southeast Asia.

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Using Big Data to Figure Out How Fair China Daily News is

02 Friday Mar 2018

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Summary: Unfair and imbalanced news stories always mislead readers, hiding and even distort truths, thus decreasing the credibility of media as well as increasing ‘news victims’. As a qualified news organization, one must get its news as close to the fact as possible. This time we want to take China Daily as an example, to analyze whether its news is fair or not.

We decided to rely on data to quantize the requirement, thus we use python to show the most effective way to figure out the fairness of news.

Background: Difficulty to Reach Absolute Objectivity

According to the Cambridge online dictionary, objectivity means “not influenced by personal opinion or feeling.” For a long time in journalism, objectivity meant writing a story without putting any personal opinion into it.

Over the last several years, many journalists stopped using “objectivity” in favor of the word “fairness.” Complete objectivity, they reasoned, is impossible. Fairness is more possible. Fairness means that you tell a story in ways that are fair to all sides once all the available information is considered.

Telling a story fairly is more difficult than it sounds. Reporters try to put colorful images and descriptions into their stories. For fresh reporters, especially those working in a second language, it can sometimes be difficult to distinguish between colorful description and editorializing. Some words have a feeling or connotation to them that is hard to recognize. Some English words have “loaded” or “double” meanings that are extremely positive or negative. Writers should be aware of the positive or negative meanings of a word and how its use to affect an article. Also, as human beings, we all have feelings and opinions about events and issues around us—-it is sometimes difficult to conceal those feelings, especially if we feel strongly about something. These feelings sometimes come through in our stories in the words we choose.

Therefore, the TextBlob, a module of python, is designed for pointing out humans’ subjectivity in news.

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Evaluate Bonds for Your Investment Plan

08 Thursday Feb 2018

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Summary: Everyone seeks fortune. But managing financial affairs is not an easy cook for the general public since that business jargon and the complicated fluctuations in price have confused many investors who merely have the faintest idea of investment. At this point, bonds — a fixed income investment with a defined holding period — draws the attention of many, yet a less risky basket of this kind could still be affected by the ever-changing market.

Thus, we used Python and the universally-acknowledged bond valuation method, Discounted Cash Flow (DCF), to design an efficient and easy-understanding calculator to help green hands to estimate if they can receive the expected proportion of benefit from a given bond.

Understanding Bonds: What is a Bond and How Bonds Work

“A bond is a fixed income investment in which an investor loans money to an entity which borrows the funds for a defined period of time at a fixed interest rate.”

— Investopedia.com

图片 1.png▲ A Bond’s Lifetime (Source: Vanguard)

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Calculate Marketing Objective for Your Media Startup

01 Thursday Feb 2018

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Summary: We use Python to make a financial calculator to help media startup founders to find out the break-even point of their business model. In other words, it can estimate how many visitors should they acquire to become profitable. We will also show you how to numerically solve the equation and study the sensitivity of key operating parameters, like member fee and CPM of Ads.

Start up A New Business

First of all, you should start up your new business. As all of our group members will graduate from the communication school of HKBU, we want to start up a new business together in the media industry. After doing analysis about the industry and competitors, we decided to found up a news website about new technologies, innovations and application in the media industry.

Business Model

Basically, our business model is like this. We produce high-quality content in different formats to help our target audience to know what is the newest trend and hot topic in the media industry. and on the monetization side, we have two ways to get revenues. The first is by combining ads into our website like feed ads, and the other way is by charging the subscription fee for exclusive content.

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Before we start to find investors, however, we should first get to know how much money is needed to make the website work.

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