顏擇雅推薦書單

 顏擇雅：暑假到了，不知大家都推薦什麼書給高中生? 以下是我順手想到的十本：

1.《張忠謀自傳》上冊。推薦原因是下冊快出來了，還沒看上冊的應該快點看。而且這本把成長與學習都寫超好的。我們可以從中學習一位偉大策略思考者應該具備什麼樣的人文養成。

學習動力與方向 (1/2)

關於學習和未來的方向，和一些同學們聊天，發現了一些有趣的現象。不論成績好壞，許多人面臨的第一個問題是學習動機和動力，例如休學比率、課堂中對「必考」兩個字無動於衷。

預備研究生 (4 + 1) (包含資料科學相關研究所和職涯準備)

預備研究生規定 (資訊處，新增加同學的問題)

• 培養
• 商業分析實驗室 (Business Analytics Laboratory)：論文。
• 人少，老師 (每週) 單獨討論。可於辦公室時間詢問庭煜。
• 學生的光，老師看得見。

Catastrophe Insurance Pricing

C. Zeng and D. Bertsimas, Catastrophe Insurance Pricing: A Robust Optimization Approach, In preparation for Management Science, 2023. 

Robust and Adaptive Optimization

Dimitris Bertsimas and Dick Den Hertog, Robust and Adaptive Optimization, Dynamic Ideas, 2022.

The purpose of this book is to provide a unified, insightful, and original treatment of robust and adaptive optimization.

Analysis and prediction of future stock price

Tasha Keeney, ARK’s Expected Value For Tesla In 2026: $4,600 Per Share, ARK, April 14, 2022. (GitHub)

Nicholas Grous and Andrew Kim, ARK’s Expected Value For Roku in 2026: $605 Per Share, ARK, July 07, 2022. (GitHub)

This is one way to predict the stock price, not for purchase recommendation. 

Machine learning and statistical testing at vantagescore

Kimberly Adams, How one credit scoring company is thinking about financial inclusion, Marketplace Tech,  JULY 8, 2022. (more around July 2022)

OR-Gym

Christian D. Hubbs, Hector D. Perez, Owais Sarwar, Nikolaos V. Sahinidis, Ignacio E. Grossmann, John M. Wassick, OR-Gym: A Reinforcement Learning Library for Operations Research Problems, arXiv:2008.06319v2. (Python)

Reinforcement learning (RL) has been widely applied to game-playing and surpassed the best human-level performance in many domains, yet there are few use-cases in industrial or commercial settings. We introduce OR-Gym, an open-source library for developing reinforcement learning algorithms to address operations research problems. In this paper, we apply reinforcement learning to the knapsack, multi-dimensional bin packing, multi-echelon supply chain, and multi-period asset allocation model problems, as well as benchmark the RL solutions against MILP and heuristic models. These problems are used in logistics, finance, engineering, and are common in many business operation settings. We develop environments based on prototypical models in the literature and implement various optimization and heuristic models in order to benchmark the RL results. By re-framing a series of classic optimization problems as RL tasks, we seek to provide a new tool for the operations research community, while also opening those in the RL community to many of the problems and challenges in the OR field.

Principles for Dealing with the Changing World Order by Ray Dalio

 Principles for Dealing with the Changing World Order by Ray Dalio

7 real-world applications of reinforcement learning

 Joy Zhang, 7 real-world applications of reinforcement learning, gocoder, February 17, 2022

1. Autonomous driving with Wayve

2. Personalizing your Netflix recommendations

3. Optimizing inventory levels for Walmart

4. Improving search engine results with search.io

5. Improving language models with OpenAI's WebGPT

6. Trading on the financial markets with IBM's DSX platform

7. Robotics with the University of California, Berkeley

台積電的訂價策略

陳良榕，台積電怎麼贏？， 天下雜誌 609 期，2016-10-25

洞悉市場的人

林錦慧譯，洞悉市場的人：量化交易之父吉姆‧西蒙斯與文藝復興公司的故事，天下文化，2020

Gregory Zuckerman, The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution, Penguin Group, 2019

在投資界，沒有哪個傳奇大師比吉姆‧西蒙斯更加神祕，他的前半生是頂尖數學家，不但曾幫助美國政府破解蘇聯密碼，還成功打造世界級的數學系。

但四十歲後，他轉戰投資界，開啟量化投資的風潮。他旗下的大獎章基金1988年至2018年的平均年化報酬率高達66.1％，就算扣除基金收取的各項費用，平均年化報酬率也高達39.1％ (*)，遠勝過巴菲特、彼得‧林區、雷‧達利歐、喬治‧索羅斯等股票大師。

他聘請頂尖數學家、物理學家與電腦工程師，藉由蒐集市場各項數據發展出一套演算法，找到買進與賣出的交易訊號，讓機器自動交易，他在華爾街掀起量化交易革命。不但成功打敗市場，還躲過歷年來的重大股災，持續擁有優異績效。

西蒙斯和他的同事擁有的財富已經富可敵國，現在開始轉往科學研究、教育與政治界發展。其中公司的前執行長羅伯特‧莫瑟還成為川普的大金主，莫瑟的女兒更是劍橋分析公司的主要投資人，在美國政壇發揮影響力。

本書作者古格里‧佐克曼採訪西蒙斯多位現任與離職員工後，寫下這個金融史上的傳奇故事，幫助我們一窺這位頂尖數學家如何掀起量化交易革命，顛覆華爾街的傳統模式。

liquefied natural gas (LNG) portfolio optimization

Alessandro Agosta, Dumitru Dediu, Timo Leenman, and Marijn van Diessen, LNG portfolio optimization: Putting the business model to the test, McKinsey, April 12, 2021.

The analysis indicates that in third quarter 2020, traditional marketers saw a 47 percent decline in realized prices compared with third quarter 2019, whereas the portfolio optimizers saw a 31 percent decline in realized prices over the same period (Exhibit 3). Although there are some obvious short­comings in this comparison—for example, the declines could be due to different geographic presences or a time lag on indexation—the findings do suggest that in the gas industry, portfolio opti­mizers were less affected by the recent downturn.

(高中) 數學與資訊工程

馬來西亞的學校提案，準備今年 5 月，開授相關線上演講，以便吸引高中生就讀資訊相關科系。去年12月中開校內協調會的時候，學校長官指派我，負責「數學與資訊工程 」。 月底到了，忙著提科技部計畫和撰寫研究的程式，但是，各種點子不斷地進入我的腦袋裡，只好趕緊把它寫下來，不然半夜進入我的夢鄉，擾人清夢。花了兩天，寫下初稿；最近又多次修正，前後花了不下 20 小時，決定提前定稿 ，以便改作其他教學和研究事務。

後來想一想， 既然這是一個有意義的工作，就準備把他錄成影片，並上傳 YouTube，以幫助有需要的年輕人。追求新知並傳授給學生，一直是我當老師快樂的泉源。

歡迎指正和提供寶貴意見。(pdf in 1) (pdf in 4，如果需要列印， 請雙面列印此版本，環保救地球)

初稿 2021/1/15。

Charting a business course for reinforcement learning

Jacomo Corbo, Oliver Fleming, and Nicolas Hohn, It’s time for businesses to chart a course for reinforcement learning, McKinsey, April 1, 2021.

Broadly speaking, we see reinforcement learning delivering this value across the business, with potential applications in every business domain and industry (Exhibit 2). Some of the near-term applications for reinforcement learning fall into three categories: speeding design and product development, optimizing complex operations, and guiding customer interactions.

 Exhibit 2 some applications.

To be sure, implementing reinforcement learning is a challenging technical pursuit. A successful reinforcement learning system today requires, in simple terms, three ingredients:

1. A well-designed learning algorithm with a reward function. 
2. A learning environment.  
3. Compute power. 

Computing power is better than compute power. 

耶魯最受歡迎的金融通識課

 陳志武，耶魯最受歡迎的金融通識課，今周刊，2019

全書文字通俗易懂，沒有公式和金融模型，卻能從歷史角度和量化分析視角來幫助讀者建立金融思維，以經濟眼光看待這個世界的運轉。是一本幫助普通大眾認清財富本源，學會用金融思維看懂世界的金融通識書。

25 頁

金融的核心任務，是要解決人與人之間的跨期價值交換的問題。…… 這些跨期價值交換涉及人與人之間的跨期承諾 (intertemporal commitment)，而跨期承諾是人類社會最難解決的挑戰。

在金融市場出現之前，人類多次的文化和社會組織創新，目的都是為了解決跨期承諾的挑戰，進而提升人與人之間跨期交換的安全度。

在書裡你會學到，傳統習俗、迷信宗教愛情婚姻家庭禮尚往來，以及儒家、基督教等文化的背後，其實含有豐富的金融邏輯。也就是說，許多文化的內涵實際上是因為金融市場的缺點而產生的，是為了解決本來應該由金融出面的問題而來的。

Decisive actions to emerge stronger in the next normal

Kevin Sneader, Shubham Singhal, and Bob Sternfels, What now? Decisive actions to emerge stronger in the next normal, McKinsey & Company, September 2020 (pdf)

1. Think of the return as a muscle
2. Focus on high-impact actions
3. Rebuild for speed
4. Reimagine the workforce from the top down
5. Make bold portfolio moves
6. Reset technology plans
7. Rethink the global footprint
8. Take the lead on climate and sustainability
9. Think about the role of regulation and government
10. Make purpose part of everything

英國製造：國家如何維繫經濟命脈

 蔡明燁譯，英國製造：國家如何維繫經濟命脈，立緒，2017

當經濟不斷受挫之際，市場上很少出現正面的經濟觀。產業外移、房市泡沫、物價齊漲，薪資水平卻長期低迷甚至倒退，除了籠統歸因大環境不景氣之外，人們也開始質疑自己究竟能夠產製或銷售什麼有價值的東西，而未來的經濟又該走向何方？

他山之石，可以攻錯，在思考我們國家的產業發展時，或許可以看看英國如何度過金融海嘯，重新調整經濟體質，站穩腳步迎向國際新局的例子。而隨著篇章開展，讀者亦能逐漸將書中分析套用在台灣的經濟發展上，當在面對國內經濟轉型的各種挑戰時，不再如無頭蒼蠅般惶惶不安。

作者伊凡．戴維斯為英國經濟學者，也是長期深入觀察當地產業的財經記者，書中對英國經濟的分析採取正向樂觀的論點，但絕不盲目，而是就事論事，從嚴謹的數據與比較分析中得出持平而論的根據，並做出有力的提醒和檢討，目的是要說服讀者，一個正常開放國家的謀生實力，其實比我們所想像中要強得多。

貝佐斯寫給股東的信 (The Bezos Letters)

李芳齡，貝佐斯寫給股東的信：亞馬遜14條成長法則帶你事業、人生一起飛，大塊文化 ，2019
Steve Anderson and Karen Anderson, The Bezos Letters: 14 Principles to Grow Your Business Like Amazon, Morgan James Publishing, 2019

濃縮21封貝佐斯致股東信精華，構建四階段成長循環，總結14條事業成長法則，揭開貝佐斯透過哪些教訓、心態和步驟，塑造出亞馬遜今日的偉大成就。 

不論組織型態、規模大小、行業別等，任何企業主、領導人、執行長、經理人和員工個人，都可以應用這些法則，快速地讓自己的事業變得更有效率、更有生產力、更成功。

ESG now a third of MioTech’s A.I. business

MioTech, ESG 101

digfin group, ESG now a third of MioTech’s A.I. business,  September 9, 2019.

MioTech builds A.I.-based solutions to help buy sides get insight from data analytics, and to help sell-side research departments and private banks’ relationship managers tell data-driven stories. It bases its service on building a library of cross-references (a “knowledge graph”) around a multitude of data points on Asian companies. The idea is to use big-data correlations to spot patterns....