stocks
Date: 10/08/2020 How Intel Comes to My Radar? Following the news of TSM, the largest holding in my portfolio, I saw several articles on Intel announces delay in its 7nm CPUs, and AMD CPUs outperform Intel ones benefiting from the use of TSM’s most advance process node
data analysis
in #4, I talked about how to find the hidden APIs of Centaline Android App. In this article, I am going to show you how to write a scraper to scrape the data. You can find the Scrapy Crawler in this Git directory. 3 Levels of Data The scraping was
data analysis
Date: 26 July 2020 In #3, I faced difficulty in having a lot of missing values in the property transaction data. After searching the web, I found that Centaline claimed they have spent 10 million HKD to fill the missing values. Maybe, I should try scraping data from Centaline. Luckily,
machine learning
Date: 13 July 2020 In part 2, we have talked about splitting data into a training set and testing set. In part 3, I would like to share some findings on the first data exploration. Big Problem: Missing Data I did not expect there are so many missing data in
google sheets
Sample Google Sheet: https://docs.google.com/spreadsheets/d/1pu1VmBKFvtdJPb78eYNooHaVAucff8_9blQPUUWoW2k/edit?usp=sharing Very often, data on websites is in presentation format instead of a nicely formatted table. This makes analysis difficult. Therefore, the first step of doing any real-world data analysis is to gather data in a nice
machine learning
In part 1, we have talked about scraping transactional data of apartments. In part 2, let’s talk about splitting data into a training set and testing set. Why we need to split? The short answer is we want to prevent overfitting/memorizing and hope that the model trained can
maths
Date : 7 July 2020 Sometimes, it is quite scary to read Maths. Just like learning a new language, we need to understand the symbols/ vocabulary first. In this post, I am going to talk about some common maths symbols. The following content is extracted from my YouTube video. Logic Symbols
machine learning
Date: 7 July 2020 Housing is still one of the greatest concerns of Hong Kong people. I think it will be fun to build a model to predict the house price in Hong Kong. Unlike, Kaggle competition, data is not readily available. So, the first step, which I am going
stocks
Date written: 21 May 2020 I have sold all my holdings on CCL and RCL. The primary reason for selling is that I cannot see how the future of cruise ships will look like. And, what’s ironic is that I thought I can see the future of cruise ships
credit scorecard
…too many financial institutions and investors simply outsourced their risk management. Rather than undertake their own analysis, they relied on the rating agencies to do the essential work of risk analysis for them. Lloyd Blankfein, CEO Goldman Sachs Credit risk scoring is simply a statistical tool for evaluating the level
financial analysis
營收成長性及穩定性顯示企業競爭力 這裏很好理解，就是從３，５年來看，營收基本每年都有增長。 毛利率穩定性反映企業對價格或生產效能的掌控力 毛利率計算公式＝（銷售收入－銷售成本）／銷售收入x 100% 毛利率穩定有兩種情況，一是公司可以掌握價格，二是公司只承接一定毛利的單。爲什麼能掌握價格，有利可圖自然有競爭，毛利會變低？原因可以是：具有產量、通路或專利優勢；具有品牌優勢；具有技術優勢；行業間協調定價；生產效能高，等等。 推銷費用的合理性反映產品市場力 推銷費用的都少要與同業比較，如果推銷費用佔營收比較低，通常暗示產品較好，銷售不費力。 尤其如果「推銷費用的增長率」高於「營業收入的增長率」時就暗示產品賣不動了。 管理費用的合理性反映管理力 跟同業比較就可以知道管理費用是否合理。還有就是和前幾年比較一下。 研究費用金額反映投資未來力度 長期性來講，研發費用與營收要有適當的配比，並且穩定按照這個比例行事，才是好的方式。當然，研發金額應不低於規模相當之同業，也要留意研發
financial analysis
最近我在看一本書叫「大會記師教你從財報數字看懂經營本質」，覺得內容不錯，就和大家分享一下。我想我會分幾部份寫，第一部分是評估企業真實價值 －從宏觀角度看懂「資產負債表」。第二部分是從微觀角度出發。 從「現金及約當現金」看穩健性 現金的安全水位宜保持兩個月用量。 從「應收票據」、「賬款」及「存貨」看管理能力 評估應收票據及賬款的目的，在於瞭解貨款是否積壓太多，有沒有呆帳的風險。計算公式很簡單：應收賬款及票據週轉天數＝期末應收賬款及票據／全年銷售金額 × (356/90/180)。天數是否正常可以比較同業，但多於三個月就很大機會不正常。 還有一個叫存貨週轉天數，期末存貨金額／全年銷售成本 × (356/90/180)。這個數太多不好，太少也不好，正常來說１至２個月是合理，高於２個月顯示經營力度有問題；低於一個月除非是特殊行業，否則有做假交易的嫌疑。 從「投資」看聚焦力 這個就不能單單看報表了，